A Study on Security Policy Violations of Organization Members (조직 구성원들의 보안정책 위반에 관한 연구)
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- Informatization Policy
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- v.25 no.3
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- pp.95-115
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- 2018
This study aims to examine organization members' intention to violate security policies based on the Person-Environment Fit Model. This study investigated the effect of the relationship between organizational security environment and the individual security value on the intention of organizational security policy violation. The security environments are classified into the organizational information security culture and peers' behavior of security compliance, while the personal values are classified into reconstructing the conduct, distorting the consequence, and devaluing the organization as presented in the moral disengagement theory. Based on the concept of the moral disengagement theory, we measured the individual security values as a second order factor. This study found that the information security culture had a statistically significant impact on devaluing the organization, but did not have as much impact on reconstructing the conduct and distorting the consequence. Peers' behavior of security compliance had a significant impact on reconstructing the conduct, distorting the consequence and devaluing the organization, all of which also had relevant impact on the organizational members' intention of security policy violation.This study measured a persons' perception on security policy breach by presenting scenarios of password sharing that is common in many organizations. This study is expected to make practical contributions, as it deals with challenges that many organizations are actually faced with.
Since reaching the top in the Billboard Main Album Chart 'Billboard 200' with Love Yourself: Tear in May of 2018, BTS once again took first place after just three months in the 'Billboard 200'(September 3, 2018) with the repackaged album Love Yourself: Answer. It opened the doors to the 'Hallyu 4.0' by conquering the main Billboard Chart with a song sung in Korean. BTS rose to the top on the 'Billboard 200' twice, thus being recognized globally for their musical talent(song, dance, promotion, etc.), and took their place in the mainstream music market of the world. BTS moved away from intuitive interaction such as mysticism, abnormality, irregularity, etc. but instead created their own world(BTS Universe) with fans around the world through two-directional communication such as consensus, sharing and co-existence. They are recognized as artists that went beyond being an idol group that simply released a few hit songs that had now elevated popular music to a new form of art. In result, they retained a highly loyal global fan base(A.R.M.Y.) and they are continuously creating good influence with them. This study analyzed the success factors of BTS using the S-M-C-R-E model as follows. ① Sender: BTS'7-person 7-colors fantasy and 'All-in-one storytelling' strategy of producer Bang Shi-hyuk ② Message: Create global consensus of 'you' rather than 'me' ③ Channel: Created real-time common grounds with global fans through social network platforms such as Youtube, Facebook and Instagram ④ Receiver: Formed highly loyal global fandom(A.R.M.Y.) that extends outside of Korea and Asia ⑤ Effect: Created additional economic value and spread good influence
This study explores urban gardening and garden culture in residential area as an everydayness that has been overlooked during the modern period urbanization and investigates the meaning and value of urban gardening from the perspective of urban formations and growth in spontaneous urban residential area, Haebangchon. The result identified that urban gardening as a meaning of contemporary culture is a new clue to improving the urban physical environment and changing the lives and community network of residents. Haebangchon is one of the few remaining spontaneous habitations in Seoul, and was created as a temporary unlicensed shantytown in 1940s. It became the representative habitation for common people in downtown Seoul through the revitalization of the 60s and the local reform through self-sustaining redevelopment projects during the 70s through the 90s. This area still contains the image of times during the 50s to the 60s, the 70s to the 80s and present, with the percentage of long-term stay residents high. Within this context, the site is divided into third quarters, and the research undertaken by observation and investigation to determine characteristics of urban gardening as an everydayness. It can be said that urban gardening and garden culture in Haebangchon is a unique location culture that has accumulated in the crevices of the physical condition and culture of life. These places are an expression of resident's desires that seeking out nature and gardening as revealed in densely-populated areas and the grounds of practical acting and participating in care and cultivation. It forms a unique, indigenous local landscape as an accumulation of everyday life of residents. Urban gardens in detached home has retained the original function of the dwelling and the garden, or 'madang', and takes on the characteristic of public space through the sharing of a public nature as well as semi-private spatial characteristic. Also, urban gardens including small kitchen garden and flowerpots that appear in the narrow streets provide pleasure as a part of nature that blossoms in narrow alley and functions as a public garden for exchanging with neighbors by sharing produce. This paper provides the concept of redefining the relationship between the private-public area that occurs between outside spaces that are cut off in a modern city.
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