A ‘brand’ metaverse is a virtual space where customers experience the brand via digital avatars. With the advancement of augmented and virtual reality technologies, a brand metaverse is an important medium for communicating the brand with customers. In this study, we focus on the resemblance between a customer’s self and his/her avatar (i.e., self-avatar resemblance) in the brand metaverse and examine its influence on brand attitude. Prior studies examine self-avatar resemblance exclusively in non-brand-related virtual gaming platforms and test its effect on identity perception and immersion in the platforms. However, few studies probe the extent to which self-avatar resemblance influences customers' exploration in a brand metaverse and their attitude toward the brand. We fill this research gap by uncovering the positive effects of self-avatar resemblance on brand attitude and purchase intentions. Moreover, we proffer that the customers’ engagement toward the brand metaverse platform mediates the relationship between self-avatar resemblance and brand attitude. In addition, based on the interactive nature of metaverse, we hypothesize copresence―the number of avatars exploring the brand metaverse at the same time―to be a moderator, which strengthens the mediation. We conduct an experiment using a fashion brand’s virtual world positioned in a popular metaverse platform. In this experiment, participants create an avatar and freely roam around in the brand metaverse with their avatars. By reviewing the screen recording of each participant’s brand exploration in the metaverse, we measure self-avatar resemblance and other constructs. We also collect responses from questionnaires designed to measure attitudinal and behavioral variables. With the accumulated data, we test the hypotheses using partial least square structural equation model and find the results largely consistent with the hypotheses. With the findings, we provide important and interesting implications to marketing practitioners considering and doing ‘metaverse marketing.’

In this study, dual drainage system based runoff model was established for W-drainage area in G-si, and considering the various rainfall characteristics determined using Huff and Mononobe methods, the degree of flooding in the target area was analyzed and the risk was compared and analyzed through the risk matrix method. As a result, the Monobe method compared to the Huff method was analyzed to be suitable analysis for flooding of recent heavy rain, and the validity of the dynamic risk assessment considering the weight of the occurrence probability as the return period was verified through the risk matrix-based analysis. However, since the definition and estimating criteria of the flood risk matrix proposed in this study are based on the return period for extreme rainfall and the depth of flooding according to the results of applying the dual drainage model, there is a limitation in that it is difficult to consider the main factors which are direct impact on inland flooding such as city maintenance and life protection functions. In the future, if various factors affecting inland flood damage are reflected in addition to the amount of flood damage, the flood risk matrix concept proposed in this study can be used as basic information for preparation and prevention of inland flooding, as well as it is judged that it can be considered as a major evaluation item in the selection of the priority management area for sewage maintenance for countermeasures against inland flooding.

Objectives of this study were to identify the hotspot for displacement of the on-line water quality sensors, in order to detect illicit discharge of untreated wastewater. A total of twenty-six water quality parameters were measured in sewer networks of the industrial complex located in Daejeon city as a test-bed site of this study. For the water qualities measured on a daily basis by 2-hour interval, the self-organizing maps(SOMs), one of the artificial neural networks(ANNs), were applied to classify the catchments to the clusters in accordance with patterns of water qualities discharged, and to determine the hotspot for priority sensor allocation in the study. The results revealed that the catchments were classified into four clusters in terms of extent of water qualities, in which the grouping were validated by the Euclidean distance and Davies-Bouldin index. Of the on-line sensors, total organic carbon(TOC) sensor, selected to be suitable for organic pollutants monitoring, would be effective to be allocated in D and a part of E catchments. Pb sensor, of heavy metals, would be suitable to be displaced in A and a part of B catchments.