• Journal of Distribution Science
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• v.10 no.4
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• pp.29-36
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• 2012

Research on subjective well-being (SWB) has flourished in recent years. As SWB determines cognitive and motivational processes, including social comparison and cognitive dissonance, it determines how consumers make decisions, including the comparison and evaluation of alternatives. Considering that the comparison and evaluation of alternatives is related to social comparison and cognitive dissonance, the influence of SWB on the comparison and evaluation of alternatives needs to be investigated. This research aims to examine the effect of SWB on the comparison and evaluation of alternatives, especially when people acquire additional information about their chosen or non-chosen alternatives, leading to a change of absolute/relative value of alternatives. The reasonable price of an alternative as evaluated by individuals is used as a measure reflecting the perceived value of an alternative. Putting all of this together, the current study intended to investigate the influence of absolute and relative value on the reasonable price of an alternative depending on SWB. Participants were randomly assigned to one of two experiment groups (deterioration of non-chosen alternative vs. improvement of non-chosen alternative). After reading consumer report ratings of alternatives shown on monitor screens, participants chose one of the alternatives, followed by the change of the consumer report ratings (deterioration of non-chosen alternative vs. improvement of non-chosen alternative). Participants evaluated the reasonable price of their chosen alternative based on the provided price of the non-chosen alternative. Two weeks after the experiment, they were asked to answer survey questionnaire on SWB measures. A regression was performed on the reasonable price with experiment groups, mean-centered SWB, and their interaction. There was a significant simple effect of groups and SWB. More importantly, these effects were qualified by the predicted interaction of groups and SWB. To interpret this interaction further, simple slope tests were performed on the price when SWB was centered at one standard deviation above (i.e., happy people) and below (i.e., unhappy people) the mean. As predicted, happy people rated the reasonable price of the chosen alternative higher in the improvement of non-chosen alternative group than in the deterioration of non-chosen alternative group. Conversely, unhappy people showed no price difference between groups. These results show that happy people pay attention to the absolute value of the alternative, whereas unhappy people give more weight to the relative value as well as to the absolute value of a chosen alternative, indicating that unhappy people are more sensitive to the negative information of a non-chosen alternative compared to happy people. The present research expanded the existing research stream on SWB by showing the influence of SWB on the consumers' evaluation of alternatives. Furthermore, this study adds to previous research on SWB and social comparison by suggesting that unhappy people tend to be more sensitive to negative social comparison information of alternatives even when a target of social comparison is not explicitly present. Moreover, these results yield some managerial implications on how to provide product information based on SWB in order to make products more attractive among the alternatives available to consumers.

• 기호학연구
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• no.56
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• pp.45-70
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• 2018

Criticism of the postmodern philosophy of the system of representation, which has continued since the Renaissance, is based on a critique of the dichotomy that separates the subjects and objects and the environment from the human being. Interactivity, highlighted in a series of works emerging as postmodern trends in the 1960s, was transmitted to an interactive aspect of digital art in the late 1990s. The key feature of digital art is the possibility of infinite variations reflecting unpredictable changes based on public participation on the spot. In this process, the importance of computer programs is highlighted. Instead of using the existing program as it is, more and more artists are creating and programming their own algorithms or creating unique algorithms through collaborations with programmers. We live in an era of paradigm shift in which programming itself must be considered as a creative act. Simulation technology and VR technology draw attention as a technique to represent the meaning of reality. Simulation technology helps artists create experimental works. In fact, Baudrillard's concept of Simulation defines the other reality that has nothing to do with our reality, rather than a reality that is extremely representative of our reality. His book Simulacra and Simulation refers to the existence of a reality entirely different from the traditional concept of reality. His argument does not concern the problems of right and wrong. There is no metaphysical meaning. Applying the concept of simulation to algorithmic art, the artist models the complex attributes of reality in the digital system. And it aims to build and integrate internal laws that structure and activate the world (specific or individual), that is to say, simulate the world. If the images of the traditional order correspond to the reproduction of the real world, the synthesized images of algorithmic art and simulated space-time are the forms of art that facilitate the experience. The moment of seeing and listening to the work of Ian Cheng presented in this article is a moment of personal experience and the perception is made at that time. It is not a complete and closed process, but a continuous and changing process. It is this active and situational awareness that is required to the audience for the comprehension of post-representation's forms.

• Asia Marketing Journal
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• v.10 no.1
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• pp.25-64
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• 2008

Third-Party awards are growing in popularity. They are the hit product of the year chosen by The Korea Economic Daily, the best 10 products of the year chosen by Sports paper, the best hit product chosen by consulting firm and the best venture company of the year chosen by Information and Communication Ministry. Then these questions may be followed. Why industry likes this type of advertisement? Does this type of advertisement influences consumers' purchase intention? And if it does, how? Many researchers have been interested in external cue of product quality by focusing research effort on brand, price, producer, warranty etc. However, important but under-explored area is the role of third-party reference for signaling product quality. This paper comes from the idea that the third-party reference may signal consumers like manufacturer brand, product brand, product price, and shop brand. We develop a related theories to address research questions and drive some research hypotheses based on the previous studies probing source credibility, attribution, and signal theory. We put more emphasis on source credibility. We conducted the research based on 3x2x2x2 between group factorial design to explore causal relationship between the third party award winning advertising(real, fictional, no) and the purchase intention of consumers exposed to other information simultaneously such as product type(experience, search), distribution channel(direct, indirect) and perceived price(high, low). Since subjects are divided into 2 groups based on the means of response without extra experimental stimulus in case of perceived price. 12 different advertisements are used for conducting this study. The results are followings. First, the source credibility of the third party goes up, consumers' purchase intention would go up. It seems that consumers think the credibility of the third-party most when they are exposed to the third party award winning advertisement. Second, the product type does moderate the relationship between the third-party award winning advertisement and purchase intention. And the type of the distribution channel also moderates this relationship. The consumers' purchase intention goes up higher when they buy experience good and there is significant difference of purchase intention when consumers are exposed to direct channel treatment condition. But, perceived price has nothing to do with the third-party winning advertisement context for raising consumer intention to buy advertised product.

• Journal of the Korean Society of Earth Science Education
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• v.16 no.3
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• pp.340-350
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• 2023

This study analyzed the difficulty level of class on the seasonal change unit for 84 students at a university of education. The conclusions of this study are as follows. First, if we first present the four topics that make up the seasonal changes in elementary science, the subjects that have the greatest difficulty in teaching for prospective elementary school teachers are 'Why do seasonal changes occur?' (Teaching difficulty level 4.05), 'The sun changes depending on the season' What is the difference between the southern altitude and the length of day and night?' (difficulty level of class, 3.12), 'What is the relationship between the altitude of the sun, length of shadow, and temperature during the day?' (difficulty level of class, 2.85), 'How does the temperature change depending on the season?' (class difficulty level 2.80). As a result, in the elementary science season change unit, the class on the four topics 'Why do seasons change?', which is classified as a class topic that requires the concept of spatial perception, showed a higher level of class difficulty than other units. Second, in the seasonal change unit, various factors of class difficulty appeared depending on the class topic. When pre-service elementary school teachers look at the factors that make class difficult when teaching a lesson on seasonal changes in order of frequency, 42 (50%) said 'Experimental instruction for comparing the altitude of solar masculine according to the tilt of the axis of rotation', followed by 'Solar masculine'. 38 people (45%) answered 'Difficulty in explaining mid-high altitude and the length of day and night', 27 people (32%) answered 'Difficulty in explaining the concept of mid-high altitude', and 24 people (32%) answered 'Difficulty in explaining seasonal changes in the sun's position.' 29%), 20 people (24%) said 'Explain the reasonable reason why the height of the light should be adjusted when measuring the solar altitude', and 16 people (19%) said 'It is difficult to explain the reason for the discrepancy between the solar altitude and the maximum temperature'. ), 'difficulties in measuring sand (ground) temperature' were mentioned by 12 people (14%). Third, when analyzing the factors of class difficulty, there were more curriculum factors than teacher factors. In this context, the exploratory activities on 'Why do seasonal changes occur?', the fourth topic of the seasonal change unit in which elementary school pre-service teachers showed the greatest difficulty in teaching, need improvement in terms of the curriculum.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.70-82
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• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.