In our recently accepted paper in Information Systems Research (VHB: A+, FT50) , we investigate how customers respond to data requests from human versus algorithmic data requesters in established B2B relationships.

With the increasing value generated through data curation and the rise of AI agents that act as human agents, vendor companies in established B2B relationships increasingly delegate data curation tasks to algorithmic data requesters (ADRs) instead of human data requesters (HDRs). Firms use these data requesters to ask current customers to provide new data for new or better customized services (i.e., data enrichment) or to update old data for maintaining existing ones (i.e., data reconciliation). 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 (vs. disagree) with a data request from an HDR (vs. ADR) in data enrichment due to their preference for minimizing effort in interactions with the data requester. However, for data reconciliation, customers prefer an HDR (vs. ADR) because they have lower concerns about errors that could arise in these interactions.

The paper's findings are important because they show that the specific data curation practice needs to align with the data requester type to achieve favorable customer responses, with clear implications for vendor companies seeking to deploy ADRs rather than HDRs in data work and established B2B relationships.

Preprint to our forthcoming open-access article:

ResearchGate