• Science of Emotion and Sensibility
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• v.26 no.3
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• pp.3-14
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• 2023

This study used intersubject representational similarity analysis (IS-RSA) to identify participant-response consistency patterns in previously published data. Additionally, analysis of variance (ANOVA) was utilized to detect any variations in the conditions of each experiment. In each experiment, a combination of ASMR stimulation, visual and auditory stimuli, and time-series emotional video stimulation was employed, and emotional ratings and physiological measurements were collected in accordance with the respective experimental conditions. Every pair of participants' measurements for each stimulus in each experiment was correlated using Pearson correlation coefficient as part of the IS-RSA. The results of study revealed a consistent response pattern among participants exposed to ASMR, visual, and auditory stimuli, in contrast to those exposed to time-series emotional video stimulation. Notably, the ASMR experiment demonstrated a high level of response consistency among participants in positive conditions. Furthermore, both auditory and visual experiments exhibited remarkable consistency in participants' responses, especially when subjected to high arousal levels and visual stimulation. The findings of this study confirm that IS-RSA serves as a valuable tool for summarizing and presenting multidimensional data information. Within the scope of this study, IS-RSA emerged as a reliable method for analyzing multidimensional data, effectively capturing and presenting comprehensive information pertaining to the participants.

• Information Systems Review
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• v.21 no.4
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• pp.175-195
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• 2019

This study conducted both big data and netnography analysis to analyze consumer needs and behaviors of online consumer community. Big data analysis is easy to identify correlations, but causality is difficult to identify. To overcome this limitation, we used netnography analysis together. The netnography methodology is excellent for context grasping. However, there is a limit in that it is time and costly to analyze a large amount of data accumulated for a long time. Therefore, in this study, we searched for patterns of overall data through big data analysis and discovered outliers that require netnography analysis, and then performed netnography analysis only before and after outliers. As a result of analysis, the cause of the phenomenon shown through big data analysis could be explained through netnography analysis. In addition, it was able to identify the internal structural changes of the community, which are not easily revealed by big data analysis. Therefore, this study was able to effectively explain much of online consumer behavior that was difficult to understand as well as contextual semantics from the unstructured data missed by big data. The big data-netnography integrated model proposed in this study can be used as a good tool to discover new consumer needs in the online environment.

• Information Systems Review
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• v.25 no.1
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• pp.75-91
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• 2023

Thanks to information technologies, sharing economy services offer a new way of consumption. Carsharing appeared as a novel type of service that transformed the conventional way of personal transportation, from owning a vehicle to using an on-demand service. Allowing users to use a vehicle without owning a car, carsharing provides various social benefits such as the reduction of resource allocation inefficiencies and the alleviation of transportation problems. To strengthen such positive aspects of carsharing service, it is essential to understand an individual's service usage pattern and reveal factors that affect users' reuse behavior. This study investigates the factors that have an influence on carsharing reuse of users applying RFMC (Recency, Frequency, Monetary, and Clumpiness) model, the popular model for understanding the reuse likelihood of customers. Using data from a leading carsharing service provider in South Korea, we empirically analyze the effect of RFMC on carsharing reuse behavior. The findings show that recency and monetary values are negatively related to reuse while frequency is positively related to carsharing service reuse. Moreover, the impact of recency and monetary value are more salient whereas the impact of frequency is smaller among users with higher clumpiness. Based on these findings, this study elaborates on theoretical and practical implications.

• Korean journal of applied entomology
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• v.48 no.4
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• pp.493-501
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• 2009

To investigate resistance of Myzus persicae, which is resistant to etofenprox belonging to pyrethriods, to other insecticides, fenpropathrin and thiamethoxam, mortality and $LC_{50}$ values for these two insecticides against the etofenprox-resistant and -susceptible populations were obtained and EPG analysis for feeding behaviors of thiamethoxam or fenpropathrin treated and non-treated M. persicae was conducted. For fenpropathrin, mortality after 48 h treatment was 28 and 29%, respectively for the resistant and susceptible population. The $LC_{50}$ value was 193.15 and 93.46 ppm, respectively. For thiamethoxam, mortality after 48 h treatment was 87 and 57%, respectively for the resistant and susceptible population. The $LC_{50}$ value was 3.17 and 30.34 ppm, respectively. There was no significant difference in feeding behavior between fenprorpathrin treated and non-treated M. persicae. They showed a continuous feeding pattern. However, contact signal frequency increased in the thiamethoxam treated M. persicae 1 hour after treatment and significant difference was shown.

• Journal of Gifted/Talented Education
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• v.27 no.1
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• pp.59-80
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• 2017

In this study, we investigate social preference of gifted students by analyzing their behaviors in social dilemma situations. We conducted an experimental study using ultimatum games and public goods games with 132 academically gifted middle school students who attended the Ewha-Seodaemun Center for gifted education from 2012 to 2016. We also experimented the same games with 87 regular students for comparative analysis. The result of ultimatum game experiment shows that there is no statistical difference in the proposed share of both groups. Their proposed share ranges from 37% to 38% as expected in other similar studies. However, the rejection rate of the respondents to the proposals with small share are significantly higher among gifted students than among their regular counterparts. This result implies that the gifted students show stronger negative reciprocity, meaning that they tend to punish selfish behaviors even when it takes some costs. In finitely repeated public goods game experiments, the results show that both groups' contribution rates decrease toward the end of the experiments. However, the gifted students show strategic cooperation by attempting to increase the other members' contribution rate within an experimental group. This implies that gifted students tend to care more about how to increase their own expected rewards by reciprocating other students' behaviors.

• Cartoon and Animation Studies
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• s.50
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• pp.239-273
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• 2018

There have been many topics in gamer research on gamers' game addiction, education, and psychological interest. This paper investigates how to perceive the virtual environment of gamers based on James Gibson 's theories of cognitive science. Gibson's theory is not a stimulus input through individual sensory receptors, but rather a learning process such as establishing a cognitive relationship between perceptual systems, external invariant property separation, behavioral learning, invariant property separation of events, selectiveism development. Based on this analysis tool, I collected and verified gamers' perception of game environment of by FGI survey method. The results of the analysis showed that Gibson 's perceptual learning process was perceived as a virtual environment as in reality, and there was also perceptual difference found only in games. Patterned perception develops in the direction of classifying invariant properties appearing in the game based on the purpose of the game. In this study, it can be seen as a result of the research that FGI interview can be summarized as patterning (typification) perception process based on the goal consciousness of gamers. But,The results of the study suggest that the psychological analysis of the gamer can not be presented by the FGI results alone. In the future, we need a model study to confirm the causality and the verification through statistical analysis.

• Journal of Food Hygiene and Safety
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• v.35 no.5
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• pp.495-502
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• 2020

Processing fresh produce into fresh-cut products increases the risk of bacterial growth and contamination by breaking the exterior barrier of produce. Our objective in this study was to develop predictive models of Listeria monocytogenes in the fresh-cut salad, fresh-cut pineapple, and frozen mango. Predictive growth and survival models were developed to predict the change of L. monocytogenes populations in the fresh-cut salad (4, 10, 12, 13, 17, 25, and 36℃), fresh-cut pineapple (4, 10, 17, 25, 30, and 36℃), and frozen mango (-2, -10 and -18℃) as a function of temperature. The growth of L. monocytogenes in fresh-cut salad and pineapple was observed at above 13℃ and 10℃, respectively. The growth of L. monocytogenes in pineapple was faster than in salad. The delta value of L. monocytogenes in frozen mango increased as the storage temperature decreased. The results indicate that L. monocytogenes behave differently according to the physicochemical properties of fresh-cut fruits and vegetables. Since L. monocytogenes grow and survive well in refrigerated and frozen conditions, management programs and preventive controls for the processing of fresh-cut produce should be effectively implemented to enhance the safety of fresh-cut fruits and vegetables at retail markets.

• Journal of Service Research and Studies
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• v.10 no.2
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• pp.1-15
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• 2020

As big data become more important socially and economically in recent years, many problems have been derived from the indiscriminate application of big data. Big data are valuable because it can figure out the meaning of informative information hidden within the data. In particular, to predict customer behavior patterns and experiences, structured data that were extracted from Customer Relationship Management (CRM) or unstructured data that were extracted from Social Network Service(SNS) can be defined as a population to interpret the data, during which many errors can occur. However, those errors are usually overlooked. In addition to data analysis techniques, some data, which should be considered in the analysis, are not included in the population and thus do not show any meaningful patterns. Therefore, this study presents the measurement and interpretation of the data generated when the cause of error in the population setting is strong relationship and interaction between people or a person and an object. In other words, it will be shown that if the relationship and interaction are strong, it is important to include data collected from the perspective of user experience and ethnography in the population by comparing various cases of big data application, through which the meaning will be derived and the best direction will be suggested.

• Journal of the Korean Regional Science Association
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• v.38 no.4
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• pp.59-74
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• 2022

We aim to explore how the COVID-19 affects commercial sales of traditional market in Seoul. We obtain data for commercial sales and several spatial variables that are related to commercial sales from the Seoul Open Data Plaza. In order to estimate the effect of COVID-19 occurrence on commercial sales, we employ fixed-effect panel data analysis models. Unlike our expectation, the empirical results show that the effect of the COVID-19 on commercial sales of traditional market is not significant. However, we found that the effects are significant in some types of businesses when we did the same analyses with subsamples. For example, service sectors are mostly negatively affected by COVID-19, and retail sectors are also second mostly affected by COVID-19. However, there is no significant relationship between COVID-19 and restaurant sectors. In addition, these effects vary by size of traditional market. Our results suggest that government should have a plan to help small businesses in traditional market because they do not have sufficient abilities to adjust to the unexpected economic shock, like COVID-19. Findings also suggest that strategies for helping them should be differentiated by business type and market size.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.2
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• pp.76-83
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• 2022

With the recent development of IT technology, research and interest in various biosignal measuring devices is increasing. As an aging society is in full swing, research on the elderly population using IT-related technologies is continuously developing. This study is about the development of life pattern detection and fall detection algorithm, which is one of the medical service areas for the elderly, who are rapidly developing as they enter a super-aged society. This study consisted of a system using a 3-axis accelerometer and an electrocardiogram sensor, collected data, and then analyzed the data. It was confirmed that behavioral patterns could be classified from the actual research results. In order to evaluate the usefulness of the human activity monitoring system implemented in this study, experiments were performed under various conditions, such as changes in posture and walking speed, and signal magnitude range and signal vector magnitude parameters reflecting the acceleration of gravity of the human body and the degree of human activity. was extracted. And the possibility of discrimination according to the condition of the subject was examined by these parameter values.