• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.127-141
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• 2017

Advances in Internet technologies and the proliferation of mobile devices enabled consumers to approach a wide range of goods and services, while causing an adverse effect that they have hard time reaching their congenial items even if they devote much time to searching for them. Accordingly, businesses are using the recommender systems to provide tools for consumers to find the desired items more easily. Association Rule Mining (ARM) technology is advantageous to recommender systems in that ARM provides intuitive form of a rule with interestingness measures (support, confidence, and lift) describing the relationship between items. Given an item, its relevant items can be distinguished with the help of the measures that show the strength of relationship between items. Based on the strength, the most pertinent items can be chosen among other items and exposed to a given item's web page. However, the diversity of the measures may confuse which items are more recommendable. Given two rules, for example, one rule's support and confidence may not be concurrently superior to the other rule's. Such discrepancy of the measures in distinguishing one rule's superiority from other rules may cause difficulty in selecting proper items for recommendation. In addition, in an online environment where a web page or mobile screen can provide a limited number of recommendations that attract consumer interest, the prudent selection of items to be included in the list of recommendations is very important. The exposure of items of little interest may lead consumers to ignore the recommendations. Then, such consumers will possibly not pay attention to other forms of marketing activities. Therefore, the measures should be aligned with the probability of consumer's acceptance of recommendations. For this reason, this study proposes a model-based approach to combine those measures into one unified measure that can consistently determine the ranking of recommended items. A regression model was designed to describe how well the measures (independent variables; i.e., support, confidence, and lift) explain consumer's acceptance of recommendations (dependent variables, hit rate of recommended items). The model is intuitive to understand and easy to use in that the equation consists of the commonly used measures for ARM and can be used in the estimation of hit rates. The experiment using transaction data from one of the Korea's largest online shopping malls was conducted to show that the proposed model can improve the hit rates of recommendations. From the top of the list to 13th place, recommended items in the higher rakings from the proposed model show the higher hit rates than those from the competitive model's. The result shows that the proposed model's performance is superior to the competitive model's in online recommendation environment. In a web page, consumers are provided around ten recommendations with which the proposed model outperforms. Moreover, a mobile device cannot expose many items simultaneously due to its limited screen size. Therefore, the result shows that the newly devised recommendation technique is suitable for the mobile recommender systems. While this study has been conducted to cover the cross-selling in online shopping malls that handle merchandise, the proposed method can be expected to be applied in various situations under which association rules apply. For example, this model can be applied to medical diagnostic systems that predict candidate diseases from a patient's symptoms. To increase the efficiency of the model, additional variables will need to be considered for the elaboration of the model in future studies. For example, price can be a good candidate for an explanatory variable because it has a major impact on consumer purchase decisions. If the prices of recommended items are much higher than the items in which a consumer is interested, the consumer may hesitate to accept the recommendations.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.113-129
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• 2015

Location branding is a very important income making activity, by giving special meanings to a specific location while producing identity and communal value which are based around the understanding of a place's location branding concept methodology. Many other areas, such as marketing, architecture, and city construction, exert an influence creating an impressive brand image. A place brand which shows great recognition to both native people of S. Korea and foreigners creates significant economic effects. There has been research on creating a strategically and detailed place brand image, and the representative research has been carried out by Anholt who surveyed two million people from 50 different countries. However, the investigation, including survey research, required a great deal of effort from the workforce and required significant expense. As a result, there is a need to make more affordable, objective and effective research methods. The purpose of this paper is to find a way to measure the intensity of the image of the brand objective and at a low cost through text mining purposes. The proposed method extracts the keyword and the factors constructing the location brand image from the related web documents. In this way, we can measure the brand image intensity of the specific location. The performance of the proposed methodology was verified through comparison with Anholt's 50 city image consistency index ranking around the world. Four methods are applied to the test. First, RNADOM method artificially ranks the cities included in the experiment. HUMAN method firstly makes a questionnaire and selects 9 volunteers who are well acquainted with brand management and at the same time cities to evaluate. Then they are requested to rank the cities and compared with the Anholt's evaluation results. TM method applies the proposed method to evaluate the cities with all evaluation criteria. TM-LEARN, which is the extended method of TM, selects significant evaluation items from the items in every criterion. Then the method evaluates the cities with all selected evaluation criteria. RMSE is used to as a metric to compare the evaluation results. Experimental results suggested by this paper's methodology are as follows: Firstly, compared to the evaluation method that targets ordinary people, this method appeared to be more accurate. Secondly, compared to the traditional survey method, the time and the cost are much less because in this research we used automated means. Thirdly, this proposed methodology is very timely because it can be evaluated from time to time. Fourthly, compared to Anholt's method which evaluated only for an already specified city, this proposed methodology is applicable to any location. Finally, this proposed methodology has a relatively high objectivity because our research was conducted based on open source data. As a result, our city image evaluation text mining approach has found validity in terms of accuracy, cost-effectiveness, timeliness, scalability, and reliability. The proposed method provides managers with clear guidelines regarding brand management in public and private sectors. As public sectors such as local officers, the proposed method could be used to formulate strategies and enhance the image of their places in an efficient manner. Rather than conducting heavy questionnaires, the local officers could monitor the current place image very shortly a priori, than may make decisions to go over the formal place image test only if the evaluation results from the proposed method are not ordinary no matter what the results indicate opportunity or threat to the place. Moreover, with co-using the morphological analysis, extracting meaningful facets of place brand from text, sentiment analysis and more with the proposed method, marketing strategy planners or civil engineering professionals may obtain deeper and more abundant insights for better place rand images. In the future, a prototype system will be implemented to show the feasibility of the idea proposed in this paper.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.135-155
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• 2018

The purpose of this study is to analyze the difference of interpretation level and intention to use message framing when autonomous vehicle, which is emerging as the product of 4th industrial revolution, is used as taxi, Interpretation level refers to the interpretation of a product or service, assuming that it will happen in the near future or in the distant future. Message framing refers to the formation of positive or negative expressions or messages at the extremes of benefits and losses. In other words, previous studies interpret the value of a product or service differently according to these two concepts. The purpose of this study is to investigate whether there are differences in intention to use when two concepts are applied when an autonomous vehicle is launched as a taxi. The results are summarized as follows: First, the message format explaining the gain and why should be used when using the autonomous taxi in the message framing configuration, and the loss and how when the autonomous taxi is not used. Messages were constructed and compared. The two message framing differed (t = 3.063), and the message type describing the benefits and reasons showed a higher intention to use. In addition, the results according to interpretation level are summarized as follows. There was a difference in intentions to use when assuming that it would occur in the near future and in the near future with respect to the gain and loss, Respectively. In summary, in order to increase the intention of using autonomous taxis, it is concluded that messages should be given to people assuming positive messages (Gain) and what can happen in the distant future. In addition, this study will be able to utilize the research method in studying intention to use new technology. However, this study has the following limitations. First, it assumes message framing and time without user experience of autonomous taxi. This will be different from the actual experience of using an autonomous taxi in the future. Second, self-driving cars should technical progress is continuing, but laws and institutions must be established in order to commercialize it and build the infrastructure to operate the autonomous car. Considering this fact, the results of this study can not reflect a more realistic aspect. However, there is a practical limit to search for users with sufficient experience in new technologies such as autonomous vehicles. In fact, although the autonomous car to take advantage of the public transportation by taxi is now ready for the road infrastructure, and technical and legal public may not be willing to choose to not have enough knowledge to use the Autonomous cab. Therefore, the main purpose of this study is that by assuming that autonomous cars will be commercialized by taxi you can do to take advantage of the autonomous car, it is necessary to frame the message, why can most effectively be used to find how to deliver. In addition, the research methodology should be improved and future research should be done as follows. First, most students responded in this study. It is also true that it is difficult to generalize the hypotheses to be tested in this study. Therefore, in future studies, it would be reasonable to investigate the population of various distribution considering the age, area, occupation, education level, etc. Where autonomous taxi can be used rather than those who can drive. Second, it is desirable to construct various message framing of the questionnaire, but it is necessary to learn various message framing in advance and to prevent errors in response to the next message framing. Therefore, it is desirable to measure the message framing with a certain amount of time when the questionnaire is designed.

• Journal of Korean Home Economics Education Association
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• v.31 no.4
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• pp.149-161
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• 2019

At present, we are at the point of change of the 4th industrial revolution era due to the development of artificial intelligence(AI) and rapid technological innovation that no one can predict until now. This study started from the question of 'What role should home economics education play in the era of the Fourth Industrial Revolution?'. The Fourth Industrial Revolution is characterized by AI, cloud computing, Internet of Things(IoT), big data, and Online to Offline(O2O). It will drastically change the social system, science and technology and the structure of the profession. Since the dehumanization of robots and artificial intelligence may occur, the 4th Industrial Revolution Education should be sought to foster future human resources with humanity and citizenship for the future community. In addition, the implication of education in the fourth industrial revolution, which will bring about a change to a super-intelligent and hyper-connected society, is that the role of education should be emphasized so that humans internalize their values as human beings. Character education should be established as a generalized and internalized consciousness with a concept established in the integration of the curriculum, and concrete practical strategies should be prepared. In conclusion, home economics education in the 4th industrial revolution era should play a leading role in the central role of character education, and intrinsic improvement of various human lives. The fourth industrial revolution will change not only what we do, or human mental and physical activities, but also who we are, or human identity. In the information society and digital society, it is important how quickly and accurately it is possible to acquire scattered knowledge. In the information society, it is required to learn how to use knowledge for human beings in rapid change. As such, the fourth industrial revolution seeks to lead the family, organization, and community positively by influencing the systems that shape our lives. Home economics education should take the lead in this role.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.19-43
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• 2016

This study examines the causes of the Asian exchange rate crisis and compares it to the European Monetary System crisis. In 1997, emerging countries in Asia experienced financial crises. Previously in 1992, currencies in the European Monetary System had undergone the same experience. This was followed by Mexico in 1994. The objective of this paper lies in the generation of useful insights from these crises. This research presents a comparison of South Korea, United Kingdom and Mexico, and then compares three different models for prediction. Previous studies of economic crisis focused largely on the manual construction of causal models using linear techniques. However, the weakness of such models stems from the prevalence of nonlinear factors in reality. This paper uses a structural equation model to analyze the causes, followed by a neural network model to circumvent the linear model's weaknesses. The models are examined in the context of predicting exchange rates In this paper, data were quarterly ones, and Consumer Price Index, Gross Domestic Product, Interest Rate, Stock Index, Current Account, Foreign Reserves were independent variables for the prediction. However, time periods of each country's data are different. Lisrel is an emerging method and as such requires a fresh approach to financial crisis prediction model design, along with the flexibility to accommodate unexpected change. This paper indicates the neural network model has the greater prediction performance in Korea, Mexico, and United Kingdom. However, in Korea, the multiple regression shows the better performance. In Mexico, the multiple regression is almost indifferent to the Lisrel. Although Lisrel doesn't show the significant performance, the refined model is expected to show the better result. The structural model in this paper should contain the psychological factor and other invisible areas in the future work. The reason of the low hit ratio is that the alternative model in this paper uses only the financial market data. Thus, we cannot consider the other important part. Korea's hit ratio is lower than that of United Kingdom. So, there must be the other construct that affects the financial market. So does Mexico. However, the United Kingdom's financial market is more influenced and explained by the financial factors than Korea and Mexico.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.69-85
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• 2016

Successful relationship with loving partners is one of the most important factors in life. In psychology, there have been some previous researches studying the factors influencing romantic relationships. However, most of these researches were performed based on statistical analysis; thus they have limitations in analyzing complex non-linear relationships or rules based reasoning. This research analyzes commitment and persistence in heterosexual involvement according to styles of loving using a datamining technique as well as statistical methods. In this research, we consider six different styles of loving - 'eros', 'ludus', 'stroge', 'pragma', 'mania' and 'agape' which influence romantic relationships between lovers, besides the factors suggested by the previous researches. These six types of love are defined by Lee (1977) as follows: 'eros' is romantic, passionate love; 'ludus' is a game-playing or uncommitted love; 'storge' is a slow developing, friendship-based love; 'pragma' is a pragmatic, practical, mutually beneficial relationship; 'mania' is an obsessive or possessive love and, lastly, 'agape' is a gentle, caring, giving type of love, brotherly love, not concerned with the self. In order to do this research, data from 105 heterosexual couples were collected. Using the data, a linear regression method was first performed to find out the important factors associated with a commitment to partners. The result shows that 'satisfaction', 'eros' and 'agape' are significant factors associated with the commitment level for both male and female. Interestingly, in male cases, 'agape' has a greater effect on commitment than 'eros'. On the other hand, in female cases, 'eros' is a more significant factor than 'agape' to commitment. In addition to that, 'investment' of the male is also crucial factor for male commitment. Next, decision tree analysis was performed to find out the characteristics of high commitment couples and low commitment couples. In order to build decision tree models in this experiment, 'decision tree' operator in the datamining tool, Rapid Miner was used. The experimental result shows that males having a high satisfaction level in relationship show a high commitment level. However, even though a male may not have a high satisfaction level, if he has made a lot of financial or mental investment in relationship, and his partner shows him a certain amount of 'agape', then he also shows a high commitment level to the female. In the case of female, a women having a high 'eros' and 'satisfaction' level shows a high commitment level. Otherwise, even though a female may not have a high satisfaction level, if her partner shows a certain amount of 'mania' then the female also shows a high commitment level. Finally, this research built a prediction model to establish whether the relationship will persist or break up using a decision tree. The result shows that the most important factor influencing to the break up is a 'narcissistic tendency' of the male. In addition to that, 'satisfaction', 'investment' and 'mania' of both male and female also affect a break up. Interestingly, while the 'mania' level of a male works positively to maintain the relationship, that of a female has a negative influence. The contribution of this research is adopting a new technique of analysis using a datamining method for psychology. In addition, the results of this research can provide useful advice to couples for building a harmonious relationship with each other. This research has several limitations. First, the experimental data was sampled based on oversampling technique to balance the size of each classes. Thus, it has a limitation of evaluating performances of the predictive models objectively. Second, the result data, whether the relationship persists of not, was collected relatively in short periods - 6 months after the initial data collection. Lastly, most of the respondents of the survey is in their 20's. In order to get more general results, we would like to extend this research to general populations.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.463-466
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• 2017

The purpose of this paper is to develop a module capable of all-directional driving different from conventional wheeled robots, and to solve the problems of the conventional mobile robot with side driving performance degradation, It is possible to overcome the disadvantages such as an increase in the time required for the unnecessary driving. The all - direction spherical wheel drive module for driving a ball - balancing robot is required to develop a power transfer mechanism and a driving algorithm for driving the robot in all directions using three rotor casters. 3DoF (Axis) A driver with built-in forward motion algorithm is embedded in the module and a driving motor module with 3DoF (axis) for driving direction and speed is installed. The movement mechanism depends on the sum of the rotation vectors of the respective driving wheels. It is possible to create various movement directions depending on the rotation and the vector sum of two or three drive wheels. It is possible to move in different directions according to the rotation vector field of each driving wheel. When a more innovative all-round spherical wheel drive module for forward movement is developed, it can be used in the driving part of the mobile robot to improve the performance of the robot more technically, and through the forward-direction robot platform with the drive module Conventional wheeled robots can overcome the disadvantage that the continuous straightening performance is lowered due to resistance to various environments. Therefore, it is necessary to use a full-direction driving function as well as a cleaning robot and a mobile robot applicable in the Americas and Europe It will be an essential technology for guide robots, boarding robots, mobile means, etc., and will contribute to the expansion of the intelligent service robot market and future automobile market.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.67-101
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• 2014

The use of new technology greatly shapes the marketing strategies used by companies to engage their consumers. Among these new technologies, social media is used to reach out to the organization's audience online. One of the most popular social media channels to date is the microblogging platform Twitter. With 500 million tweets sent on average daily, the microblogging platform is definitely a rich source of data for researchers, and a lucrative marketing medium for companies. Nonetheless, one of the challenges for companies in developing an effective Twitter campaign is the limited theoretical and empirical evidence on the proper organizational usage of Twitter despite its potential advantages for a firm's external communications. The current study aims to provide empirical evidence on how firms can utilize Twitter effectively in their marketing communications using the association between brand personality and brand engagement that several branding researchers propose. The study extends Aaker's previous empirical work on brand personality by applying the Brand Personality Scale to explore whether Twitter brand communities convey distinctive brand personalities online and its influence on the communities' level or intensity of consumer engagement and sentiment quality. Moreover, the moderating effect of the product involvement construct in consumer engagement is also measured. By collecting data for a period of eight weeks using the publicly available Twitter application programming interface (API) from 23 accounts of Twitter-verified business-to-consumer (B2C) brands, we analyze the validity of the paper's hypothesis by using computerized content analysis and opinion mining. The study is the first to compare Twitter marketing across organizations using the brand personality concept. It demonstrates a potential basis for Twitter strategies and discusses the benefits of these strategies, thus providing a framework of analysis for Twitter practice and strategic direction for companies developing their use of Twitter to communicate with their followers on this social media platform. This study has four specific research objectives. The first objective is to examine the applicability of brand personality dimensions used in marketing research to online brand communities on Twitter. The second is to establish a connection between the congruence of offline and online brand personalities in building a successful social media brand community. Third, we test the moderating effect of product involvement in the effect of brand personality on brand community engagement. Lastly, we investigate the sentiment quality of consumer messages to the firms that succeed in communicating their brands' personalities on Twitter.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.133-149
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• 2014

One of the main purposes of universities is to create new intellectual resources that will increase social values. These intellectual resources include academic research papers, lecture notes, patents, and creative ideas produced by both professors and students. However, intellectual resources in universities are often not distributed to the actual users or companies; and moreover, they are not even systematically being managed inside of the universities. Therefore, it is almost impossible for companies to access the knowledge created by university students and professors to utilize them. Thus, the current level of knowledge sharing between universities and industries are very low. This causes a great extravagant with high-quality intellectual and human resources, and it leads to quite an amount of social loss in the modern society. In the 21st century, the creative ideas are the key growth powers for many industries. Many of the globally leading companies such as Fedex, Dell, and Facebook have established their business models based on the innovative ideas created by university students in undergraduate courses. This indicates that the unconventional ideas from young generations can create new growth power for companies and immensely increase social values. Therefore, this paper suggests of a new platform for intellectual properties distribution with university-industry cooperation. The suggested platform distributes intellectual resources of universities to industries. This platform has following characteristics. First, it distributes not only the intellectual resources, but also the human resources associated with the knowledge. Second, it diversifies the types of compensation for utilizing the intellectual properties, which are beneficial for both the university students and companies. For example, it extends the conventional monetary rewards to non-monetary rewards such as influencing on the participating internship programs or job interviews. Third, it suggests of a new knowledge map based on the relationships between key words, so that the various types of intellectual properties can be searched efficiently. In order to design the system platform, we surveyed 120 potential users to obtain the system requirements. First, 50 university students and 30 professors in humanities and social sciences departments were surveyed. We sent queries on what types of intellectual resources they produce per year, how many intellectual resources they produce, if they are willing to distribute their intellectual properties to the industries, and what types of compensations they expect in returns. Secondly, 40 entrepreneurs were surveyed, who are potential consumers of the intellectual properties of universities. We sent queries on what types of intellectual resources they want, what types of compensations they are willing to provide in returns, and what are the main factors they considered to be important when searching for the intellectual properties. The implications of this survey are as follows. First, entrepreneurs are willing to utilize intellectual properties created by both professors and students. They are more interested in creative ideas in universities rather than the academic papers or educational class materials. Second, non-monetary rewards, such as participating internship program or job interview, can be the appropriate types of compensations to replace monetary rewards. The results of the survey showed that majority of the university students were willing to provide their intellectual properties without any monetary rewards to earn the industrial networks with companies. Also, the entrepreneurs were willing to provide non-monetary compensation and hoped to have networks with university students for recruiting. Thus, the non-monetary rewards are mutually beneficial for both sides. Thirdly, classifying intellectual resources of universities based on the academic areas are inappropriate for efficient searching. Also, the various types of intellectual resources cannot be categorized into one standard. This paper suggests of a new platform for the distribution of intellectual materials and human resources, with university-industry cooperation based on these survey results. The suggested platform contains the four major components such as knowledge schema, knowledge map, system interface, and GUI (Graphic User Interface), and it presents the overall system architecture.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.51-66
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• 2018

As AI (Artificial Intelligence) technologies have been swiftly evolved, a lot of products and services are under development in various fields for better users' experience. On this technology advance, negative effects of AI technologies also have been discussed actively while there exists positive expectation on them at the same time. For instance, many social issues such as trolley dilemma and system security issues are being debated, whereas autonomous vehicles based on artificial intelligence have had attention in terms of stability increase. Therefore, it needs to check and analyse major social issues on artificial intelligence for their development and societal acceptance. In this paper, multi-categorical sentiment analysis is conducted over online public opinion on artificial intelligence after identifying the trending topics related to artificial intelligence for two years from January 2016 to December 2017, which include the event, match between Lee Sedol and AlphaGo. Using the largest web portal in South Korea, online news, news headlines and news comments were crawled. Considering the importance of trending topics, online public opinion was analysed into seven multiple sentimental categories comprised of anger, dislike, fear, happiness, neutrality, sadness, and surprise by topics, not only two simple positive or negative sentiment. As a result, it was found that the top sentiment is "happiness" in most events and yet sentiments on each keyword are different. In addition, when the research period was divided into four periods, the first half of 2016, the second half of the year, the first half of 2017, and the second half of the year, it is confirmed that the sentiment of 'anger' decreases as goes by time. Based on the results of this analysis, it is possible to grasp various topics and trends currently discussed on artificial intelligence, and it can be used to prepare countermeasures. We hope that we can improve to measure public opinion more precisely in the future by integrating empathy level of news comments.