The hospitality, tourism, and travel (HTT) industry has a significant environmental impact due to its water, energy, and waste production. One of the main challenges faced by the HTT industry is the reduction of the negative environmental impact of hotel businesses. Recent studies have shown that consumers are willing to pay a premium for green hotels that adopt environmentally friendly practices to minimize their impact on the environment. While interest in these practices has been increasing, not all consumers are familiar with actual benefits of green hotels. Therefore, it is necessary to employ strategies to increase consumers’ awareness and encourage positive pre-purchasing decisions when selecting a green hotel.

본 연구는 골프연습장 이용객을 대상으로 골프 참여자의 건강신념과 환경관심도 및 운동지속에는 어떠한 관계가 있는지 규명하는데 목적이 있다. 본 연구의 목적을 달성하기 위하여 SPSS 18.0과 AMOS 18.0을 이용하여 빈도분석, 탐색적 요인분석, 확인적 요인분석, 신뢰도분석과 상관관계분석을 실시하였고 모형을 설정한 뒤 구조방적식모형(SEM)을 통하여 변인간의 인과적 관계를 규명하였다. 이상과 같은 연구 방법과 연구모형 검증을 기초로 하여 본 연구에서 도출된 결과는 다음과 같다. 첫째, 골프 참여자들의 건강 신념은 환경관심도에 정적인 영향을 미치는 것으로 나타났다. 둘째, 골프 참여자들의 환경관심도는 운동지속에 정적인 영향을 미치는 것으로 나타났다. 셋째, 골프 참여자의 건강신념은 운동지속에 정적인 영향을 미치는 것으로 나타났다. 넷째, 골프 참여자의 건강신념과 운동지속의 관계에서 환경관심도는 매개하는 것으로 나타났다. 골프참여자들이 골프를 통해 신체적, 심리적 건강효과를 증진시키고 나아가 골프 기술습득 이나 기량향상 등의 성취감을 만들어 환경관심도가 높아진다면, 보다 나은 삶의 질을 영위할 수 있을 것이다.

The paper examined empirically environmental beliefs among Jeju women in South Korea by analyzing survey data collected in 1999. The findings indicate that 58 per cent of Jeju women held pro-environmental beliefs that were measured with the Revised New Ecological Paradigm Scale. Environmental beliefs being structured with four dimensions in the mind of Jeju women educational attainment proved a significant determinant for the two belief dimensions: human’s excessive involvement in nature and human superiority over nature. Those with higher educational attainment agreed strongly with the belief in human’s excessive involvement in nature whereas rejecting the belief in human superiority over nature.

The objective of this study was to evaluate the causal relationships among environmental belief, ambivalence, subjective norm, attitude and meat consumption behavior. A total of 318 questionnaires were completed. A structural equation model was employed to assess the causal effects of constructs. The results of the study demonstrated that the structural analysis results for the data also indicated excellent model fit. The effects of environmental belief, ambivalence, and subjective norm on attitude were statistically significant. The effects of environmental belief, subjective norm and attitude on meat consumption were statistically significant. The effects of attitude on intention were statistically significant. As had been expected, intention exerted a significant effect on meat consumption. Moreover, environmental belief and ambivalence exerted significant indirect effects on meat consumption through attitude. Subjective norm exerted a significant indirect effect on meat consumption through attitude and intention. Subjective norm also exerted a significant indirect effect on intention through attitude. In developing and testing conceptual models which integrate the relationship among behavioral belief, attitude variable, behavioral intention and meat consumption, this study may approach a deeper understanding of the complex relationship among meat consumption behavior-related variables. Greater understanding of the complex relationship among meat consumption behavior-related variables can improve the practical or managerial diagnosis of the problem and opportunities for different marketing strategies including meat production and meat product development and marketing communication.

The purpose of this study was to measure the causal relationships among affective belief, environmental belief, subjective norm, attitude and meat consumption behavior. A total of 318 questionnaires were completed. Structural equation model was used to measure the causal relationships among the constructs. Results of the study demonstrated that the structural analysis result for the data also indicated excellent model fit. The effects of affective belief, environmental belief and subjective norm on attitude were statistically significant. The effects of affective belief, environmental belief and subjective norm on meat consumption were statistically significant. As expected, attitude had a significant effects on behavioral intention. Moreover, attitude played a mediating role in the relationship between affective belief and meat consumption, environmental belief and meat consumption, subjective norm and meat consumption. Consumption played a mediating role in the relationship between attitude and behavioral intention. In conclusion, based on structural analysis, a model was proposed of interrelations among affective belief, environmental belief, subjective norm, attitude, meat consumption and intention. It should be noted that the original model was modified and should, preferably, be validated in future research. Other variables may be incorporated to form models that consist of new antecedent and consequence pairs.

This paper suggests the method of the spherical signature description of 3D point clouds taken from the laser range scanner on the ground vehicle. Based on the spherical signature description of each point, the extractor of significant environmental features is learned by the Deep Belief Nets for the urban structure classification. Arbitrary point among the 3D point cloud can represents its signature in its sky surface by using several neighborhood points. The unit spherical surface centered on that point can be considered to accumulate the evidence of each angular tessellation. According to a kind of point area such as wall, ground, tree, car, and so on, the results of spherical signature description look so different each other. These data can be applied into the Deep Belief Nets, which is one of the Deep Neural Networks, for learning the environmental feature extractor. With this learned feature extractor, 3D points can be classified due to its urban structures well. Experimental results prove that the proposed method based on the spherical signature description and the Deep Belief Nets is suitable for the mobile robots in terms of the classification accuracy.