Visitor-centered approaches have been widely discussed in the museum experience research field. One notable approach was suggested by Falk and Dierking, who defined museum visitor experience as having a physical, personal, and social context. Many studies have been conducted based on this approach, yet the interactions between personal and social contexts have not been fully researched. Since previous studies related to these interactions have focused on the face-to-face conversation of visitor groups, attempts to provide the social information contributed by visitors have not progressed. To fill this gap, we examined such interactions in collaboration with the Lee-Ungno Art Museum in South Korea. Specifically, we investigated the influence of individual visitors’ social contextual information about their art museum experience. This data, which we call “visitor-based social contextual information” (VSCI), is the social information individuals provide—feedback, reactions, or behavioral data—that can be applied to facilitate interactions in a social context. The study included three stages: In Stage 1, we conducted an online survey for a preliminary investigation of visitors’ requirements for VSCI. In Stage 2, we designed a mobile application prototype. Finally, in Stage 3, we used the prototype in an experiment to investigate the influence of VSCI on museum experience based on visitors’ behaviors and reactions. Our results indicate that VSCI positively impacts visitors’ museum experiences. Using VSCI enables visitors to compare their thoughts with others and gain insights about art appreciation, thus allowing them to experience the exhibition from new perspectives. The results of this novel examination of a VSCI application suggest that it may be used to guide strategies for enhancing the experience of museum visitors.

Industrial tourism has become an increasingly important means of promoting corporate identity and fostering public engagement, yet many factory tours suffer from fragmented layouts, congestion, and low visitor engagement. This study addresses these challenges by developing a hybrid framework that integrates expert-driven spatial zoning with bottom-up visitor analytics. Using an optical lens manufacturer in Taiwan as a case study, we applied a three-step process: (1) Delphi-based zoning of key subareas into functional zones, (2) empirical analysis of visitor movement, feedback, and shadowing data, and (3) computational spatial evaluation through Visibility Graph Analysis (VGA). The findings revealed thematic inconsistencies, overlooked exhibits, and bottlenecks that disrupted narrative flow and reduced engagement. Spatial reorganization—such as relocating interactive subareas to visually integrated zones—enhanced circulation, storytelling alignment, and experiential coherence. A complementary service blueprint linked spatial redesign to operational delivery, ensuring consistency between frontstage activities and backstage support. The data-driven spatial analytics validated the effectiveness of this study’s hybrid approach—combining expert-driven insights with grounded visitor behavior data—to optimize factory tours. Spatial efficiency contributes to reduced energy use and congestion, participatory experiences enhance education and inclusivity, and improved visitor satisfaction strengthens brand resilience and economic viability. The framework thus provides a replicable and sustainable model for industrial tourism development across diverse manufacturing sectors.

This paper aims at building a knowledge-based system of smart mirrors for the cosmetic industry. In order to better understand hair and cosmetic experts, we first conduct interviews for knowledge acquisition. Then, we obtain insights from each category of answers collected from expert interviews. To design this knowledge-based system, we define concepts, main tasks and subtasks in order to extract rules to design the system. This system can suggest hairstyles and make-up colors by considering users’ face shapes and their personal colors.

The authors would like to make the following corrections to the published paper