Mixed reality technology is being increasingly used in cultural heritage attractions to enhance visitors’ experiences. However, how the characteristics of mixed reality affect satisfaction and brand loyalty has not been explored in previous research. The purpose of this study is to identify factors affecting satisfaction with mixed reality experiences at cultural and artistic visitor attractions and their influence on brand loyalty, which is connected with management performance. We propose a theoretical model based on brand equity theory in the context of mixed reality experience. Survey data were gathered from 251 respondents visiting a cultural and artistic visitor attraction in Seoul, Korea, using a stratified sampling method. Partial least squares structural equation modeling (PLS-SEM) was employed for the data analysis. The results suggest that the characteristics of mixed reality (interactivity, vividness) not only influence the affective aspects (perceived immersion, perceived enjoyment) of visitors’ experiences, but also positively affect brand awareness, brand association, and brand loyalty.

The aim of this paper is to empirically examine how individual characteristics affect user acceptance of context-aware services such as GPS-based telematics systems and location-based commerce, which together have become highly promising fields for mobile or ubiquitous e-commerce. To examine these individual characteristics, we applied a technology acceptance model to construct an amended model that focuses on three individual differences: self-efficacy, personal innovativeness and perceived sensitivity on contextual pressure. As a result, we discovered that the perceived sensitivity on contextual pressure, which is newly adopted as a construct in this paper, affects behavioural intention in using context-aware services.

A novel activity recognition method is proposed based on acoustic information acquired from microphones in an unobtrusive and privacy-preserving manner. Behavior detection mechanisms may be useful in context-aware domains in everyday life, but they may be inaccurate, and privacy violation is a concern. For example, vision-based behavior detection using cameras is difficult to apply in a private space such as a home, and inaccuracies in identifying user behaviors reduce acceptance of the technology. In addition, activity recognition using wearable sensors is very uncomfortable and costly to apply for commercial purposes. In this study, an acoustic information-based behavior detection algorithm is proposed for use in private spaces. This system classifies human activities using acoustic information. It combines strategies of elimination and similarity and establishes new rules. The performance of the proposed algorithm was compared with that of commonly used classification algorithms such as case-based reasoning, k-nearest neighbors, support vector machine, and multiple regression.