Purpose – The purpose of this study is to investigate the perceived importance of soft skills for hotel employees, their willingness to use electronic learning (e-learning) as a training tool to improve their soft skills, and the impact of hotel employees' individual characteristics (i.e. motivation, self-efficacy, technology anxiety) on their intentions to use e-learning across different age groups. Design/methodology/approach – The sample was randomly selected from hotel employees working at various upscale international chain hotels in South Korea. The data were analyzed using structural equation modeling (SEM) to simultaneously measure the impact of four independent variables on the intention to use e-learning for both younger and older learners. Findings – The analysis revealed that responsibility, self-esteem, sociability, and working with diverse groups were rated more important by younger hotel employees. The results suggest that learners who have higher extrinsic motivations in using e-learning will be more likely to use e-learning. However, the other variables (i.e. technology anxiety, self-efficacy, and intrinsic motivation) did not significantly affect the intention to use e-learning. Research limitations/implications – The findings are practical for hotel managers/trainers, because they can focus on external rewards instead of internal rewards to motivate employees to use e-learning. Age did not have a moderating effect between technology anxiety and the intention to use e-learning. Since the respondents tend to be younger and have a higher standard of education compared with those of the general population, they may more accurately represent hotel employees at upscale or international chain hotels. Originality/value – The study proposes a framework to examine the impact of hotel employees' individual characteristics on their intention to use e-learning. The study also validates some relationships that have shown inconsistent results in previous studies. Future research could employ qualitative studies to investigate underlying dimensions of the variables tested in this study.

Since the fire causes serious disasters, fire detection has been an important study to protect human life. Based on the deficiencies of existing fire detection on real-time and monitoring accuracy, the wireless sensor network technique for fire detection was introduced and needed. In this paper, we proposed the fire detection algorithm in video sequences on wireless sensor network. The proposed fire detection algorithm processes visual information acquired through static camera that lets us incorporate the algorithm to CCTV surveillance system, and therefore does not require an additional expanses on conventional fire sensors. Fire detection method based on motion information, any input image, is compared to background in order to identify foreground. Background is statistically modeled by mixture of Gaussians. To detect the foreground of video sequences, the proposed color detection algorithm was performed in RGB space. The procedure of algorithm eliminates all objects that do not fulfill color requirements without fire-like objects. And the change map and blob's area are computed, change map shows temporal variation of pixel between two consecutive binary frames, and percentage area increase or decrease characterizes a fire property for swinging.