Due to social phenomena such as rapid aging in Korea, nuclear familyization, single marriage, and low birth rate, the number of Companion animals and the number of households with Companion animals are increasing due to the increase in single-person households. In fact, one out of every four households has a pet, and the scale of the industry is expected to reach 6 trillion won in 2027. In particular, in a situation where the Companion animal cosmetics market is in the spotlight amid the diversification of the pet industry, there is a great lack of research on related research and industry development methods. Accordingly, this study attempted to search and analyze academic data, patented technologies, and the latest data related to pet cosmetics and provide them as basic data for the Companion animal cosmetics industry, and the results are as follows. Academic data included verification of the effectiveness of natural materials to improve the skin condition of dogs, analysis of the pet cosmetics industry, and research on ICT-converged pet cosmetics, and the industry was mainly cleaning cosmetics, with pet shampoo launches in Amorepacific, LG Household & Healthcare, and Aekyung. In the patented technology for pets, a patent has been registered for natural product material composition and formulation ratio for skin moisturizing, skin improvement, thinning, and inflammation symptom relief. As a result of this award, it was confirmed that research and development are still insufficient compared to the consumption demand of the pet cosmetics market, and it is believed that industry analysis and development research in related fields should be actively carried out.
Assuming that not all background music in advertising function as effective retrieval cues for the advertised messages, this study proposes that we should be able to distinguish the retrieval cue effect of music from the simple ad exposure effect. This study tries to identify which specific characteristics of music are related to the retrieval cue effect. Our experiment focuses on congruency and familiarity of music as key characteristics of music which affect the effectiveness of the music's role as a retrieval cue for the advertised messages. We used four groups of subjects to test the retrieval cue effect of the background music. Each group was exposed to one of the four different types of background music and was again sub-divided into an experimental and a control group (i.e., a total of eight independent sub-groups were included in the experiment.) The first two sub-groups were exposed to the experimental advertisement with the background music of high congruency and high familiarity. After the ad exposure, the background music was provided as a retrieval cue to only one of the two sub-groups. Comparison of the memory performance between the two sub-groups will reveal the net retrieval cue effect of the music of high congruency and high familiarity. Similarly, another two sub-groups watched the same ad but with the background music of high congruency and low familiarity. Also the same ad but with the music of low congruency/ high familiarity was shown to another two sub-groups and that of low congruency and low familiarity music was to another two. Among the two sub-groups with the same music, only one group had the music cue at the memory tasks. One hundred and seventy four undergraduate students at the college of one of authors in Asia participated in the study. Their ages ranged from 18 to 24 with a median of 20. The sample was composed of 51.7 percent male subjects. They were randomly assigned to each of the eight sub-group. The results show that the music highly congruent with the advertised message facilitates the message retrieval, while the low congruency music cue does not. It was also found that the low familiarity music cue improves memory performance only when the music is perceived as congruent with the advertised message. From a theoretical and practical perspective, this study provides boundary conditions for effective retrieval and suggests that the congruent music specifically created for the ad is a more effective retrieval cue than other types of music cues.
Small and medium-sized enterprises ("SMEs") are vulnerable to trade secret misappropriation. Korea's legislation for the protection of SMEs' trade secrets and provision of civil, criminal, and administrative remedies includes the SME Technology Protection Act, the Unfair Competition Prevention Act, the Industrial Technology Protection Act, the Mutually Beneficial Cooperation Act, and the Subcontracting Act. Among these acts, the revised SME Technology Protection Act of 2018 introduced the "administrative technology misappropriation investigation system" to facilitate a rapid resolution of SMEs' technology misappropriation disputes. On September 27, 2021, Korea's Ministry of SMEs announced that it had reached an agreement to resolve the dispute between Hyundai Heavy Industries and Samyeong Machinery through the administrative technology misappropriation investigation system. However, not until 3 years and a few months passed since the introduction of the system could it be used to resolve an SME's technology misappropriation dispute with a large corporation. So there arose a question on the usefulness of the system. Therefore, we conducted a comparative legal analysis of Korea's laws enacted to protect trade secrets of SMEs and to address technology misappropriation, focusing on their legislative purpose, protected subject matter, types of misappropriation, and legal remedies. Then we analyzed the administrative technology misappropriation investigation system and the cases where this system was applied. We developed a proposal to enhance the usefulness of the system. The expert interviews of 4 attorneys who are experienced in the management of the system to check the practical value of the proposal. Our analysis shows that the lack of compulsory investigation and criminal sanctions is the fundamental limitation of the system. We propose revising the SME Technology Protection Act to provide correction orders, criminal sanctions, and compulsory investigation. We also propose training professional workforces to conduct digital forensics, enabling terminated SMEs to utilize the system, and assuring independence and fairness of the mediation and arbitration of the technology misappropriation disputes.
In the age of digital media transformation, the rapid rise of social media has changed the paradigm of traditional marketing techniques by leveraging the influence of influencers. However, the influence of influencers cannot be freed from ethical issues that arise as individuals, so virtual influencers are emerging as a countermeasure. This study is a study on how to increase the influencer effect of virtual models with a focus on the MZ generation in medical service. This study investigated whether respondents in their 40s or younger were aware of 'Rosy', a virtual influencer, and then conducted a survey on those who recognized 'Rosy'. As a result of this study, first, both cognitive and emotional motivation had a positive influence on fanship and attractiveness for virtual influencer. In addition, it was found that there was a difference in follow motive according to gender. Second, in order to lead to the intention of visiting hospitals, which is the medical service industry, only the cognitive motives with useful and reliable information and useful information for the virtual influencer were found to be significant in intention to visit.
Small- and medium-sized enterprises (SMEs) have strong incentives to engage in open innovation to enhance innovation efficiency and effectiveness due to their 'liability of smallness.' Previous research examined the performance effects of various open innovation practices, but whether coupled open innovation practices positively affect SMEs' firm performance is somewhat controversial. To resolve the issue, this study examined the effects of coupled open innovation activities on SMEs' firm performance using Heckman's two stage model to control endogeneity of the firms' self-selection bias in open innovation engagement. This study used the Korean Innovation Survey (KIS) 2020 collected by the Science and Technology Policy Institute (STEPI), and tested the effects of SMEs' coupled open innovation activities, R&D and non-R&D cooperation, on their innovative and financial performance indicators. The results showed that SMEs' R&D cooperation positively affects the new-to-market (NTM) product innovation only. Moreover, SMEs' non-R&D cooperation has positive effects on the product innovation, business process innovation, new-to-the-market product innovation, and new-to-firm (NTF) product innovation. However, the results showed that both R&D and non-R&D innovation cooperation activities have no significant effects on SMEs' financial performance indicators. This study contributes to research on SMEs' open innovation and provides insights for SMEs' managers and policymakers.
International Development Cooperation (IDC hereafter), which closely relates to international trade and foreign direct investment, is gaining global importance regarding diplomatic relations and economic cooperation. As Korea contributes increasing resources to the international community, Korea should play a larger role in the IDC along with increasing academic cooperation, impling more necessity to grow research on the IDC as a crucial element of international trade. The IDC has focused on the provision of basic human needs, such as food, shelter, and clothing. However, higher education has not yet be explored for its effectiveness or validity as to service area including foreign trade or insurance. In this regard, this research aims to review existing IDC literature, to propose a project for insurance education, and to provide alternative ways for future development of the IDC. This paper is structured as follows. First, the literature review begins with the IDC's history and development, review of traditional methods of providing basic human needs (food or sheltering) and public health, importance of job creation and business activities to alleviate poverty, and the introduction of insurance education as a vehicle to reduce poverty. Results of the case study provide implications for service area projects including foreign trade education.
In logistics and distribution, Market Basket Analysis (MBA) is used as an important means to analyze the correlation between major sales products and to increase internal operational efficiency. In particular, the results of market basket analysis are used as important reference data for decision-making processes such as product purchase prediction, product recommendation, and product display structure in stores. With the recent development of e-commerce, the number of items handled by a single distribution and logistics company has rapidly increased, And the existing analytical methods such as Apriori and FP-Growth have slowed down due to the exponential increase in the amount of calculation and applied to actual business. There is a limit to examining important association rules to overcome this limitation, In this study, at the Main-Category level, which is the highest classification system of products, the utility item set mining technique that can consider the sales volume of products together was used to first select a group of products mainly sold together. Then, at the sub-category level, the types of products sold together were identified using FP-Growth. By using this sequential layer filtering technique, it may be possible to reduce the unnecessary calculations and to find practically usable rules for enhancing the effectiveness and profitability.
In the growing field of green marketing there are various psychological influences that can lead to green purchase behavior. An understanding of these influences can lead to greater green marketing effectiveness. The purpose of this paper is to analyze the effects of several value types, environmental attitudes, and preference for product attributes on green purchase behavior. To this end, a conceptual model has been proposed and tested for empirical verification with the use of a survey. Data collected from 266 Korean respondents are analyzed using path analysis. Results provide support for the proposed model, demonstrating positive links among universalism, environmental attitudes, preference for environmental attribute, and green buying behavior. It indicates that individuals with universalism as a preferred value type are high in their environmental attitudes and finally, tend to buy green products through their preference for environmental attribute. The mediating role of preference for price is not significant between environmental attitudes and green purchase behavior. The present findings, in addition, contribute the width of understanding of various proenvironmental behaviors by focusing on green purchase behavior and surveying with a Korean sample. The implications for the practices of green marketing are discussed.
This study primarily aimed to develop an automated stuttering identification and classification method using artificial intelligence technology. In particular, this study aimed to develop a deep learning-based identification model utilizing the convolutional neural networks (CNNs) algorithm for Korean speakers who stutter. To this aim, speech data were collected from 9 adults who stutter and 9 normally-fluent speakers. The data were automatically segmented at the phrasal level using Google Cloud speech-to-text (STT), and labels such as 'fluent', 'blockage', prolongation', and 'repetition' were assigned to them. Mel frequency cepstral coefficients (MFCCs) and the CNN-based classifier were also used for detecting and classifying each type of the stuttered disfluency. However, in the case of prolongation, five results were found and, therefore, excluded from the classifier model. Results showed that the accuracy of the CNN classifier was 0.96, and the F1-score for classification performance was as follows: 'fluent' 1.00, 'blockage' 0.67, and 'repetition' 0.74. Although the effectiveness of the automatic classification identifier was validated using CNNs to detect the stuttered disfluencies, the performance was found to be inadequate especially for the blockage and prolongation types. Consequently, the establishment of a big speech database for collecting data based on the types of stuttered disfluencies was identified as a necessary foundation for improving classification performance.
Smart home services are growing rapidly as the development of the Internet of Things (IoT) opens the era of the so-called "Connected Living." Although personal information leaks through smart home cameras are increasing, however, users-while concerned-tend to take passive measures to protect their personal information. This study theoretically explained and verified how to design effective software update notification messages for smart home cameras to ensure that users comply with the recommended security behavior (i.e., update installation). In a survey experiment participated in by 120 actual users, the effectiveness of both emotional appeals (i.e., security breach warning images for fear appeals) and rational appeals (i.e., loss-framed messages emphasizing the negative consequences of not installing the updates) were confirmed. The results of this study provide theoretical interpretations and practical guidelines on the message design features that are effective for threat appraisals (i.e., severity, vulnerability) of smart home camera users and their protection motivation.
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