• Proceedings of the Korea Society of Design Studies Conference
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• 2004.05a
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• pp.168-169
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• 2004

• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.2
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• pp.91-100
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• 2009

"How does advertising affect sales?" is the fundamental issue of modern advertising research. There is an interesting issue for estimating carryover effects of advertising on sales, and the aggregated data biases exist in the duration of advertising effect. This research suggests an extended model of Koyck Model which is employed for micro-data (Koyck 1954) to estimate aggregated advertising data, and empirically shows the aggregated data bias. Our developed model with the aggregated level of actual advertising data is more appropriate than the basic Koyck model for micro-data. The result figures out that it is important to consider the disaggregated data level in the analysis of dynamic effects of adverting such as carryover effects.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2008.10a
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• pp.319-323
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• 2008

"How does advertising affect sales?" is the fundamental issue of modern advertising research. There is an interesting issue for estimating carry over effects of advertising on sales, and the aggregated data biases exist in the duration of advertising effect. This research suggests a modified model at micro-data using Koyck model (Koyck 1954) by estimated model the aggregate data, and empirically shows the aggregated data bias. Our modified model with the aggregated level of actual data is more appropriate than the base model for micro-data. The result shows that it is very important to consider the disaggregated data level in the analysis of dynamic effects of adverting such as lagged effects.

• Journal of The Korean Association of Information Education
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• v.23 no.6
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• pp.529-542
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• 2019

Adverting of the 4th Industrial Revolution, maker education is emerged as a new paradigm and related studies keep increasing, As ideal human resources model has been changed. For this study, 65 theses on maker education were reviewed. The study has mainly analyzed the key factors for applying maker education in school. Though this study, it hopes to propose how further maker education studies should be developed. From the theses, 1.4 percent of the maker education program is actually usable in regular school class. For utilizing this education, it requires prior training and needs computer facilities. Most programs were designed as fusion form in more than two subject and included cooperative activities and sharing time. Further studies should be conducted on the application of education programs of within regular classes of school, recommended tools based on the school levels and prior training for tools, established the Makerspace in schools, shared student's output, evaluated students, and teacher training.

• Fashion & Textile Research Journal
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• v.21 no.4
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• pp.432-451
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• 2019

Companies and brands that practice sustainability pay attention to New Media due to its ability to build a sustainable relationship between companies and consumers. The need for research on specific roles, characteristics, and social media effects on eco-friendly advertising has had rapid growth in marketing programs for sustainable activities especially shown through social media. Information about sustainable fashion has spread to consumers through social media, and multifarious efforts have been made to attract the attention of youth. Despite the dramatic increase in eco-friendly marketing through social media as a part of sustainability, there is a lack of research on the major influences of emotional factors such as ad color and expression in social media. In this context, it is meaningful to identify relationships between emotional responses, advertising value and consumer behavior of sustainable fashion brands in Instagram and implement a suitable advertising type (color vs expression) for consumers. We used 366 responses for the final analysis. Data were analyzed by factor analysis, structural equation modeling using SPSS 18.0 and AMOS 18.0. The results of this study suggest that emotional responses, advertising value have a significant effect on the flow. This study expands on a previously limited research field by verifying consumer responses to image advertising on Instagram, rather than general sustainable fashion marketing. The study results also provide meaningful implications for a relation formation between customers and fashion brands vis-${\grave{a}}$-vis sustainable social media marketing.

• Journal of Service Research and Studies
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• v.13 no.2
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• pp.24-40
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• 2023

As the aversion to advertising grows, native advertising has gained significant attention as a means to counteract this rejection. Native advertising is designed to seamlessly blend with other content on the page where they are displayed. They mimic the overall design and presentation of the platform, providing a natural exposure to consumers. Many companies utilize native advertising, but there is not much academic research on this topic. Therefore this study aims to examine the effectiveness of native advertising for social issues. The study looked at two types of native advertising: ads created by a company and ads sponsored by a company. Results suggested that consumers showed more positive attitudes towards a company's ads if they were sponsored by the company rather than created by the company. This study argued that this effect would be more pronounced for consumers who tended to buy ethically. To verify these hypotheses an online experiment was conducted revealing that on average consumers displayed the same attitude toward a company whether the native advertising was created or sponsored by a company. However, it was also discovered that consumers with a tendency for ethical consumerism formed more positive attitudes toward companies when the company sponsored native advertising as opposed to having created it. In practice, this study suggests how companies can improve the effectiveness of native advertising, such as implementing native advertising as a sponsor and being transparent about their advertising to appeal to consumers with high ethical consumption tendencies. This study expands the scope of research in areas related to native adverting and corporate social responsibilities.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.163-176
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• 2014

Social media is becoming the platform for users to communicate their activities, status, emotions, and experiences to other people. In recent years, microblogs, such as Twitter, have gained in popularity because of its ease of use, speed, and reach. Compared to a conventional web blog, a microblog lowers users' efforts and investment for content generation by recommending shorter posts. There has been a lot research into capturing the social phenomena and analyzing the chatter of microblogs. However, measuring television ratings has been given little attention so far. Currently, the most common method to measure TV ratings uses an electronic metering device installed in a small number of sampled households. Microblogs allow users to post short messages, share daily updates, and conveniently keep in touch. In a similar way, microblog users are interacting with each other while watching television or movies, or visiting a new place. In order to measure TV ratings, some features are significant during certain hours of the day, or days of the week, whereas these same features are meaningless during other time periods. Thus, the importance of features can change during the day, and a model capturing the time sensitive relevance is required to estimate TV ratings. Therefore, modeling time-related characteristics of features should be a key when measuring the TV ratings through microblogs. We show that capturing time-dependency of features in measuring TV ratings is vitally necessary for improving their accuracy. To explore the relationship between the content of microblogs and TV ratings, we collected Twitter data using the Get Search component of the Twitter REST API from January 2013 to October 2013. There are about 300 thousand posts in our data set for the experiment. After excluding data such as adverting or promoted tweets, we selected 149 thousand tweets for analysis. The number of tweets reaches its maximum level on the broadcasting day and increases rapidly around the broadcasting time. This result is stems from the characteristics of the public channel, which broadcasts the program at the predetermined time. From our analysis, we find that count-based features such as the number of tweets or retweets have a low correlation with TV ratings. This result implies that a simple tweet rate does not reflect the satisfaction or response to the TV programs. Content-based features extracted from the content of tweets have a relatively high correlation with TV ratings. Further, some emoticons or newly coined words that are not tagged in the morpheme extraction process have a strong relationship with TV ratings. We find that there is a time-dependency in the correlation of features between the before and after broadcasting time. Since the TV program is broadcast at the predetermined time regularly, users post tweets expressing their expectation for the program or disappointment over not being able to watch the program. The highly correlated features before the broadcast are different from the features after broadcasting. This result explains that the relevance of words with TV programs can change according to the time of the tweets. Among the 336 words that fulfill the minimum requirements for candidate features, 145 words have the highest correlation before the broadcasting time, whereas 68 words reach the highest correlation after broadcasting. Interestingly, some words that express the impossibility of watching the program show a high relevance, despite containing a negative meaning. Understanding the time-dependency of features can be helpful in improving the accuracy of TV ratings measurement. This research contributes a basis to estimate the response to or satisfaction with the broadcasted programs using the time dependency of words in Twitter chatter. More research is needed to refine the methodology for predicting or measuring TV ratings.