University Student's Beliefs, Attitudes and Intention with Regard to Applying for Jobs in SME (중소기업 취업에 관한 대학생들의 신념, 태도 및 취업의도에 관한 연구)
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- Korean small business review
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- v.39 no.3
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- pp.57-76
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- 2017
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While the unemployment rate is rising rapidly due to recent economic recession at home and abroad, university students' reluctance to apply for jobs in Small and Medium Enterprises (SME's) causes instability in manpower supply and demand and social unrest. To provide insights for solving the problem, this study explores how beliefs and attitudes of university students influence their intention to apply for jobs in SME's using Theory of Planned Behavior proposed by Icek Ajzen. This study followed the 2-stage survey methodology suggested by Ajzen. In the first stage of pilot study, a small sample of university students was used to illicit readily accessible behavioral outcomes, normative referents, and control factors. In the second stage of main study, the standard questionnaire was designed and administered and data were collected and analysed using the PLS Structural Equation Modeling (SEM) technique. PLS-SEM was used instead of Covariance Based (CB)- SEM considering the exploratory nature of this study. In overall, the results showed that TPB is very effective in explaining and predicting the university student's intention to apply for jobs in SEM's. Gender turned out to be a significant moderator variable in the relations between intention and its influence factors. Student's scholastic performance showed a negative correlation with intention. More research efforts need to be exerted to better understand university student's job seeking behavior.
The construction of smart communities is a new method and important measure to ensure the security of residential areas. In order to solve the problem of low accuracy in face recognition caused by distorting facial features due to monitoring camera angles and other external factors, this paper proposes the following optimization strategies in designing a face recognition network: firstly, a global graph convolution module is designed to encode facial features as graph nodes, and a multi-scale feature enhancement residual module is designed to extract facial keypoint features in conjunction with the global graph convolution module. Secondly, after obtaining facial keypoints, they are constructed as a directed graph structure, and graph attention mechanisms are used to enhance the representation power of graph features. Finally, tensor computations are performed on the graph features of two faces, and the aggregated features are extracted and discriminated by a fully connected layer to determine whether the individuals' identities are the same. Through various experimental tests, the network designed in this paper achieves an AUC index of 85.65% for facial keypoint localization on the 300W public dataset and 88.92% on a self-built dataset. In terms of face recognition accuracy, the proposed network achieves an accuracy of 83.41% on the IBUG public dataset and 96.74% on a self-built dataset. Experimental results demonstrate that the network designed in this paper exhibits high detection and recognition accuracy for faces in surveillance videos.
It is common description that modern society is In the era of limitless competition. In order to challenge the change of economy and its management at home and abroad, organization should be changed anew, and in this case, there accompanies conflict or trouble whether the subject of change wants it or not. Therefore, according to change, we should concern with settlement of small troubles as well as big ones, and by managing the conflict or trouble productively and originally, it should be utilized as new fatality and chance to develop something in organization. In the system organized by people, there exist various conflicts in accordance with the target and want of the system, therefore giving no freedom to each Individual member of the system, and this is an unavoidable tate in consideration of the modem society where the survival of mankind and human systems should be guaranteed. Therefore, it determines the coordinates of success of any of organizations to manage conflict or trouble well, and so, when decreasing or increasing conflicts so that the organization exerts its full influence, it is note worthy that conflict itself should be rationally and efficiently managed. In a view point of the theory of organization and its behavior, relating job satisfaction with the performance and validity of organization, the influence of individual conflict is so great on the rate of job transfer within an organization or nonattendance, even on the productivity of the organization. So, the manager to cope with conflict within an organization should devise following three plans to manage conflict for job satisfaction and conflict settlement. In conclusion, it is suggested that in order to manage conflict within an organization well, some plans to control conflict should be well utilized while giving more efforts to improving management of individual conflict, job satisfaction, validity of organization, productivity, etc and all the members of organization should remember that the Issue of conflict within an organization be recognized ad an opportunity of new development and a way to settle a trouble within an organization, and a direction of conflict management should be suggested so that new innovation ca be created.
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