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

Author

Listed:
  • Gaul, Johannes J.

    (ZEW)

  • Keusch, Florian

    (University of Mannheim)

  • Rostam-Afschar, Davud

    (University of Mannheim)

  • Simon, Thomas

    (University of Mannheim)

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.

Suggested Citation

  • Gaul, Johannes J. & Keusch, Florian & Rostam-Afschar, Davud & Simon, Thomas, 2024. "Invitation Messages for Business Surveys: A Multi-Armed Bandit Experiment," IZA Discussion Papers 17534, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp17534
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    References listed on IDEAS

    as
    1. Eric M. Schwartz & Eric T. Bradlow & Peter S. Fader, 2017. "Customer Acquisition via Display Advertising Using Multi-Armed Bandit Experiments," Marketing Science, INFORMS, vol. 36(4), pages 500-522, July.
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    4. Maximilian Kasy & Anja Sautmann, 2021. "Adaptive Treatment Assignment in Experiments for Policy Choice," Econometrica, Econometric Society, vol. 89(1), pages 113-132, January.
    5. Corinna König & Joseph W. Sakshaug, 2023. "Nonresponse trends in establishment panel surveys: findings from the 2001–2017 IAB establishment panel," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 57(1), pages 1-17, December.
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    adaptive randomization; reinforcement learning; nonresponse; email invitation; web survey; firm survey; organizational survey;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • M00 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - General - - - General
    • M40 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - General

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