[目的/意义] 数据交易是数据要素市场化配置改革的关键和难点，当前我国数据交易面临诸多问题，亟需破局走出困境。引入生态系统的理论视角，有助于从整体协调的角度认清数据交易的发展制约因素，进而提出可行之策。[方法/过程] 采用比较类推法，通过分析数据交易与生态系统的相似性来论证数据交易生态系统的可行性，进而明确该生态系统的运行机制与特点。以此为基点，通过访谈法和网络调研法，发现数据交易现实中还存在数据要素权属复杂、交易双方彼此博弈、第三方机构缺失及监管缺位等制约因素。[结果/结论] 为充分释放数据交易潜力，需为克服这些制约因素制定路径策略，通过长期、系统、动态、可控的治理实现生态系统的动态演进与稳定平衡。
[Purpose/Significance] Data transaction is the key and difficult point of the reform of market-oriented allocation of data elements. Currently, Chinese data transaction is faced with many problems and needs to break through the dilemma. The introduction of ecosystem theory is helpful to recognize the development constraints of data transaction from the perspective of overall coordination, and then put forward feasible measures. [Method/Process] By analogy method, the feasibility of data transaction ecosystem was demonstrated by analyzing the similarity between data transaction and ecosystem, and then the operating mechanism and characteristics of the ecosystem were clarified. Based on this, through the interview method and network research method, it was found that in the reality of data transaction, there were also constraints such as complex ownership of data elements, game between the two sides of the transaction, lack of third-party institutions and absence of supervision. [Result/Conclusion] In order to fully release the potential of data transaction, it is necessary to formulate a path strategy to overcome these constraints, and realize the dynamic evolution and stable balance of the ecosystem through longterm, systematic, dynamic and controllable governance.
[1] 陈书晴, 任昊翔, 陶思佳, 等. 数据要素与多元市场主体融合机制研究[J]. 信息通信技术与政策, 2022(1):2-10.
[2] 罗曼, 田牧. 理想很丰满现实很骨感贵阳大数据交易所这六年[N]. 证券时报, 2021-07-12(A01).
[3] 史乐蒙. 大数据交易场所能否乘势而上?[N]. 期货日报, 2022-08-12(7).
[4] 李勇坚. 我国数据交易机构高质量发展:现实问题、理论逻辑与实现路径[J]. 广东社会科学, 2023(1):47-56.
[5] POTOGLOU D, PATIL S, GIJON C, et al. The value of personal information online:results from three stated preference discrete choice experiments in the UK[C]//21st European conference for information systems. Utrecht:ECIS, 2013:189-200.
[6] 王蒙燕. 我国数据要素统一大市场构建的问题与对策[J]. 西南金融, 2022(7):80-90.
[7] 刘吉超. 我国数据要素市场培育的实践探索:成效、问题与应对建议[J]. 价格理论与实践, 2021(12):18-22.
[8] 任保平, 王思琛. 新发展格局下我国数据要素市场培育的逻辑机理与推进策略[J]. 浙江工商大学学报, 2022(3):85-93.
[9] SPIEKERMANN M. Data marketplaces:trends and monetisation of data goods[J]. Intereconomics, 2019, 54(4):208-216.
[10] SCHOMM F, STAHL F, VOSSEN G. Marketplaces for data:an initial survey[J]. ACM SIGMOD record, 2013, 42(1):15-26.
[11] STAHL F, SCHOMM F, VOSSEN G. The data marketplace survey revisited[C]//BECKER J, BACKHAUS K, GROB H L. ERCIS Working Paper, No. 18. Münster:European Research Center for Information Systems, 2014:1-20.
[12] 王卫, 张梦君, 王晶. 国内外大数据交易平台调研分析[J]. 情报杂志, 2019, 38(2):181-186, 194.
[13] FLORIAN S, GOTTFRIED V. Fair knapsack pricing for data marketplaces[C]//POKORNÝ J, IVANOVIĆ M, THALHEIM B. Advances in databases and information systems. Berlin:Springer, 2016:46-59.
[14] TANG R, WU H, BAO Z, et al. The price is right:models and algorithms for pricing data[C]//DECKER H, LHOTSKÁ L, LINK S. DEXA:Database and expert systems applications. Berlin:Springer, 2013:380-394.
[15] GOLREZAEI N, NAZERZADEH H. Pricing schemes for metropolitan traffic data markets[C]//MARKUS H, HELFERT M, HOLZINGER A, et al. DATA 2014:Proceedings of 3rd international conference on data management technologies and applications. Setubal:SciTePress, 2014:266-271.
[16] REN X, PALMA L, JUBA Z, et al. Datum:managing data purchasing and data placement in a geo-distributed data market[J]. IEEE/ACM transactions on networking, 2018, 26(2):893-905.
[17] 黄倩倩, 王建冬, 陈东, 等. 超大规模数据要素市场体系下数据价格生成机制研究[J]. 电子政务, 2022(2):21-30.
[18] 刘航. 大数据交易法律监管制度研究[D]. 南京:南京航空航天大学, 2020.
[19] 魏婉君. 大数据交易监管的法律问题研究[D]. 兰州:兰州大学, 2020.
[20] HU Y, LI C, HU A, et al. Trading off data resource availability and privacy preservation in multi-layer network transaction[J]. Physical communication, 2021, 46:101317.
[21] ZHAO Y, YU Y, LI Y, et al. Machine learning based privacypreserving fair data trading in big data market[J]. Information sciences, 2019, 478:449-460.
[22] 杜自然, 窦悦, 易成岐, 等. TID-MOP:面向数据交易所场景下的安全管控综合框架[J]. 数据分析与知识发现, 2022, 6(1):13-21.
[23] 中国大百科全书总编委会. 中国大百科全书:第二版[M]. 北京:中国大百科全书出版社, 2009:566.
[24] 储节旺, 李佳轩, 唐亮亮. 元宇宙视域下的知识生态系统探析——要素、机理与展望[J/OL]. 情报科学:1-8[2023-01-05]. http://kns.cnki.net/kcms/detail/22.1264.G2.20221215.2327.007.html.
[25] NARDI B A, O'DAY V L. Information ecologies:using technology with heart[M]. Cambridge:MIT Press, 1999.
[26] 柳瑞禹, 何志明, 范如国. 中部六省科技创新效率与影响因素研究[J]. 资源开发与市场, 2016, 32(3):277-283.
[27] 陈丽君, 李言, 傅衍. 激发人才创新活力的生态系统研究[J]. 治理研究, 2022, 38(4):39-50+125.
[28] 彭正银, 姚双双. 平台生态系统中平台企业与互补企业实现协同合作的路径研究——基于演化博弈分析[J/OL]. 软科学:1-25[2022-09-26]. http://kns.cnki.net/kcms/detail/51.1268.g3.20220725.1928.021.html.
[29] 盛朝迅. 产业生态主导企业培育的国际经验与中国路径[J]. 改革, 2022(10):34-44.
[30] ODUM E P. Fundamentals of ecology[M]. 3rd ed. Philadelphia:W.B. Saunders Co, 1971.
[31] 葛永林, 徐正春. 生态整体论的演进逻辑[J]. 哈尔滨工业大学学报(社会科学版), 2014, 16(1):135-140.
[32] CURRY E. The big data value chain:definitions, concepts, and theoretical approaches[M]//CAVANILLAS J M, CURRY E, WAHLSTER W. New horizons for a data-driven economy:a roadmap for usage and exploitation of big data in Europe. Berlin:Springer, 2016:29-37.
[33] MOORE J. Predators and prey:a new ecology of competition[J]. Harvard business review, 1999, 71:75-86.
[34] 胡艳, 张光辉. 风险投资生态系统的构建及其特征分析[J]. 武汉理工大学学报(信息与管理工程版), 2007(8):98-100, 105.
[35] MOORE J. The death of competition:leadership and strategy in the age of business ecosystems[M]. New York:Wiley Harper Business, 1996.
[36] 深入 理解"坚持 系统 观念"[EB/OL].[2022-12-30]. https://www.mca.gov.cn/article/xw/mtbd/202011/20201100030444.shtml.
[37] 贾天明, 雷良海, 王茂南. 众创空间生态系统:内涵、特点、结构及运行机制[J]. 科技管理研究, 2017, 37(11):8-14.
[38] 郑师章. 普通生态学:原理、方法和应用[M]. 上海:复旦大学出版社, 1994.
[39] 黄丽华, 窦一凡, 郭梦珂, 等. 数据流通市场中数据产品的特性及其交易模式[J]. 大数据, 2022, 8(3):3-14.
[40] 柳峰, 高绍林. 数据要素市场发展的立法范式研究[J]. 信息安全研究, 2021, 7(11):1052-1062.
[41] COASE R. The nature of the firm[J]. Economics, 1937(4):386-405.
