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数据分析与知识发现  2017, Vol. 1 Issue (6): 72-82     https://doi.org/10.11925/infotech.2096-3467.2017.06.08
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于二模复杂网络的共享经济平台用户交互行为研究*
陈远, 刘福珍, 吴江()
武汉大学信息管理学院 武汉 430072
武汉大学电子商务研究与发展中心 武汉 430072
Studying Users’ Interaction Behaviors of Sharing Economic Platform with 2-Mode Complex Network Analysis
Chen Yuan, Liu Fuzhen, Wu Jiang()
School of Information Management, Wuhan University, Wuhan 430072, China
The Center of E-commerce Research and Development of Wuhan University, Wuhan 430072, China
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摘要 

目的】在“共同拥有而不占有”的共享经济理念下, 探索如何优化供需方的服务。【方法】爬取“小猪短租”的用户数据, 利用二模网络分析工具Ucinet探究用户位置演变, 结合一模网络中用户复杂关系, 构建固定效应模型分析个体中心度对相连用户交易行为的影响程度。【结果】度数中心度会正相关显著影响相连用户行为, 而房东中介中心度显著影响房客消费行为, 核心房客中介中心度显著影响房东订单供应行为。【局限】主要针对互动性强的用户采取滚雪球抽样, 无法完全体现整个关系网络的特点。【结论】为了促进小猪短租因交易行为构建的社会网络活跃, 应鼓励用户充当消费者, 并主动参与其中作为服务提供商。

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陈远
刘福珍
吴江
关键词 二模网络共享经济中心度用户行为短租平台    
Abstract

[Objective] This paper explores the service optimization methods based on the concept of “shared ownership without possession” of the sharing economy. [Methods] First, we retrieved data from the website of “xiaozhu short-term rentals”. Then, we used the 2-mode network tool “Ucinet” to analyze the changing of users’ locations. Third, we studied the impacts of individual centrality on users’ behaviors through the fixed effect model and the relationship among the one-mode network users. [Results] We found that degree centrality positively influenced users’ behaviors. The betweenness centrality of the host agents was negatively correlated with the consumers’ behaviors, while the betweenness centrality of the key tenant agents positively affected the hosts’ offering behaviors. [Limitations] We focused on active users, and did not investigate the characteristics of the entire network. [Conclusions] Business social network systems like xiaozhu.com should encourage their users to become both consumers and service providers, which will promote the development of Sharing Economy.

Key words2-Mode Network    Sharing Economy    Centrality    User Behavior    Short Rental Platform
收稿日期: 2017-04-10      出版日期: 2017-08-25
ZTFLH:  TP393  
基金资助:*本文系国家自然科学基金项目“创新2.0超网络中知识流动和群集交互的协同研究”(项目编号: 71373194)的研究成果之一
引用本文:   
陈远, 刘福珍, 吴江. 基于二模复杂网络的共享经济平台用户交互行为研究*[J]. 数据分析与知识发现, 2017, 1(6): 72-82.
Chen Yuan,Liu Fuzhen,Wu Jiang. Studying Users’ Interaction Behaviors of Sharing Economic Platform with 2-Mode Complex Network Analysis. Data Analysis and Knowledge Discovery, 2017, 1(6): 72-82.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2017.06.08      或      http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2017/V1/I6/72
时期 节点数 新增
节点数
边数 新增
订单数
关系 新增
关系数
8月 1 958 3 266 2 125
9月 2 204 246 3 722 456 2 400 275
10月 2 367 163 4 018 296 2 579 179
  2016年8月-10月“小猪短租”数据汇总
  2016年8月-10月“小猪短租”用户关系结构图
(注: 圆圈代表房客, 矩形代表房东。)
Uname 8月 9月 10月 变化量
Outdegree Degree Outdegree Degree Outdegree Degree Rate1 Rate2
水果女王 1 5 1 5 1 5 0 0
虹狐狸 2 237 2 244 2 245 7 1
星期日 0 202 0 336 0 405 134 69
一人依梦 5 358 5 395 5 443 37 48
NANA_ 2 442 2 486 2 509 44 23
曾国藩 0 470 0 489 0 505 19 16
蒋小姐 0 15 0 16 0 16 1 0
杨洋洋
YAYANGNG
5 55 5 55 5 55 0 0
DP 4 122 4 226 4 279 104 53
柏林 0 313 0 347 0 365 34 18
  一模网络中的用户出入度(部分)
  二模网络中房东8月-10月的度数中心度分布
  二模网络中房东8月-10月的接近中心度
  二模网络中房东8月-10月的中介中心度
Uname Degree Closeness Betweenness
8月 9月 10月 8月 9月 10月 8月 9月 10月
玫瑰1992 0.05 0.05 0.05 0.438 0.436 0.436 0 0 0
杨洋洋YAYANGNG 0.2 0.2 0.2 0.573 0.59 0.594 0.043 0.046 0.047
芒果公寓 0.05 0.05 0.05 0.5 0.493 0.491 0 0 0
哆啦之家 0.1 0.1 0.1 0.519 0.518 0.517 0.004 0.003 0.003
虹狐狸 0.1 0.1 0.1 0.534 0.533 0.532 0.006 0.005 0.005
DP 0.1 0.1 0.1 0.54 0.539 0.539 0.006 0.005 0.005
王GARY 0.05 0.05 0.05 0.497 0.515 0.525 0 0 0
一人依梦 0.2 0.2 0.2 0.575 0.582 0.583 0.035 0.031 0.028
NANA_ 0.1 0.1 0.1 0.53 0.519 0.515 0.003 0.002 0.002
  二模网络中房客的中心度(部分)
用户类型 变量 平均值 标准差 最小值 最大值
房东 Degree 0.0545167 0.0394721 0.002 0.129
Closeness 35.42335 124.5371 0.183 601.25
Betweenness 0.1049167 0.0828587 0 0.258
Order 182.7667 151.7818 4 504
房客 Degree 0.0544869 0.0178262 0.05 0.2
Closeness 2.656368 29.55877 0.307 678.857
Betweenness 0.0005464 0.0032874 0 0.082
Consumption 1.688142 1.738549 1 26
  用户网络参数的描述性统计
房东 房客
Degree Closeness Betweenness Order Degree Closeness Betweenness Consumption
房东 Degree 1
Closeness -0.3742 1
Betweenness 0.995 -0.363 1
Order 0.9797 -0.3329 0.9683 1
房客 Degree 1
Closeness -0.018 1
Betweenness 0.7875 -0.0118 1
Consumption 0.3602 -0.0275 0.2362 1
  用户网络参数的相关性统计
Order 组内R2=0.5542, 组间R2=0.9401, 总体R2= 0.9263
路径系数 标准误差 t P>|t| [95% Conf. Interval]
Degree 10212.23 2677.958 3.81 0.001 4786.172 15638.29
Closeness 0.0192705 0.2978323 0.06 0.949 -0.5841951 0.6227362
Betweenness -3580.766 1395.009 -2.57 0.014 -6407.323 -754.2087
_cons 1.029319 32.41452 0.03 0.975 -64.64873 66.70737
  Model1回归分析
Consumption 所有成员: 组内R2=0.1700, 组间R2=0.1283, 总体R2= 0.1314
核心成员: 组内R2= 0.9843, 组间R2= 0.3478, 总体R2=0.3606
路径系数 标准误差 t P>|t| [95% Conf. Interval]
Degree 33.17095 1.408312 23.55 0.000 30.40991 35.932
Degree1 32.26913 0.3866577 83.46 0.000 31.50837 33.02989
Closeness 0.0000124 0.0012254 0.01 0.992 -0.00239 0.0024149
Closeness1 -3.88E-07 0.0001621 0 0.998 -0.0003193 0.0003186
Betweenness -8.587634 7.20836 -1.19 0.234 -22.71988 5.544615
Betweenness1 23.7467 2.566337 9.25 0.000 18.69737 28.79603
_cons -0.1145803 0.0744668 -1.54 0.124 -0.2605751 0.0314145
_cons1 -0.4499313 0.0216332 -20.8 0.000 -0.4924951 -0.4073675
  Model2 回归分析
[1] Belk R.You are What You Can Access: Sharing and Collaborative Consumption Online[J]. Journal of Business Research, 2014, 67(8): 1595-1600.
doi: 10.1016/j.jbusres.2013.10.001
[2] Nunes M, Correia J.Improving Trust Using Online Credibility Sources and Social Network Quality in P2P Marketplaces[C]//Proceedings of the 8th Iberian Conference on Information Systems and Technologies. IEEE Computer Society, 2013.
[3] Bucher E, Fieseler C, Lutz C.What’s Mine is Yours (for a Nominal Fee) - Exploring the Spectrum of Utilitarian to Altruistic Motives for Internet-Mediated Sharing[J]. Computers in Human Behavior, 2016, 62: 316-326.
doi: 10.1016/j.chb.2016.04.002
[4] Matzler K, Veider V, Kathan W.Adapting to the Sharing Economy[J]. MIT Sloan Management Review, 2015, 56(2): 71-77.
[5] 李晓雪, 赵亮. 浅析共享经济视角下全域旅游的发展趋势[J]. 当代经济, 2016(31): 17-19.
doi: 10.3969/j.issn.1007-9378.2016.31.005
[5] (Li Xiaoxue, Zhao Liang.Study on the Development Trend of Global Tourism from the Perspective of Sharing Economy[J]. Contemporary Economics, 2016(31): 17-19.)
doi: 10.3969/j.issn.1007-9378.2016.31.005
[6] 谢丹丹. 小猪短租:回归共享经济的原点[J]. 中外管理, 2015 (12): 96-97.
[6] (Xie Dandan.Xiaozhu: Return to the Origin of Sharing Economy[J]. Sino Foreign Management, 2015(12): 96-97.)
[7] 何琳. 在线短租企业商业模式分析——以小猪短租为例[J]. 现代商业, 2016(9): 44-45.
doi: 10.3969/j.issn.1673-5889.2016.09.023
[7] (He Lin.An Analysis of the Business Model of Online Short-Rental Enterprises — Taking Xiaozhu as an Example[J]. Modern Business, 2016(9): 44-45.)
doi: 10.3969/j.issn.1673-5889.2016.09.023
[8] Batagelj V.STANLEY WASSERMAN AND KATHERINE FAUST. Social Network Analysis: Methods and Applications[J]. Psychometrika, 1998, 63(1): 103-104.
[9] Newman M E J. Detecting Community Structure in Networks[J]. European Physical Journal B, 2004, 38(2): 321-330.
doi: 10.1140/epjb/e2004-00124-y
[10] Cheng M.Sharing Economy: A Review and Agenda for Future Research[J]. International Journal of Hospitality Management, 2016, 57: 60-70.
doi: 10.1016/j.ijhm.2016.06.003
[11] Ert E, Fleischer A, Magen N.Trust and Reputation in the Sharing Economy: The Role of Personal Photos in Airbnb[J]. Tourism Management, 2016, 55: 62-73.
doi: 10.1016/j.tourman.2016.01.013
[12] Edelman B G, Luca M.Digital Discrimination: The Case of Airbnb.com[OL]. Harvard Business School NOM Unit Working Paper No. 14-054.DOI: 10.2139/ssrn.2377353.
doi: 10.2139/ssrn.2377353
[13] Karlsson L, Kemperman A, Dolnicar S.May I Sleep in Your Bed? Getting Permission to Book[J]. Annals of Tourism Research, 2017, 62: 1-12.
doi: 10.1016/j.annals.2016.10.002
[14] 谢雪梅, 石娇娇. 共享经济下消费者信任形成机制的实证研究[J]. 技术经济, 2016, 35(10): 122-127.
doi: 10.3969/j.issn.1002-980X.2016.10.017
[14] (Xie Xuemei, Shi Jiaojiao.Empirical Study on the Formation Mechanism of Consumer Trust in the Sharing Economy[J]. Technology Economics, 2016, 35(10): 122-127.)
doi: 10.3969/j.issn.1002-980X.2016.10.017
[15] Wu J, Ma P, Zeng M.The Role of Service-Provider’s Attributes in Sharing Economy: A Data-driven Study from the Perspective of Trust[C]//Proceedings of the 15th Wuhan International Conference on E-Business, 2016: 67-77.
[16] Putnik G, Costa E, Alves C, et al.Analysing the Correlation Between Social Network Analysis Measures and Performance of Students in Social Network-Based Engineering Education[J]. International Journal of Technology and Design Education, 2016, 26(3): 413-437.
doi: 10.1007/s10798-015-9318-z
[17] Lopez M D R, Corrales M E V, Valencia S D R. Human Resource Management and Organizational Behavior[C] //Proceedings of the AHFE 2016 International Conference on Human Factors, 2017: 1101-1106.
[18] Jacobs W, Goodson P, Barry A E, et al.Adolescent Social Networks and Alcohol Use: Variability by Gender and Type[J]. Substance Use & Misuse, 2017, 52(4): 477-487.
doi: 10.1080/10826084.2016.1245333 pmid: 28010159
[19] Sibbald S L, Wathen C N, Kothari A, et al.Knowledge Flow and Exchange in Interdisciplinary Primary Health Care Teams (PHCTs): An Exploratory Study[J]. Journal of the Medical Library Association, 2013, 101(2): 128-137.
doi: 10.3163/1536-5050.101.2.008 pmid: 23646028
[20] Zhou Y, Guo C C, Zhang Q L, et al.Free Rider Behavior is Determined by Innate Factor or Acquired Factor?[C]// Proceedings of the 2014 International Conference on Management Science & Engineering. IEEE Computer Society, 2014: 515-522.
[21] Chang W L, Cheng B L, Hao C T.Exploring the Drifting Behavior on Different Social Media[C]//Proceedings of the 2014 Iiai 3rd International Conference on Advanced Applied Informatics (Iiai-Aai 2014). IEEE Computer Society, 2014: 535-536.
[22] 张玥, 朱庆华. 学术博客交流网络的核心—边缘结构分析实证研究[J]. 图书情报工作, 2009, 53(12): 25-29.
[22] (Zhang Yue, Zhu Qinghua.An Empirical Study on the Core— Periphery Structure Analysis of Academic Blog Communication Network[J]. Library and Information Service, 2009, 53(12): 25-29.)
[23] 张静. 基于复杂网络的微博用户群体行为研究[D]. 北京: 北京邮电大学, 2015.
[23] (Zhang Jing.Research of Micro-blog User Group Behavior Based on Complex Network [D]. Beijing: Beijing University of Posts and Telecommunications, 2015.)
[24] Gamble J, Chintakunta H, Wilkerson A, et al.Node Dominance: Revealing Community and Core-Periphery Structure in Social Networks[J]. IEEE Transactions on Signal and Information Processing over Networks, 2016, 2(2): 186-199.
doi: 10.1109/TSIPN.2016.2527923
[25] Goodman L A.Snowball Sampling[J]. The Annals of Mathematical Statistics, 1961, 32(1): 148-170.
[26] Borgatti S P, Mehra A, Brass D J, et al.Network Analysis in the Social Sciences[J]. Science, 2009, 323(5916): 892-895.
[27] Weng C S.Identifying the Core/Periphery Technological Positions from Affiliation Networks: The Network Analysis of 2-Mode[C]//Proceedings of 2011 Picmet 11: Technology Management in the Energy-Smart World. IEEE Computer Society, 2011.
[28] Johnson J D.Ucinet: A Software Tool for Network Analysis[J]. Communication Education, 1987, 36(1): 92-94.
doi: 10.1080/03634528709378647
[29] Freeman L C. Centrality in Social Networks Conceptual Clarification[J]. Social Networks, 1978-1979, 1(3): 215-239.
[30] Faust K.Centrality in Affiliation Networks[J]. Social Networks, 1997, 19(2): 157-191.
[31] Kramer W.A Hausman Test with Trending Data[J]. Economics Letters, 1985, 19(4): 323-325.
doi: 10.1016/0165-1765(85)90228-9
[32] Signh J.Estimation of Effects in a Fixed Effect Model[J]. The Annals of Mathematical Statistics, 1969, 40(2): 720-727.
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