基于非径向SBM模型的文化服务业上市公司运营效率研究
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摘要
文化服务业上市公司的运营效率影响着我国文化行业的发展和整个国民经济的健康发展。本文运用SBM模型对我国32家文化服务业上市公司2008-2017年年报数据进行分析,结果发现:文化服务业上市公司中约28%的企业相对效率最优,但整体运营效率仍亟待提高;32家上市公司中平均每年有7家保持DEA有效,其中丽江旅游在2008-2017年间一直保持DEA最优,多数企业对资源价值挖掘不足,效率水平不高;从松弛值指标来看,部分文化服务业上市公司过于追求速度与规模,存在不同程度的投入冗余和产出不足。根据实证分析结果,提出以下政策建议:强化核心竞争力建设;补齐企业创新短板;加快推进多渠道融资;促进资源要素合理流动;构建全要素、全产业链体系。
Operating efficiency of listed companies in cultural service industry influences the development of China's cultural industry and the healthy development of the whole national economy. This paper analyzes the annual report data of 32 listed companies in cultural service industry in China from 2008 to 2017 based on SBM model. The results are as follows: about28% of the listed companies in cultural service industry have the best relative efficiency, but the overall operation efficiency still needs to be improved; on average, 7 out of 32 listed companies keep DEA effective every year, in which,Lijiang tourism has always maintained the best DEA from 2008 to 2017, and most of the enterprises are insufficient to excavate the resources value and the efficiency level is not high; the slack value indexes show that some listed companies in cultural service industry pursue speed and scale too much, and have varying degrees of input redundancy and output insufficiency. According to the results of the empirical analysis, the following policy suggestions are put forward:strengthening the construction of core competitiveness; complementing the short board of enterprise innovation; accelerating the promotion of multi-channel financing; promoting the rational flow of resource elements; and constructing a whole-factor and whole-industry chain system.
Operating efficiency of listed companies in cultural service industry influences the development of China's cultural industry and the healthy development of the whole national economy. This paper analyzes the annual report data of 32 listed companies in cultural service industry in China from 2008 to 2017 based on SBM model. The results are as follows: about28% of the listed companies in cultural service industry have the best relative efficiency, but the overall operation efficiency still needs to be improved; on average, 7 out of 32 listed companies keep DEA effective every year, in which,Lijiang tourism has always maintained the best DEA from 2008 to 2017, and most of the enterprises are insufficient to excavate the resources value and the efficiency level is not high; the slack value indexes show that some listed companies in cultural service industry pursue speed and scale too much, and have varying degrees of input redundancy and output insufficiency. According to the results of the empirical analysis, the following policy suggestions are put forward:strengthening the construction of core competitiveness; complementing the short board of enterprise innovation; accelerating the promotion of multi-channel financing; promoting the rational flow of resource elements; and constructing a whole-factor and whole-industry chain system.
引文
[1]刘园香.年终盘点2017年文化产业资本市场全景图[EB/OL].http://www.ccobn.cn/news/sd/10637.html,2017-12-27.
[2]Shi-lian Shan. Chinese cultural policy and the culturalindustries[J].City, Culture and Society, 2014,5(3).
[3]Li Shu-sheng. CultureIndustryDevelopment and RegionalEconomy-Case Study of Tianjin[J].Physics Procedia,2012,25.
[4]王家庭,张容.基于三阶段DEA模型的中国31省市文化产业效率研究[J].中国软科学,2009,(9).
[5]蒋萍,王勇.全口径中国文化产业投入产出效率研究———基于三阶段DEA模型和超效率DEA模型的分析[J].数量经济技术经济研究,2011,(12).
[6]郭淑芬,裴耀琳.中国内地31省市文化产业创新绩效区域比较———基于过程视角[J].科技进步与对策,2016,(4).
[7]肖卫国,刘杰.文化产业资源配置绩效评价研究———以中部地区为例[J].当代经济研究,2014,(3).
[8]陈敦亮.考虑R&D投入的中国文化产业投入产出效率测度及评价[J].湖北社会科学,2014,(12).
[9]徐文燕,张玉兰.基于DEA的文化产业投入与产出效率趋势实证研究———以江苏2004-2010年文化产业投入产出数据为例[J].南京财经大学学报,2013,(5).
[10]王学军.基于三阶段DEA模型的甘肃省区域文化产业效率研究[J].统计与信息论坛,2015,(7).
[11]方忠,张华荣.基于Malmquist指数的福建文化创意产业效率区域差异分析[J].亚太经济,2014,(3).
[12]郭淑芬,郝言慧,王艳芬.文化产业上市公司绩效评价———基于超效率DEA和Malmquist指数[J].经济问题,2014,(2).
[13]周宏,建蕾,李国平,等.不同市场竞争程度与相对绩效评价———基于2004-2012年沪深上市公司的实证检验[J].会计研究,2014,(5).
[14]朱尔茜.文化产业上市公司经营绩效评价———基于81家公司财务数据的实证分析[J].技术经济与管理研究,2017,(1).
[15]Wen-Min Lu, Qian Long Kweh, Dong-Sing He, Jui-Min Shih. Performance analysis of the cultural and creativeindustry:a network-based approach[J].Naval Research Logistics(NRL),2017,64(8).
[16]赵琼,姜惠宸.文化产业上市公司效率评价及影响因素分析———基于DEA模型的分析框架[J].经济问题,2014,(9).
[17]雷原,赵倩,朱贻宁.我国文化创意产业效率分析———基于68家上市公司的实证研究[J].当代经济科学,2015,(2).
[18]吴鸣然,马骏.中国区域生态效率测度及其影响因素分析———基于DEA-Tobit两步法[J].技术经济,2016,(3).
[19]Kaoru Tone. A slacks-based measure of efficiency indata envelopment analysis[J].European Journal of OperationalResearch,2001,130(3).
[20]Hirofumi Fukuyama, William L. Weber. A directionalslacks-based measure of technical inefficiency[J].Socio-EconomicPlanning Sciences, 2008, 43(4).
[21]Xueping Tao, Ping Wang, Bangzhu Zhu. Provincialgreen economic efficiency of China:A non-separable input–output SBM approach[J].Applied Energy, 2016,171.
[22]H. Ebrahimzadeh Shermeh,S.E. Najafi,M.H. Alavidoost.A novel fuzzy network SBM model fordata envelopment analysis:A case study in Iran regional power companies[J].Energy,2016,112.
[23]许立新,杨淼.我国文化产业上市公司经营效率的实证研究———基于DEA-Tobit模型的两阶段分析[J].电子科技大学学报(社科版),2014,(1).
[2]Shi-lian Shan. Chinese cultural policy and the culturalindustries[J].City, Culture and Society, 2014,5(3).
[3]Li Shu-sheng. CultureIndustryDevelopment and RegionalEconomy-Case Study of Tianjin[J].Physics Procedia,2012,25.
[4]王家庭,张容.基于三阶段DEA模型的中国31省市文化产业效率研究[J].中国软科学,2009,(9).
[5]蒋萍,王勇.全口径中国文化产业投入产出效率研究———基于三阶段DEA模型和超效率DEA模型的分析[J].数量经济技术经济研究,2011,(12).
[6]郭淑芬,裴耀琳.中国内地31省市文化产业创新绩效区域比较———基于过程视角[J].科技进步与对策,2016,(4).
[7]肖卫国,刘杰.文化产业资源配置绩效评价研究———以中部地区为例[J].当代经济研究,2014,(3).
[8]陈敦亮.考虑R&D投入的中国文化产业投入产出效率测度及评价[J].湖北社会科学,2014,(12).
[9]徐文燕,张玉兰.基于DEA的文化产业投入与产出效率趋势实证研究———以江苏2004-2010年文化产业投入产出数据为例[J].南京财经大学学报,2013,(5).
[10]王学军.基于三阶段DEA模型的甘肃省区域文化产业效率研究[J].统计与信息论坛,2015,(7).
[11]方忠,张华荣.基于Malmquist指数的福建文化创意产业效率区域差异分析[J].亚太经济,2014,(3).
[12]郭淑芬,郝言慧,王艳芬.文化产业上市公司绩效评价———基于超效率DEA和Malmquist指数[J].经济问题,2014,(2).
[13]周宏,建蕾,李国平,等.不同市场竞争程度与相对绩效评价———基于2004-2012年沪深上市公司的实证检验[J].会计研究,2014,(5).
[14]朱尔茜.文化产业上市公司经营绩效评价———基于81家公司财务数据的实证分析[J].技术经济与管理研究,2017,(1).
[15]Wen-Min Lu, Qian Long Kweh, Dong-Sing He, Jui-Min Shih. Performance analysis of the cultural and creativeindustry:a network-based approach[J].Naval Research Logistics(NRL),2017,64(8).
[16]赵琼,姜惠宸.文化产业上市公司效率评价及影响因素分析———基于DEA模型的分析框架[J].经济问题,2014,(9).
[17]雷原,赵倩,朱贻宁.我国文化创意产业效率分析———基于68家上市公司的实证研究[J].当代经济科学,2015,(2).
[18]吴鸣然,马骏.中国区域生态效率测度及其影响因素分析———基于DEA-Tobit两步法[J].技术经济,2016,(3).
[19]Kaoru Tone. A slacks-based measure of efficiency indata envelopment analysis[J].European Journal of OperationalResearch,2001,130(3).
[20]Hirofumi Fukuyama, William L. Weber. A directionalslacks-based measure of technical inefficiency[J].Socio-EconomicPlanning Sciences, 2008, 43(4).
[21]Xueping Tao, Ping Wang, Bangzhu Zhu. Provincialgreen economic efficiency of China:A non-separable input–output SBM approach[J].Applied Energy, 2016,171.
[22]H. Ebrahimzadeh Shermeh,S.E. Najafi,M.H. Alavidoost.A novel fuzzy network SBM model fordata envelopment analysis:A case study in Iran regional power companies[J].Energy,2016,112.
[23]许立新,杨淼.我国文化产业上市公司经营效率的实证研究———基于DEA-Tobit模型的两阶段分析[J].电子科技大学学报(社科版),2014,(1).