县域尺度下中原城市群C2C淘宝店铺服务质量的空间分异及其影响因素
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摘要
基于C2C店铺服务质量指标,运用多种空间分析方法分析县域尺度下中原城市群C2C淘宝店铺服务质量的空间分异特征及其影响因素。研究发现:①欠佳、低质区数量总占比超过85%,反映出整体服务质量不高的客观现实;服务优质区较少且组团效应不明显,反映出高水平的辐射带动能力不强。与市域尺度相比,县域尺度更能反映局部低值地区的优质增长点。②空间关联的四个类型区呈现出"L-L区范围大,H-H区、L-H区和H-L区范围较小"的特征,整体空间形态呈现出以郑州市为中心"H-H"—"L-H"—"L-L"的环状扩散。与市域尺度相比,低值塌陷区的局部H-H区、H-L区被凸显出来。③空间相互作用强度以四级联系轴为主,与前三个等级联系轴共同塑造"一团一带多核心"的网状格局。与市域尺度下以郑州为核心的放射状格局不同,核心辐射区明显减少且局限在郑州都市区范围。④从影响因素看,信息化水平是影响C2C店铺服质量空间分异的最重要因素;地形与区位条件已成为网商发展的基本要素,而作为制约因素的"障碍"作用愈发弱化;城镇化、工业化与经济发展水平的提高,对C2C店铺服务质量提升起基础支撑作用;专业化经营对知名品牌的孕育和服务质量的提高有重要的推动作用;人口受教育程度是直接因素,影响着C2C店铺的经营理念与开拓创新;宏观政策所营造的外部环境对C2C淘宝店服务质量的提升起着重要引导作用。
Based on the index of comprehensive service quality and using multiple spatial analysis methods, this paper aims to investigate the spatial differences and influencing factors of comprehensive service quality of Taobao online C2 C stores in Central Plains Urban Agglomeration at county level. The results are as follows. Firstly, the number of research units with lower and low quality accounts for more than 85% of the total, which reflects the overall service quality is weak at county level. The number and spatial scope of cities with high service quality are small and it has not formed the group linkage effect, which reflects the capacity of radiation is at low level. Compared with the city level, the county level can better reflect the high-quality growth points of local low-value areas. Secondly, the spatial correlation pattern shows a weak spatial positive correlation and Low-Low areas dominate the distribution type, High-High, High-Low and LowHigh areas occupy a relatively smaller distribution scope. The overall spatial correlation pattern presents a ring diffusion trend of "High-High"-"Low-High"-"Low-Low" with Zhengzhou City as the center. Compared with the city level, local HH, H-L areas located in low-value areas are highlighted. Thirdly, from the spatial interactive strength perspective, the interactive axis is occupied with four-level strength, which shapes the spatial interactive pattern of "one group, one belt and multiple cores" with the first three levels strength axis. Unlike the radial pattern with Zhengzhou as the core at city level, the core radiation area is limited to the Zhengzhou metropolitan area. Finally, based on the evaluation results, the influencing factors analysis is carried out by the combination of qualitative and quantitative methods, we found that the information level is the main key factor, the terrain and location conditions are the basic elements and its constraints become weak, the improvement of urbanization-industrialization and the economic development level provides foundation supporting, professional management plays an important role in promoting the quality of well-known brands and service quality, population education level also plays important roles for conducting online stores and improving products, the external environment created by macro policies plays an important guiding role in improving the service quality of Taobao C2 C stores.
Based on the index of comprehensive service quality and using multiple spatial analysis methods, this paper aims to investigate the spatial differences and influencing factors of comprehensive service quality of Taobao online C2 C stores in Central Plains Urban Agglomeration at county level. The results are as follows. Firstly, the number of research units with lower and low quality accounts for more than 85% of the total, which reflects the overall service quality is weak at county level. The number and spatial scope of cities with high service quality are small and it has not formed the group linkage effect, which reflects the capacity of radiation is at low level. Compared with the city level, the county level can better reflect the high-quality growth points of local low-value areas. Secondly, the spatial correlation pattern shows a weak spatial positive correlation and Low-Low areas dominate the distribution type, High-High, High-Low and LowHigh areas occupy a relatively smaller distribution scope. The overall spatial correlation pattern presents a ring diffusion trend of "High-High"-"Low-High"-"Low-Low" with Zhengzhou City as the center. Compared with the city level, local HH, H-L areas located in low-value areas are highlighted. Thirdly, from the spatial interactive strength perspective, the interactive axis is occupied with four-level strength, which shapes the spatial interactive pattern of "one group, one belt and multiple cores" with the first three levels strength axis. Unlike the radial pattern with Zhengzhou as the core at city level, the core radiation area is limited to the Zhengzhou metropolitan area. Finally, based on the evaluation results, the influencing factors analysis is carried out by the combination of qualitative and quantitative methods, we found that the information level is the main key factor, the terrain and location conditions are the basic elements and its constraints become weak, the improvement of urbanization-industrialization and the economic development level provides foundation supporting, professional management plays an important role in promoting the quality of well-known brands and service quality, population education level also plays important roles for conducting online stores and improving products, the external environment created by macro policies plays an important guiding role in improving the service quality of Taobao C2 C stores.
引文
[1]丁志伟,周凯月,康江江,等.中国中部C2C店铺服务质量的空间分异及其影响因素——以淘宝网5类店铺为例[J].地理研究,2016,35(6):1 074-1 094.
[2]从电商角度看阿里——渠道到生态,阿里开启新零售征程[EB/OL]. http://www.sohu.com/a/193817453_769093,2017-9-22.
[3] Ainsworth J,Ballantine P W. That’s different!How consumers respond to retail website change[J]. Journal of Retailing and Consumer Services,2014,21(5):764-772.
[4] Lim S,Lee B. Loyalty Programs and Dynamic Consumer Preference in Online Markets[J]. Decision Support Systems,2015(78):104-112.
[5] Wang Y J,Minor M S,Wei J. Aesthetics and the online shopping environment:Understanding consumer responses[J]. Jour‐nal of Retailing,2011,87(1):46-58.
[6] Liao T H,Keng C J. Online shopping delivery delay:Finding a psychological recovery strategy by online consumer experiences[J]. Computers in Human Behavior,2013,29(4):1 849-1 861.
[7] Verhagen T,van Dolen W. The influence of online store beliefs on consumer online impulse buying:A model and empirical ap‐plication[J]. Information&Management,2011,48(8):320-327.
[8] Utz S,Kerkhof P,van den Bos J. Consumers rule:How consumer reviews influence perceived trustworthiness of online stores[J]. Electronic Commerce Research and Applications,2012,11(1):49-58.
[9] Salehan M,Kim D J. Predicting the performance of online consumer reviews:A sentiment mining approach to big data analyt‐ics[J]. Decision Support Systems,2016,81:30-40.
[10] Benedicktus R L,Brady M K,Darke P R,et al. Conveying trustworthiness to online consumers:Reactions to consensus,physical store presence,brand familiarity,and generalized sus‐picion[J]. Journal of Retailing,2010,86(4):322-335.
[11] Park S,Nicolau J L. Asymmetric effects of online consumer reviews[J]. Annals of Tourism Research,2015,50:67-83.
[12] Vladlena B,Saridakis G,Tennakoon H,et al. The role of security notices and online consumer behaviour:an empirical study of social networking users[J]. International Journal of HumanComputer Studies,2015,80:36-44.
[13] Yoo J,Kim M. The effects of online product presentation on consumer responses:A mental imagery perspective[J]. Journal of Business Research,2014,67(11):2 464-2 472.
[14] Li T,Meshkova Z. Examining the impact of rich media on consumer willingness to pay in online stores[J]. Electronic Com‐merce Research and Applications,2013,12(6):449-461.
[15] Rong-Da Liang A,Lee C L,Tung W. The role of sunk costs in online consumer decision-making[J]. Electronic Commerce Research and Applications,2014,13(1):56-68.
[16] Koo W,Cho E,Kim Y K. Actual and ideal self-congruity affecting consumers’emotional and behavioral responses toward an online store[J]. Computers in Human Behavior,2014,36:147-153.
[17] Hostler R E,Yoon V Y,Guo Z,et al. Assessing the impact of recommender agents on on-line consumer unplanned purchase behavior[J]. Information&Management,2011,48(8):336-343.
[18] Close A G,Kukar-Kinney M.Beyond buying:Motivations behind consumers’online shopping cart use[J]. Journal of Busi‐ness Research,2010,63(9):986-992.
[19] Cetin?I,Munthiu M C,R?dulescu V. Psychological and social factors that influence online consumer behavior[J]. ProcediaSocial and Behavioral Sciences,2012,62:184-188.
[20] Sam K M,Chatwin C. Online consumer decision-making styles for enhanced understanding of Macau online consumer behavior[J]. Asia Pacific Management Review,2015,20(2):100-107.
[21]路紫,李晓楠,杨丽花,等.基于邻域设施的中国大城市网络店铺的区位取向——以上海、深圳、天津、北京四城市为例[J].地理学报,2011,66(6):813-820.
[22]丁疆辉,吴建民,李冰洁.天猫平台入驻店铺时空格局及变化特征[J].经济地理,2017,37(11):136-144.
[23]徐智邦,王中辉,周亮,等.中国“淘宝村”的空间分布特征及驱动因素分析[J].经济地理,2017,37(1):107-114.
[24]俞金国,王丽华,李娜.电子商铺空间分布特征分析——来自淘宝网的实证[J].经济地理,2010,30(8):1 248-1 253.
[25]千庆兰,陈颖彪,刘素娴,等.淘宝镇的发展特征与形成机制解析——基于广州新塘镇的实证研究[J].地理科学,2017,37(7):1 040-1 048.
[26]王贤文,徐申萌.我国C2C电子商务的地理格局及其演化机制[J].经济地理,2011,31(7):1 064-1 069.
[27]张敏,张翔,申峻霞.网络消费空间的性质与生产——以淘宝网原创女装店为例[J].地理科学,2015,35(8):960-968.
[28]钟海东,张少中,华灵玲,等.中国C2C电子商务卖家空间分布模式[J].经济地理,2014,34(4):92-96.
[29]浩飞龙,关皓明,王士君.中国城市电子商务发展水平空间分布特征及影响因素[J].经济地理,2016,36(2):1-10.
[30]浩飞龙,王彬燕,王士君.东北地区县域电子商务发展水平的空间差异及影响因素[J].地域研究与开发,2016,35(4):16-21.
[31]徐杰,罗震东,何鹤鸣,等.中国县域电子商务发展的空间特征及影响因素研究[J].上海城市规划,2017(2):90-97.
[32]刘晓阳,丁志伟,黄晓东,等.中国电子商务发展水平空间分布特征及其影响因素——基于1 915个县(市)的电子商务发展指数[J].经济地理,2018,38(11):11-21,38.
[33]余金艳,刘卫东,王亮.基于时间距离的C2C电子商务虚拟商圈分析——以位于北京的淘宝网化妆品零售为例[J].地理学报,2013,68(10):1 380-1 388.
[34] Xi G,Zhen F,He J,et al. City networks of online commodity services in China:empirical analysis of Tmall clothing and electronic retailers[J]. Chinese Geographical Science,2018,28(2):231-246.
[35] Zhou C,Jin W,Zhang G. Impact of Shipping Distance on Online Retailers’Sales:A Case Study of Maiyang on Tmall[J].Chinese Geographical Science,2018,28(2):261-273.
[36]邓之宏,郑伟亮,秦军昌. C2C电子商务服务质量评价实证研究——基于中国C2C市场的问卷调查[J].图书情报工作,2012,56(14):141-147.
[37]曲慧梅.电子商务物流服务质量测度研究——以淘宝电子商务平台为例[J].物流技术,2014(17):294-296.
[38]唐纯.基于顾客角度的电子商务服务质量动态演化博弈[J].商业经济研究,2017(22):85-88.
[39]左文明,陈华琼,张镇鹏.基于网络口碑的B2C电子商务服务质量管理[J].管理评论,2018,30(2):94-106.
[40]张海平,周星星,代文.空间插值方法的适用性分析初探[J].地理与地理信息科学,2017,33(6):14-18,105.
[41]徐建华.计量地理学(第二版)[M].北京:高等教育出版社,2014.
[42]刘静玉,杨虎乐,宋琼,等.中原经济区城市间相互作用时空格局演变研究[J].地理科学,2014,34(9):1 060-1 068.
[43]何胜,唐承丽,周国华.长江中游城市群空间相互作用研究[J].经济地理,2014,34(4):46-53.
[2]从电商角度看阿里——渠道到生态,阿里开启新零售征程[EB/OL]. http://www.sohu.com/a/193817453_769093,2017-9-22.
[3] Ainsworth J,Ballantine P W. That’s different!How consumers respond to retail website change[J]. Journal of Retailing and Consumer Services,2014,21(5):764-772.
[4] Lim S,Lee B. Loyalty Programs and Dynamic Consumer Preference in Online Markets[J]. Decision Support Systems,2015(78):104-112.
[5] Wang Y J,Minor M S,Wei J. Aesthetics and the online shopping environment:Understanding consumer responses[J]. Jour‐nal of Retailing,2011,87(1):46-58.
[6] Liao T H,Keng C J. Online shopping delivery delay:Finding a psychological recovery strategy by online consumer experiences[J]. Computers in Human Behavior,2013,29(4):1 849-1 861.
[7] Verhagen T,van Dolen W. The influence of online store beliefs on consumer online impulse buying:A model and empirical ap‐plication[J]. Information&Management,2011,48(8):320-327.
[8] Utz S,Kerkhof P,van den Bos J. Consumers rule:How consumer reviews influence perceived trustworthiness of online stores[J]. Electronic Commerce Research and Applications,2012,11(1):49-58.
[9] Salehan M,Kim D J. Predicting the performance of online consumer reviews:A sentiment mining approach to big data analyt‐ics[J]. Decision Support Systems,2016,81:30-40.
[10] Benedicktus R L,Brady M K,Darke P R,et al. Conveying trustworthiness to online consumers:Reactions to consensus,physical store presence,brand familiarity,and generalized sus‐picion[J]. Journal of Retailing,2010,86(4):322-335.
[11] Park S,Nicolau J L. Asymmetric effects of online consumer reviews[J]. Annals of Tourism Research,2015,50:67-83.
[12] Vladlena B,Saridakis G,Tennakoon H,et al. The role of security notices and online consumer behaviour:an empirical study of social networking users[J]. International Journal of HumanComputer Studies,2015,80:36-44.
[13] Yoo J,Kim M. The effects of online product presentation on consumer responses:A mental imagery perspective[J]. Journal of Business Research,2014,67(11):2 464-2 472.
[14] Li T,Meshkova Z. Examining the impact of rich media on consumer willingness to pay in online stores[J]. Electronic Com‐merce Research and Applications,2013,12(6):449-461.
[15] Rong-Da Liang A,Lee C L,Tung W. The role of sunk costs in online consumer decision-making[J]. Electronic Commerce Research and Applications,2014,13(1):56-68.
[16] Koo W,Cho E,Kim Y K. Actual and ideal self-congruity affecting consumers’emotional and behavioral responses toward an online store[J]. Computers in Human Behavior,2014,36:147-153.
[17] Hostler R E,Yoon V Y,Guo Z,et al. Assessing the impact of recommender agents on on-line consumer unplanned purchase behavior[J]. Information&Management,2011,48(8):336-343.
[18] Close A G,Kukar-Kinney M.Beyond buying:Motivations behind consumers’online shopping cart use[J]. Journal of Busi‐ness Research,2010,63(9):986-992.
[19] Cetin?I,Munthiu M C,R?dulescu V. Psychological and social factors that influence online consumer behavior[J]. ProcediaSocial and Behavioral Sciences,2012,62:184-188.
[20] Sam K M,Chatwin C. Online consumer decision-making styles for enhanced understanding of Macau online consumer behavior[J]. Asia Pacific Management Review,2015,20(2):100-107.
[21]路紫,李晓楠,杨丽花,等.基于邻域设施的中国大城市网络店铺的区位取向——以上海、深圳、天津、北京四城市为例[J].地理学报,2011,66(6):813-820.
[22]丁疆辉,吴建民,李冰洁.天猫平台入驻店铺时空格局及变化特征[J].经济地理,2017,37(11):136-144.
[23]徐智邦,王中辉,周亮,等.中国“淘宝村”的空间分布特征及驱动因素分析[J].经济地理,2017,37(1):107-114.
[24]俞金国,王丽华,李娜.电子商铺空间分布特征分析——来自淘宝网的实证[J].经济地理,2010,30(8):1 248-1 253.
[25]千庆兰,陈颖彪,刘素娴,等.淘宝镇的发展特征与形成机制解析——基于广州新塘镇的实证研究[J].地理科学,2017,37(7):1 040-1 048.
[26]王贤文,徐申萌.我国C2C电子商务的地理格局及其演化机制[J].经济地理,2011,31(7):1 064-1 069.
[27]张敏,张翔,申峻霞.网络消费空间的性质与生产——以淘宝网原创女装店为例[J].地理科学,2015,35(8):960-968.
[28]钟海东,张少中,华灵玲,等.中国C2C电子商务卖家空间分布模式[J].经济地理,2014,34(4):92-96.
[29]浩飞龙,关皓明,王士君.中国城市电子商务发展水平空间分布特征及影响因素[J].经济地理,2016,36(2):1-10.
[30]浩飞龙,王彬燕,王士君.东北地区县域电子商务发展水平的空间差异及影响因素[J].地域研究与开发,2016,35(4):16-21.
[31]徐杰,罗震东,何鹤鸣,等.中国县域电子商务发展的空间特征及影响因素研究[J].上海城市规划,2017(2):90-97.
[32]刘晓阳,丁志伟,黄晓东,等.中国电子商务发展水平空间分布特征及其影响因素——基于1 915个县(市)的电子商务发展指数[J].经济地理,2018,38(11):11-21,38.
[33]余金艳,刘卫东,王亮.基于时间距离的C2C电子商务虚拟商圈分析——以位于北京的淘宝网化妆品零售为例[J].地理学报,2013,68(10):1 380-1 388.
[34] Xi G,Zhen F,He J,et al. City networks of online commodity services in China:empirical analysis of Tmall clothing and electronic retailers[J]. Chinese Geographical Science,2018,28(2):231-246.
[35] Zhou C,Jin W,Zhang G. Impact of Shipping Distance on Online Retailers’Sales:A Case Study of Maiyang on Tmall[J].Chinese Geographical Science,2018,28(2):261-273.
[36]邓之宏,郑伟亮,秦军昌. C2C电子商务服务质量评价实证研究——基于中国C2C市场的问卷调查[J].图书情报工作,2012,56(14):141-147.
[37]曲慧梅.电子商务物流服务质量测度研究——以淘宝电子商务平台为例[J].物流技术,2014(17):294-296.
[38]唐纯.基于顾客角度的电子商务服务质量动态演化博弈[J].商业经济研究,2017(22):85-88.
[39]左文明,陈华琼,张镇鹏.基于网络口碑的B2C电子商务服务质量管理[J].管理评论,2018,30(2):94-106.
[40]张海平,周星星,代文.空间插值方法的适用性分析初探[J].地理与地理信息科学,2017,33(6):14-18,105.
[41]徐建华.计量地理学(第二版)[M].北京:高等教育出版社,2014.
[42]刘静玉,杨虎乐,宋琼,等.中原经济区城市间相互作用时空格局演变研究[J].地理科学,2014,34(9):1 060-1 068.
[43]何胜,唐承丽,周国华.长江中游城市群空间相互作用研究[J].经济地理,2014,34(4):46-53.
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