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作 者:康莉 李振亭 李文瑞 KANG Li;LI Zhenting;LI Wenrui(School of Geography and Tourism,Shaanxi Normal University,Xi′an 710119,Shaanxi,China;Shaanxi Key Laboratory of Tourism Informatics,Xi′an 710119,Shaanxi,China)
机构地区:[1]陕西师范大学地理科学与旅游学院,陕西西安710119 [2]陕西省旅游信息科学重点实验室,陕西西安710119
出 处:《陕西师范大学学报(自然科学版)》2020年第4期20-27,共8页Journal of Shaanxi Normal University:Natural Science Edition
基 金:国家自然科学基金(41271158)。
摘 要:根据入境旅游流的流量份额与流质指数双重指标,提出入境旅游市场结构的流量-流质(Q-Q)分析模型,把反映流量的市场份额数量指标和反映流质的质量指标结合,用两个指标组合从两个维度综合地反映一个市场的规模和发展质量状态。将入境旅游流流量、流质研究与市场结构分析相结合,借鉴波士顿矩阵分析法,把我国省域入境旅游市场划分为“明星市场”“金牛市场”“瘦狗市场”和“问题市场”。以2009—2018年中国31个省(自治区、直辖市)的入境旅游为研究对象,利用Q-Q模型对中国省域入境旅游市场结构及其变化进行分析,结果发现:中国入境旅游的“明星市场”集中于经济发达、交通区位优越的东南沿海地区;中国入境旅游市场发展不均衡,优质市场较少,整体质量偏低;中国省域入境旅游市场结构正在不断趋于优化。Based on the double-indicator of flow share and flow quality index of China′s inbound tourism flow,the Q-Q model of China′s inbound tourism market structure is proposed,which reflecting the quantity indicator of the market shares on the one hand,and the quality indicator of the flow quality on the other hand,using a combination of the two indicators comprehensively reflecting the quantity and quality status of the market.By combining the study of inbound tourism flow and quality with market structure analysis,the Boston matrix was used to divide the China′s inbound tourism market into“bright-star markets”,“cash-cow markets”,“thin-dog markets”and“problem markets”.Taking the inbound tourism of 31 provinces from 2009 to 2018 as the research object and the Q-Q model of China′s inbound tourism market structure was used to analyze the structure and changes of the China′s provincial inbound tourism market,the results show that the“bright-star markets”of China′s inbound tourism are concentrated in the southeast coastal areas with developed economy and superior transportation locations.The development of China′s inbound tourism market is unbalance,and there are fewer high-quality markets and the overall quality of the markets is low.The structure of China′s provincial inbound tourism market is constantly optimizing.
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