Regulation (EC) No 261/2004 ("Regulation") is a common rule on compensation and assistance to passengers in the event of denied boarding and of cancellation or long delay of flights. In some recent cases of European nations, passengers sued the air carrier in order to obtain monetary compensation under Article 7(1) of the Regulation. Some courts dismissed the actions on the grounds that, unlike denied boarding or cancellation of the flight, the Regulation provides no compensation in relation to delayed flights. However, Court of Justice of the European Union(CJEU) ruled that Regulation 261/2004 must be interpreted to mean that passengers whose flights are delayed have a right to compensation in cases when the loss of time is equivalent to, or is in excess of three hours - where the passengers eventually reached their final destination three hours or more later than the originally scheduled arrival time. It is true that a strict interpretation of the regulation would suggest that passengers whose flight has merely been delayed are not entitled to compensation. They should only be offered assistance in accordance with the Articles 6 and 9. Nevertheless, the Court recognized the same right to the same compensation for passengers of flights delayed by more than three hours as that explicitly provided for passengers of cancelled flights. On the one hand, the Court bases this ruling on the recitals of the Regulation, in which the legislature links the question of compensation to that of a long delay, while indicating that the Regulations seek to ensure a high level of protection for passengers regardless of whether they are denied boarding or their flight is cancelled or delayed. On the other hand, the Court interprets the relevant provisions of the Regulation in light of the general principle of equal treatment. Furthermore, the Court delivered a ruling that the loss of time inherent in a flight delay, which constitutes an inconvenience within the intention of Regulation No 261/2004 and which cannot be categorized as 'damage occasioned by delay' within the meaning of Article 19 of the Montreal Convention, cannot come within the scope of Article 29 of that convention. Consequently, under this view, the obligation under Regulation No 261/2004 intended to compensate passengers whose flights are subject to a long delay is in line with Article 29 of the Montreal Convention. Although the above interpretation of the Court can be a analogical interpretation, the progressive attitude of the Regulation and the view of Court forward to protect passengers' interest is a leading role in the area of international air passenger transportation. Hopefully, after the model of the positive support in Europe, Korea can establish a concrete rule for protecting passengers' right and interest.
Vegetation development in rivers is one of the important issues not only in academic fields such as geomorphology, ecology, hydraulics, etc., but also in river management practices. The problem of river vegetation is directly connected to the harmony of conflicting values of flood management and ecosystem conservation. In Korea, since the 2000s, the issue of river vegetation and land formation has been continuously raised under various conditions, such as the regulating rivers downstream of the dams, the small eutrophicated tributary rivers, and the floodplain sites for the four major river projects. In this background, this study proposes a method for classifying the distribution of vegetation in rivers based on remote sensing data, and presents the results of applying this to the Naeseong Stream. The Naeseong Stream is a representative example of the river landscape that has changed due to vegetation development from 2014 to the latest. The remote sensing data used in the study are images of Sentinel 1 and 2 satellites, which is operated by the European Aerospace Administration (ESA), and provided by Google Earth Engine. For the ground truth, manually classified dataset on the surface of the Naeseong Stream in 2016 were used, where the area is divided into eight types including water, sand and herbaceous and woody vegetation. The classification method used a random forest classification technique, one of the machine learning algorithms. 1,000 samples were extracted from 10 pre-selected polygon regions, each half of them were used as training and verification data. The accuracy based on the verification data was found to be 82~85%. The model established through training was also applied to images from 2016 to 2020, and the process of changes in vegetation zones according to the year was presented. The technical limitations and improvement measures of this paper were considered. By providing quantitative information of the vegetation distribution, this technique is expected to be useful in practical management of vegetation such as thinning and rejuvenation of river vegetation as well as technical fields such as flood level calculation and flow-vegetation coupled modeling in rivers.
Introduction As consumers' purchase behavior change into a rational and practical direction, the discount store industry came to have keen competition along with rapid external growth. Therefore as a solution, distribution businesses are concentrating on developing PB(Private Brand) which can realize differentiation and profitability at the same time. And as improvement in customer loyalty beyond customer satisfaction is effective in surviving in an environment with keen competition, PB is being used as a strategic tool to improve customer loyalty. To improve loyalty among PB users, it is necessary to develop PB by examining properties of a customer group, first of all, quality level perceived by consumers should be met to obtain customer satisfaction and customer trust and consequently induce customer loyalty. To provide results of systematic analysis on relations between antecedents influenced perceived quality and variables affecting customer loyalty, this study proposed a research model based on causal relations verified in prior researches and set 16 hypotheses about relations among 9 theoretical variables. Data was collected from 400 adult customers residing in Seoul and the Metropolitan area and using large scale discount stores, among them, 375 copies were analyzed using SPSS 15.0 and Amos 7.0. The findings of the present study followed as; We ascertained that the higher company reputation, brand reputation, product experience and brand familiarity, the higher perceived quality. The study also examined the higher perceived quality, the higher customer satisfaction, customer trust and customer loyalty. The findings showed that the higher customer satisfaction and customer trust, the higher customer loyalty. As for moderating effects between PB and NB in terms of influences of perceived quality factors on perceived quality, we can ascertain that PB was higher than NB in the influences of company reputation on perceived quality while NB was higher than PB in the influences of brand reputation and brand familiarity on perceived quality. These results of empirical analysis will be useful for those concerned to do marketing activities based on a clearer understanding of antecedents and consecutive factors influenced perceived quality. At last, discussions about academical and managerial implications in these results, we suggested the limitations of this study and the future research directions. Research Model and Hypotheses Test After analyzing if antecedent variables having influence on perceived quality shows any difference between PB and NB in terms of their influences on them, the relation between variables that have influence on customer loyalty was determined as Figure 1. We established 16 hypotheses to test and hypotheses are as follows; H1-1: Perceived price has a positive effect on perceived quality. H1-2: It is expected that PB and NB would have different influence in terms of perceived price on perceived quality. H2-1: Company reputation has a positive effect on perceived quality. H2-2: It is expected that PB and NB would have different influence in terms of company reputation on perceived quality. H3-1: Brand reputation has a positive effect on perceived quality. H3-2: It is expected that PB and NB would have different influence in terms of brand reputation on perceived quality. H4-1: Product experience has a positive effect on perceived quality. H4-2: It is expected that PB and NB would have different influence in terms of product experience on perceived quality. H5-1: Brand familiarity has a positive effect on perceived quality. H5-2: It is expected that PB and NB would have different influence in terms of brand familiarity on perceived quality. H6: Perceived quality has a positive effect on customer satisfaction. H7: Perceived quality has a positive effect on customer trust. H8: Perceived quality has a positive effect on customer loyalty. H9: Customer satisfaction has a positive effect on customer trust. H10: Customer satisfaction has a positive effect on customer loyalty. H11: Customer trust has a positive effect on customer loyalty. Results from analyzing main effects of research model is shown as