Satcharoen, Kleddao
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Item type:Publication, A Generational Cohort Comparison of Icon Selection Accuracy under Varying Conditions of Icon Entropy and Concreteness(2023-01-01); —The objective of this research was to compare icon selection accuracy under varying icon entropy and concreteness conditions between different generational cohorts (Millennial, Generation X, and Baby Boomers). These generational cohorts have different levels of experience with technology, with younger generations often being framed as “digital natives” and holding stronger technological experience and competence in comparison to older groups. Generational groups also have variations in physiological factors including visual acuity and reaction time. Despite these differences between user groups, many user interaction systems and processes are designed for a single user, rather than considering differences in user processing between different groups. Therefore, this study compares generational cohorts in their icon selection accuracy under varying icon conditions, to help identify what generational differences can be observed in this task. The study selected a sample of 150 participants (n = 50 for each generational cohort). The experiment was a 2☓2☓3 design (entropy (high/low) ☓ abstractness (abstract/concrete) ☓ time (9/6/3 seconds), with each participant completing 60 trials (five questions per entropy/abstractness pair over three timed runs). Results showed that there were significant differences in mean accuracy per trial under all of the time conditions and icon entropy and concreteness conditions. Mean differences showed that under most conditions, Millennial and Generation X participants did not have a significant mean difference, but Baby Boomers were significantly slower under almost all conditions. The implication of this finding is that Baby Boomers are more sensitive to icon abstractness and entropy conditions than other age groups tested. - Some of the metrics are blocked by yourconsent settings
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Item type:Publication, Enhancing Odor Classification of Essential Oils with Electronic Nose Data(2024-01-01); In the current business landscape, the fragrance industry has gained substantial prominence. In this context, there is a requirement to create the most compact and sufficiently accurate model possible, suitable for deployment on a portable device. The aim is to develop a model capable of effectively classifying various fragrance types based on data pertaining to air properties and fragrance component attributes. This paper presents the feature extraction from the dataset electronic node to classify odor types using a machine learning model compared before and after the feature extraction of the dataset. In our investigation, we employed datasets of varying sizes, including small datasets (composed of 1000 samples), large datasets (composed of 10000 samples), and raw datasets (composed of 21000 samples). This methodology was employed to discern disparities in model performance, average accuracy, and computational runtime across these different dataset sizes. We observed that the decision tree model, post-training with principal component analysis, showed a performance improvement when compared to the basic machine learning model. Specifically, the decision tree model achieved accuracy rates of 100.00%, 99.97%, and 97.00% respectively. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The Factors that Affect the Selecting of Sticker Image on Social Media Application: A Case Study of Retired People Purchasing Behavior in Thailand(2022-01-01); This research aimed to investigate factors that influence retirees in Thailand on selection of Line stickers image to purchase. The study uses an integrated model of attitude-behavior decision-making, incorporating the theory of planned behavior (TPB) and technology acceptance model (TAM) along with personal and demographic factors. The data were collected via online questionnaire survey, from 400 retirees who live in Thailand. The results showed that an integrated model that incorporated TPB and TAM was generally successful at predicting consumer attitudes, behavioral intentions, and behaviors toward the purchase of Line stickers image. However, there was a notable exception, which was that subjective norms did not have a significant effect on attitudes except for perceived ease of use.