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  • 标题:Imperfect Data, Art Markets and Internet Research
  • 本地全文:下载
  • 作者:Hans J. Van Miegroet ; Kaylee P. Alexander ; Fiene Leunissen
  • 期刊名称:Arts
  • 电子版ISSN:2076-0752
  • 出版年度:2019
  • 卷号:8
  • 期号:3
  • 页码:1-13
  • DOI:10.3390/arts8030076
  • 出版社:MDPI Publishing
  • 摘要:The sheer volume of data generated on the Internet has reached unprecedented numerical heights and has enabled new data-driven methodologies to study art and its markets. Yet, this type of data-driven research has also generated several unexpected methodological constraints for art markets researchers, particularly due to informational asymmetry. This observation is related to how various players on the Internet make data available, as well as summarize, transmit, gather, and access those data globally. It is not our ambition to present another historiography of art markets research, past and present. Rather, and in keeping with the theme of this special issue, we would like to focus on a few key constraints related to data-driven, contemporary art markets research, the Internet, and the structural recurrence of imperfect data. This contribution focuses on four areas of Internet research and its methods that are particularly problematic for researchers today, namely (1) auctions and online auctions; (2) dealers and galleries; (3) art indices; and (4) art fairs.
  • 关键词:art markets; informational asymmetry; data constraints; online auctions; art indices; Internet galleries; art fairs art markets ; informational asymmetry ; data constraints ; online auctions ; art indices ; Internet galleries ; art fairs
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