Invitation Messages for Business Surveys: A Multi-Armed Bandit Experiment

Johannes J. Gaul, Florian Keusch, Davud Rostam-Afschar and Thomas Simon

No 1540, GLO Discussion Paper Series from Global Labor Organization (GLO)

Abstract: This study investigates how elements of a survey invitation message targeted to businesses influence their participation in a self-administered web survey. We implement a full factorial experiment varying five key components of the email invitation. Unlike traditional experimental setups with static group composition, however, we employ adaptive randomization in our sequential research design. Specifically, as the experiment progresses, a Bayesian learning algorithm assigns more observations to invitation messages with higher starting rates. Our results indicate that personalizing the message, emphasizing the authority of the sender, and pleading for help increase survey starting rates, while stressing strict privacy policies and changing the location of the survey URL have no response-enhancing effect. The implementation of adaptive randomization is useful for other applications of survey design and methodology.

Keywords: Adaptive Randomization; Reinforcement Learning; Nonresponse; Email Invitation; Web Survey; Firm Survey; Organizational Survey (search for similar items in EconPapers)
JEL-codes: C11 C44 C93 D83 M00 M40 (search for similar items in EconPapers)
Date: 2024
New Economics Papers: this item is included in nep-exp
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https://www.econstor.eu/bitstream/10419/307923/1/GLO-DP-1540.pdf (application/pdf)

Related works:
Working Paper: Invitation messages for business surveys: A multi-armed bandit experiment (2025)
Working Paper: Invitation Messages for Business Surveys: A Multi-Armed Bandit Experiment (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:glodps:1540

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