소셜 빅데이터를 활용한 ‘저비용항공사’ 인식에 관한 연구
Network Analysis on the Recognition of‘Low Cost Carrier’ Using Social Big Data
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참고문헌
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This study is to investigate the customers’ perceptions on LCCs(Low cost carriers) of South Korea that have grown in quantity. Social data were collected using a search key word ‘Low cost carrier’ and frequency analysis and network analysis were used. The results are as follows. First, the top 10 related words of LCCs were Low cost carrier, travel, use, carrier, aircraft, T-way airlines, luggage, reservation, Jeju-Air, and aviation. Second, the centrality analysis indicated that words with high degree centrality were also high in eigenvector centrality. Third, the results of CONCOR were divided into 4 clusters. Each clust was named as ‘LCCs of Korea and Destinations in Asia’, ‘LCCs of Europe and Destinations in Europe’, ‘Use of LCCs’, and ‘Accidents of LCCs’. This study is meaningful in adapting new approach on base of big data and trying to find out hidden insight on LCCs. The results are as follows. First. LCCs should build customer trust through safety-related information and safety-related activities. Second, the centrality analysis found that words with high degree centrality were also high in eigenvector centrality. Third, LCCs are needed to develop various destinations and routes outside Southeast Asia while maintaining a low-cost policy. and improve diverse and differentiated services. LCCs can be expected to see a sharp rise in LCCs' growth if they can increase their confidence in LCCs’ safety by promoting LCCs' safety to customers, overhauling systematic aircraft and strengthening safety education and services for flight attendants.
Abstract
This study is to investigate the customers’ perceptions on LCCs(Low cost carriers) of South Korea that have grown in quantity. Social data were collected using a search key word ‘Low cost carrier’ and frequency analysis and network analysis were used. The results are as follows. First, the top 10 related words of LCCs were Low cost carrier, travel, use, carrier, aircraft, T-way airlines, luggage, reservation, Jeju-Air, and aviation. Second, the centrality analysis indicated that words with high degree centrality were also high in eigenvector centrality. Third, the results of CONCOR were divided into 4 clusters. Each clust was named as ‘LCCs of Korea and Destinations in Asia’, ‘LCCs of Europe and Destinations in Europe’, ‘Use of LCCs’, and ‘Accidents of LCCs’. This study is meaningful in adapting new approach on base of big data and trying to find out hidden insight on LCCs. The results are as follows. First. LCCs should build customer trust through safety-related information and safety-related activities. Second, the centrality analysis found that words with high degree centrality were also high in eigenvector centrality. Third, LCCs are needed to develop various destinations and routes outside Southeast Asia while maintaining a low-cost policy. and improve diverse and differentiated services. LCCs can be expected to see a sharp rise in LCCs' growth if they can increase their confidence in LCCs’ safety by promoting LCCs' safety to customers, overhauling systematic aircraft and strengthening safety education and services for flight attendants.
참고문헌 (24)
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