As platforms become primary decision making tools, platforms for decision have been introduced to improve quality of decision results. Because, decision platforms applied augmented decision-making process which uses experiences and feedback of users. This process creates a variety of alternatives tailored for users’ abilities and characteristics. However, platform users choose alternatives before considering significant quality factors based on securing decision quality. In real world, platform managers use an algorithm that distorts appropriate alternatives for their commercial benefits. For improving quality of decision-making, preceding researches approach trying to increase rational decision -making ability based on experiences and feedback. In order to overcome bounded rationality, users interact with the machine to approach the optional situation. Differentiated from previous studies, our study focused more on characteristics of users while they use decision platforms. This study investigated the impact of quality factors on decision-making using platforms, the dimensions of systematic factors and user characteristics. Systematic factors such as platform reliability, data quality, and user characteristics such as user abilities and biases were selected, and measuring variables which trust, satisfaction, and loyalty of decision platforms were selected. Based on these quality factors, a structural equation research model was created. A survey was conducted with 391 participants using a 7-point Likert scale. The hypothesis that quality factors affect trust was proved to be valid through path analysis of the structural equation model. The key findings indicate that platform reliability, data quality, user abilities, and biases affect the trust, satisfaction and loyalty. Among the quality factors, group bias of users affects significantly trust of decision platforms. We suggest that quality factors of decision platform consist of experience-based and feedback-based decision-making with the platform's network effect. Through this study, the theories of decision-making are empirically tested and the academic scope of platform-based decision-making has been further developed.

There has been a steady rate of accident in Coal Thermal Power Plants which have relatively higher chance of mortality. However, neither the systematic view of safety management nor the methodology such as safety factors or system requirements are yet to be studied in detail. Therefore, this study aims to propose a methodology to preemptively deal with safety issues and to secure fact focused responsibility in safety. It consists of two main parts. First, the Safety Measurement Index(SMI) with total 50 factors is proposed by analyzing the key factors that contribute to safety accidents based on failure mode and effect analysis (FMEA) and quality function deployment (QFD). To analyze the safety requirements, index presented by major countries and organizations are discussed. Second, main features of intelligent CCTV are studied to determine their relative importance for the framework of Smart Safety Management System (SSMS). Main features are discussed with four technological steps. Also, QFD was held to analyze to analyze how key technologies deal with Quality Measurement Index(QMI). The research results of this study reveal that scientific approaches could be utilized in integrating CCTV technologies into a smart safety management system in the era of Industry 4.0. Moreover, this reasearch provides an specific approach or methodology for dealing with safety management in Coal Thermal Power Plant.

Kwanganbyeo is a japonica rice variety developed from the cross between Namyang 7 and SR14779-HB234-31, anelite line derived from the cross between Cheonmabyeo and Aichi 37 at the National Crop Experiment Station, RDA, and washi-tecture and light-dark gre