• Journal of Global Scholars of Marketing Science
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• v.10
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• pp.45-70
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• 2002

The purpose of this study is to obtain more insight into the explanation and prognosis of consumer behavior in real and virtual museums. The analysis focuses on the influence of the museum environment on the museum patrons (rather than on the influence of the art objects). On the basis of the emotional approach to environmental psychology by Mehrabian and Russell (1974), a behavior model has been developed for museums. The model, which is based on the emotional variables pleasure, arousal and dominance (PAD), is also enhanced by cognitive variabies (learning attractiveness, education standard and information demand). The enhancement of the classical model was necessary because cognitive variables play a major role in cultural institutions such as museums: One important objective of museums is the communication of cultural knowledge to visitors. The model is tested empirically using structural equation modeling. 301 visitors were interviewed individually. Two different museum environments were represented using visual stimuli. The theoretical model for museums can be proved empirically. The degree to which the model fits the empirical data was extensively tested. The model showed high compatibility with the data and could be accepted. The study proves that a model can be developed, which explains visitor behavior in museums. The model shows museum designers how museums should be designed to be both emotionally appealing and a learning environment. Based on empirical studies in virtual stores on the Internet, it is discussed whether the research findings in these environments may be applied to virtual museum environments. In order to create an emotionally appealing virtual museum, it is recommended that one uses a 3-dimensional representation to offer various possibilities for interaction and to create a multi-sensual environment that appears highly realistic.

• Asia-Pacific Journal of Business
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• v.15 no.1
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• pp.319-334
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• 2024

Purpose - The purpose of this study is to examine the gravity model commonly used for demand forecasting upon the implementation of new tourist facilities and analyze the main causation of forecasting errors to provide a suggestion on how to improve. Design/methodology/approach - This study first measured the errors in predicted values derived from past feasibility study reports by examining the cases of five national science museums. Next, to improve the predictive accuracy of the gravity model, the study identified the five most likely issues contributing to errors, applied modified values, and recalculated. The potential for improvement was then evaluated through a comparison of forecasting errors. Findings - First, among the five science museums with very similar characteristics, there was no clear indication of a decrease in the number of visitors to existing facilities due to the introduction of new facilities. Second, representing the attractiveness of tourist facilities using the facility size ratio can lead to significant prediction errors. Third, the impact of distance on demand can vary depending on the characteristics of the facility and the conditions of the area where the facility is located. Fourth, if the distance value is below 1, it is necessary to limit the range of that value to avoid having an excessively small value. Fifth, depending on the type of population data used, prediction results may vary, so it is necessary to use population data suitable for each latent market instead of simply using overall population data. Finally, if a clear trend is anticipated in a certain type of tourist behavior, incorporating this trend into the predicted values could help reduce prediction errors. Research implications or Originality - This study identified the key factors causing prediction errors by using national science museums as cases and proposed directions for improvement. Additionally, suggestions were made to apply the model more flexibly to enhance predictive accuracy. Since reducing prediction errors contributes to increased reliability of analytical results, the findings of this study are expected to contribute to policy decisions handled with more accurate information when running feasibility analyses.

• The Journal of Korea Robotics Society
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• v.18 no.4
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• pp.463-471
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• 2023

This paper proposes a method to estimate the level of interest shown by visitors towards a specific target, a guide robot, in spaces where a large number of visitors, such as exhibition halls and museums, can show interest in a specific subject. To accomplish this, we apply deep learning-based behavior recognition and object tracking techniques for multiple visitors, and based on this, we derive the behavior analysis and interest level of visitors. To implement this research, a personalized dataset tailored to the characteristics of exhibition hall and museum environments was created, and a deep learning model was constructed based on this. Four scenarios that visitors can exhibit were classified, and through this, prediction and experimental values were obtained, thus completing the validation for the interest estimation method proposed in this paper.