Agent-Based Data Curation Practices: Customer Responses to Human versus Algorithmic Data Requesters in Established Business-to-Business Relationships

• 成果类型: Article; Early Access
• 署名作者: Adam, Martin; Mishra, Abhay Nath; Benlian, Alexander
• 署名单位: University of Gottingen; Iowa State University; Technical University of Darmstadt
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047; 1526-5536
• DOI: 10.1287/isre.2023.0478
• 发表日期: 2025-11-18
• 关键词: AI agents; human-AI interaction; algorithm aversion; data enrichment; data error; data reconciliation; data curation practice; field experiment; gain-loss framing; multimethod; PRODUCT RECOMMENDATION AGENTS; mixed-methods research; TRADE-OFF DIFFICULTY; E-commerce; artificial-intelligence; information-technology; decision-support; user acceptance; trust; MODEL

摘要

With the increasing value generated by data curation and the rise of artificial intelligence (AI) agents that converse and act like human agents, vendor companies in established business-to-business (B2B) relationships increasingly delegate data curation tasks to algorithmic data requesters (ADRs) rather than human data requesters (HDRs). Firms employ these data requesters to email existing customers either to collect new information for improved services (data enrichment) or to update outdated information to maintain existing ones (data reconciliation). Despite the growing use of agents in data curation, little is known about how customers respond to these practices-particularly how their responses vary by requester type (HDR versus ADR) and by the nature of the data work practice (enrichment versus reconciliation). Drawing on the effort-accuracy framework and gain-loss message framing, we investigate customer agreement with emailbased data requests from HDRs (versus ADRs) in data enrichment (versus data reconciliation) in a multimethod approach. Evidence from a randomized field experiment with a leading European pharmaceutical company followed by an online experiment reveals that customers have a reduced inclination to agree (versus disagree) with a data request from an HDR (versus ADR) in data enrichment because of their preference for minimizing effort in interactions with the data requester. However, for data reconciliation, customers prefer an HDR (versus ADR) because they have lower concerns about errors that could arise in these interactions. A post hoc analysis reveals that although the findings on customer agreement are supported for data enrichment (i.e., the customer completion rate is higher for ADRs versus HDRs), we find only marginal support for data reconciliation (i.e., the customer completion rate is only marginally higher for HDRs versus ADRs). Qualitative responses from a follow-up online survey and interviews with customers of the pharmaceutical company corroborate and complement the main quantitative findings. Overall, this research expands our understanding of continued customer engagement in data curation practices and has implications for vendor companies seeking to deploy ADRs instead of HDRs in data work and established B2B relationships.