A Study on Accounting Information and Stock Price of IoT-related Companies after COVID-19 (코로나-19 이후 IoT 관련 기업의 회계정보와 주가에 관한 연구)
-
- Journal of Internet of Things and Convergence
- /
- v.8 no.1
- /
- pp.1-10
- /
- 2022
The purpose of this study is to establish a foundation for IoT-related industries to secure financial soundness and to dominate the global market after COVID-19. Through this study, the quantitative management status of IoT-related companies was checked. It also was attempted to preemptively prepare for corporate insolvency by examining the relationship between financial ratios in accordance with stock price fluctuations and designation of management items. This study selected 502 companies that were listed on the KOSPI and KOSDAQ in the stock market from 2019 to 2020. For statistical analysis, multiple regression analysis, difference analysis and logistic regression analysis were performed. The research results are as follows. First, it was found that the impact of IoT company accounting information on stock prices differs depending on before and after COVID-19. Second, it was found that there is a difference in the closing stock prices of IoT companies before and after COVID-19. Third, it was found that financial ratios according to stock price fluctuations exist differently after COVID-19. Fourth, it was found that the financial ratios according to the designation of management items after COVID-19 exist differently. Through these studies, some suggestions were made to secure the financial soundness of IoT companies and to lay the groundwork for leaping into the global market after COVID-19. Through the results of this study, it is expected that it will lead the growth of IoT companies and contribute to growth as a decacorn company of the future that can guarantee financial soundness in the changing financial market.
The purpose of this study was to investigate how the management strategies and organizational culture required in the digital economy have an effect on business performance. It provided basic data on management strategies and organizational culture necessary to approach as a digital leading country. For data collection, a survey was conducted from March 1 to May 30, 2022 for companies located in J province and engaged in industries related to the digital economy. The survey was conducted online and non-face-to-face, and a total of 225 companies participated in the survey. For statistical analysis, frequency analysis, exploratory factor analysis and reliability analysis, cluster analysis, independent sample t-test, and multiple regression analysis were performed. The research results are as follows. First, organizational culture was classified into high and low groups according to preference in innovation oriented, relationship oriented, task oriented, and hierarchical oriented. Second, the 4 types of organizational culture showed differences in prospectors strategy, analyzers strategy, defenders strategy, differentiation strategy, cost leadership strategy, financial performance, and non-financial performance according to preference. Third, management strategies affecting financial performance were found to be analyzers strategy, differentiation strategy, prospectors strategy, and cost leadership strategy. Fourth, management strategies affecting non-financial performance were found to be differentiation strategy, defenders strategy, analysis strategy, offensive strategy, cost leadership strategy, and focus strategy. Fifth, organizational culture affecting financial performance was found to be task oriented. Sixth, organizational culture affecting non-financial performance was found to be innovation oriented and relationship oriented. Through these studies, it is expected that the economy will be revitalized in the domestic market and a growth ecosystem that can take a new leap forward is created in the global market.
The purpose of this study is to identify the difficulties in business implementation that blockchain suppliers are experiencing, and to suggest ways to promote blockchain technology by solving them. First, industrial surveys of blockchain supply companies were collected. Next, a survey was conducted to confirm whether financial service users intend to use blockchain technology. The research results are as follows. First, in user characteristics, usefulness and innovation were found to have an effect on intention to use. In the technical characteristics, suitability and reliability were found to affect the intention to use. Second, in user characteristics, usefulness and innovativeness were found to affect the intention to use by mediating promotion conditions. In the technical characteristics, suitability and reliability were found to affect the intention to use by mediating the promotion conditions. Third, it was found that the new technology environment modulates the effect of ubiquity and innovativeness on the intention to use. The new technology environment was found to moderate the impact of security on intention to use. Fourth, it was found that the organizational environment moderates the effect of security and suitability on the intention to use. A plan to solve the difficulties of these blockchain suppliers and a plan to promote blockchain-based financial services are presented.
Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used