• Journal of Korea Entertainment Industry Association
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• v.14 no.8
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• pp.25-33
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• 2020

This study was based on the research contents of previous studies. and After operatively defining public culture and arts institutions in terms of combining the characteristics of public institutions and culture and arts, the necessity of analyzing the priority of performance evaluation indicators was raised in accordance with this. The results of priority analysis based on the AHP axiom are as follows. The priorities for the performance evaluation index of public culture and arts institutions were in the order of artistry, expertise, marketing, publicity, organizational management, exchange and cooperation, management efficiency, operating system, and convenience. Based on the analysis results, the following implications were presented. First, it is necessary to first pay attention to the items of artistry that appear as the highest priority and seek strategies. Second, it is necessary to put a variety of experts in the original plan so that the audience and the public can have a high interest in this. However, in order not to exceed the 10 items recommended by the AHP axiom, we had to intentionally limit the evaluation items. Therefore, it is necessary to analyze priorities by deriving various evaluation items in subsequent studies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.2827-2848
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• 2021

Mobile Crowdsourcing (MCS) has become an emerging paradigm evolved from crowdsourcing by employing advanced features of mobile devices such as smartphones to perform more complicated, especially spatial tasks. One of the key procedures in MCS is to collect answers from mobile users (workers), which may face several security issues. First, authentication is required to ensure that answers are from authorized workers. In addition, MCS tasks are usually location-dependent, so the collected answers could disclose workers' location privacy, which may discourage workers to participate in the tasks. Finally, the overhead occurred by authentication and privacy protection should be minimized since mobile devices are resource-constrained. Considering all the above concerns, in this paper, we propose a lightweight and privacy-preserving answer collection scheme for MCS. In the proposed scheme, we achieve anonymous authentication based on traceable ring signature, which provides authentication, anonymity, as well as traceability by enabling malicious workers tracing. In order to balance user location privacy and data availability, we propose a new concept named current location privacy, which means the location of the worker cannot be disclosed to anyone until a specified time. Since the leakage of current location will seriously threaten workers' personal safety, causing such as absence or presence disclosure attacks, it is necessary to pay attention to the current location privacy of workers in MCS. We encrypt the collected answers based on timed-release encryption, ensuring the secure transmission and high availability of data, as well as preserving the current location privacy of workers. Finally, we analyze the security and performance of the proposed scheme. The experimental results show that the computation costs of a worker depend on the number of ring signature members, which indicates the flexibility for a worker to choose an appropriate size of the group under considerations of privacy and efficiency.

• Journal of Korea Technology Innovation Society
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• v.21 no.2
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• pp.587-614
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• 2018

The Korean government is aiming to strengthen industrial and national competitiveness through the promotion of cooperation between universities, public research institutes and industry and vitalization of technology transfer. In 2013 and 2014, the Ministry of Trade, Industry and Energy and Ministry of Science, ICT and Future Planning have announced policies to support SMEs by public research organizations. In addition, in 2015, the 'Korean Fraunhofer support system', which pay government subsidies according to the amounts of private R&D funds was adopted. However, there are some concern about the government's policies. There is yet disclosed how these policies affect technology transfer because industrial R&D funding has not been activated in Korea unlike German. Therefore this paper analyzes effects of relationship between universities, public research institutes and external organizations on performance of technology transfer based on the Triple Helix Model. Empirical results show that the relationship with the government has a significant impact on the resource security and the relationship with the industry has a significant effect on the diffusion of the performance. In addition, a public research institute was selected and case analysis was conducted to suggest policy implications for improving the technology transfer performance of universities and public research institutes.

• Management & Information Systems Review
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• v.17
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• pp.191-214
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• 2005

Recently, most enterprises are having a knowlege management boom. A number of books associated with the knowlege management are being published, countless public seminars are held, and many research councils have been organized studying it formally or informally as if not importing the system is like falling behind a fashion. However, there are not many cases that achieved success by constructing the system of knowledge management. Then, why the knowledge management is not so much effective despite so many voices wanting the change of management system and a lot of public lectures about it? I guess the reason is that most companies do not have concrete methodology. Seeing a result of a survey which reported that with spread of venture boom and successful examples being known widely, the outflow of precious human resources is accelerated and a large number of employees of conglomerates have already resigned or are considering separation from their positions, we can realize that are occurring a change which can be nearly called severance in an occupational view and an organization culture. The preference to a large enterprise or a public institution of labor is low today and the notion about a lifelong job is regarded as past remains. As for this, it could be said that the social atmosphere that pursued the stability of a job has been changed to the practical one that attaches importance to ability and pay. The way of thinking of employees has been changed while established organizations cannot satisfy their desire and this explains why important members of a company are flown out. The reason why superior human resources move to venture businesses is that they can do their likable work and also prove their ability as well as unconventional rewards. Although existing companies are trying to preserve important human resources through performance compensating stock option, temporary patching up of personnel management cannot retard the rushing wind of foundation and the outflow of labor. On the contrary, clumsy import of performance-based reward system not only fails to hire superior labor power but also can bring about a sense of incompatibility and conflicts among the remaining employees. Therefore, this thesis, focusing on how to choose, develop, and maintain the human resources, will suggest a future-aiming human resources management model of Korean enterprises after comparing and analyzing the actual condition of domestic companies and the trends of advanced corportaions.

• Journal of Distribution Science
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• v.10 no.10
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• pp.17-24
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• 2012

Purpose - This study aimed to (a) investigate the effects of emotional intelligence on customer orientation, (b) examine the impact of customer orientation on customer relationship management (CRM) performance (including CRM-related variables such as 'relationship commitment,' 'image of corporation,' and 'customer loyalty'), and (c) identify the conceptual framework of emotional intelligence. Research design, data, and methodology - The data were collected using a questionnaire given to a sample of employees of luxury hotels in the metropolitan area. To test the hypotheses, AMOS were conducted for the 271 respondents of the sample using the SPSS Win 17.0 software. The concept of emotional intelligence (EI) has been on the radar of many leaders and managers over the past few decades. Emotional intelligence is generally accepted to be a combination of emotional and interpersonal competencies that influence behavior, thoughts, and interactions with others. Emotional intelligence consists of four factors: understanding the self's emotion, understanding other people's emotions, emotion utilization, and emotion control. Understanding the self's emotion means to understand of my own emotions. Understanding other people's emotions is to understand of the emotions of the people around me and to know how my friends feel based on their behavior. The concept of emotion utilization means to set goals for myself and then try to achieve them, encouraging myself to do my best. The concept of emotion control means I can control my temper, handle difficult situations rationally, and calm down quickly when I am very angry. Results - As a result of the analysis, three factors (understanding the self's emotion, understanding of other people's emotions, and emotion utilization) were shown to have a significant effect on customer orientation. Emotion control had an insignificant effect on customer orientation. Only emotion control makes it difficult to solve customers' problems because it is a passive behavior. In order to solve the customers' problems, hotel employees have to show a positive attitude. Second, customer orientation had a significant effect on customer relationship management performance (customer relationship commitment, corporate image, and customer loyalty). In other words, customer orientation increases commitment to customer relationships. For example, employees who have a customer-orientated perspective provide good service to their customers, while employees who don't have a customer-orientated perspective can't satisfy their customers. Customer orientation can also generate a good image among customers, because they evaluate the image of a hotel through the behavior of hotel employees. So it is very important for employees to show excellent customer orientation. Conclusions - It is very important for hotel CEOs to manage their employees' emotional intelligence. In order to increase their employees' emotional intelligence abilities, CEOs have to manage the overall corporate culture and reward programs to achieve what they want. This is because the system can lead to a customer-orientated mind-set and CRM performance among employees. As a result, the hotel CEO has to pay attention to the emotional intelligence of employees to achieve strong CRM performance. The sentence as originally written was a bit unclear. If this edit does not retain your intended meaning please consider: "Only emotion control does not have a significant impact on customer orientation, and therefore on the ability of an employee to solve customer problems, because it is a passive behavior." Please use the version of the sentence that best captures your original meaning.

• Journal of Distribution Science
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• v.15 no.5
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• pp.53-64
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• 2017

Purpose - This study focuses on use intention of mobile travel Apps by Chinese tourists visiting Korea based on UTAUT model, ISS model and ITM model. And the corresponding market promotion schemes are proposed for operators of mobile travel Apps by the research results. Research design, data, and methodology - After collecting 326 respondents in China with cross-sectional questionnaires, this study begins the empirical research with users of mobile travel Apps, and analyzes data with IBM SPSS 23.0 and IBM AMOS 23.0. Results - The results of this study include the following aspects: firstly, the System quality and Information quality are accepted for hypotheses of Satisfaction and Performance expectancy. Secondly, the Personal Propensity to Trust and Firm Reputation are accepted for Initial Trust hypothesis, and the hypotheses of Firm Reputation and Initial Trust are accepted for Use Intention. Thirdly, the Performance expectancy, Effort expectancy, Social influence are accepted for Use Intention hypothesis. Conclusions - With the increase of tourists visiting Korea, it can be predicted that the needs visiting Korea will be increased persistently for Chinese - this trend brings about the increase of the Chinese travel. First, information quality greatly influences satisfaction and performance expectancy. The research result shows that, the higher the mobile traveling App's information quality is, the higher the satisfaction and performance expectancy will be. Therefore, operators of mobile traveling App should have in-depth investigations towards users, to know the latter's real demand to the information quality and then provide corresponding services. Second, performance expectancy and effort expectancy greatly influence users' intention. Therefore, mobile traveling App operators should improve Apps' convenience and efficiency and, in doing so, find an effective method for market expansion. Third, social influence greatly affects users' intention. The result shows that mobile traveling App operators should pay attention to the influence of mass media and friends' recommendation on users, thereby it is necessary to improve advertisement activities. Fourth, initial trust also influences users' intention. The result shows that initial trust is a key element inducing users to generate use intention. Therefore, mobile traveling Apps operators should make efforts to catch elements that influence users' initial trust.

• Survey Research
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• v.1 no.1
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• pp.45-71
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• 2000

This paper presents two cases of organizational studies and deals with the issue of the level of analysis in the study of organizational phenomena. The first study focuses on the individual level of analysis and examines the effect of a merit pay system on employee attitudes, In this study data were collected using questionnaires from 195 employees across 18 companies. Analyses were are performed using 195 questionnaires at the individual level of analysis. On the other hand, the second study focuses on the group level of analysis examines the relationship between transformational leadership and group performance. In this study data were collected using questionnaires from 320 employees embeded in 40 groups in an insurance company. The 320 questionnaires were aggregated by groups and analyses were performed based on the aggregated scores at the group level of analysis. The importance of the level of analysis in the study of organizational phenomena was discussed.

• The Journal of Bigdata
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• v.7 no.2
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• pp.31-41
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• 2022

Steel is one of the most fundamental components to mechanical industry. However, the quality of products are greatly impacted by the surface defects in the steel. Thus, researchers pay attention to the need for surface defects detector and the deep learning methods are the current trend of object detector. There are still limitations and rooms for improvements, for example, related works focus on developing the models but don't take into account real-time application with practical implication on industrial settings. In this paper, a real-time application of steel surface defects detection based on YOLOv4 is proposed. Firstly, as the aim of this work to deploying model on real-time application, we studied related works on this field, particularly focusing on one-stage detector and YOLO algorithm, which is one of the most famous algorithm for real-time object detectors. Secondly, using pre-trained Yolov4-Darknet platform models and transfer learning, we trained and test on the hot rolled steel defects open-source dataset NEU-DET. In our study, we applied our application with 4 types of typical defects of a steel surface, namely patches, pitted surface, inclusion and scratches. Thirdly, we evaluated YOLOv4 trained model real-time performance to deploying our system with accuracy of 87.1 % mAP@0.5 and over 60 fps with GPU processing.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.49-65
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• 2014

Word of Mouth (WOM) is a behavior used by consumers to transfer or communicate their product or service experience to other consumers. Due to the popularity of social media such as Facebook, Twitter, blogs, and online communities, electronic WOM (e-WOM) has become important to the success of products or services. As a result, most enterprises pay close attention to e-WOM for their products or services. This is especially important for movies, as these are experiential products. This paper aims to identify the network factors of an online movie community that impact box office revenue using social network analysis. In addition to traditional WOM factors (volume and valence of WOM), network centrality measures of the online community are included as influential factors in box office revenue. Based on previous research results, we develop five hypotheses on the relationships between potential influential factors (WOM volume, WOM valence, degree centrality, betweenness centrality, closeness centrality) and box office revenue. The first hypothesis is that the accumulated volume of WOM in online product communities is positively related to the total revenue of movies. The second hypothesis is that the accumulated valence of WOM in online product communities is positively related to the total revenue of movies. The third hypothesis is that the average of degree centralities of reviewers in online product communities is positively related to the total revenue of movies. The fourth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. The fifth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. To verify our research model, we collect movie review data from the Internet Movie Database (IMDb), which is a representative online movie community, and movie revenue data from the Box-Office-Mojo website. The movies in this analysis include weekly top-10 movies from September 1, 2012, to September 1, 2013, with in total. We collect movie metadata such as screening periods and user ratings; and community data in IMDb including reviewer identification, review content, review times, responder identification, reply content, reply times, and reply relationships. For the same period, the revenue data from Box-Office-Mojo is collected on a weekly basis. Movie community networks are constructed based on reply relationships between reviewers. Using a social network analysis tool, NodeXL, we calculate the averages of three centralities including degree, betweenness, and closeness centrality for each movie. Correlation analysis of focal variables and the dependent variable (final revenue) shows that three centrality measures are highly correlated, prompting us to perform multiple regressions separately with each centrality measure. Consistent with previous research results, our regression analysis results show that the volume and valence of WOM are positively related to the final box office revenue of movies. Moreover, the averages of betweenness centralities from initial community networks impact the final movie revenues. However, both of the averages of degree centralities and closeness centralities do not influence final movie performance. Based on the regression results, three hypotheses, 1, 2, and 4, are accepted, and two hypotheses, 3 and 5, are rejected. This study tries to link the network structure of e-WOM on online product communities with the product's performance. Based on the analysis of a real online movie community, the results show that online community network structures can work as a predictor of movie performance. The results show that the betweenness centralities of the reviewer community are critical for the prediction of movie performance. However, degree centralities and closeness centralities do not influence movie performance. As future research topics, similar analyses are required for other product categories such as electronic goods and online content to generalize the study results.

• 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.