2026-04-20T17:39:58Z https://datacatalogue.cessda.eu/oai-pmh/v0/oai

d9caa936e45ab186132041858d064a40c74055defaa461b9e63fe1361ea4e3b2 2025-10-01T03:06:25Z language:enopenaire_data

DDI study level documentation for study 10.7802/2836 Replication data and code: Invitation Messages for Business Surveys: A Multi-Armed Bandit Experiment Replication data and code: Invitation Messages for Business Surveys: A Multi-Armed Bandit Experiment Replication data and code: Invitation Messages for Business Surveys: A Multi-Armed Bandit Experiment 10.7802/283610.7802/283610.7802/283610.7802/2836 Gaul, Johannes J. Gaul, Johannes J. Keusch, Florian Keusch, Florian Rostam-Afschar, Davud Rostam-Afschar, Davud Simon, Thomas Simon, Thomas [Collaborative Research Center (SFB/TRR) Project-ID 403041268 – TRR 266 Accounting for Transparency] GESIS Data Archive for the Social SciencesGESIS Datenarchiv für Sozialwissenschaften methodological researchenterprisemethodological researchenterprise This replication package contains the experimental data and code from a study investigating how elements of a survey invitation message targeted to businesses influence their participation in a self-administered web survey. The experiment was conducted in collaboration with the German Business Panel (GBP) during its fifth survey wave, spanning from August 16, 2022, to November 25, 2022. A full factorial design was implemented, varying five key components of the email invitation. Unlike conventional experimental setups with static group assignments, the study employed adaptive randomization, wherein a Bayesian learning algorithm sequentially allocated more observations to invitation messages exhibiting higher survey starting rates. Over the 15-week experimental period, 738,598 invitation messages were distributed to business contacts, of which 176,000 were opened within one week. A total of 7,833 recipients initiated the survey, and 3,733 completed it. The dataset includes detailed records of message distribution, survey engagement metrics, and adaptive randomization adjustments, providing a comprehensive basis for analyzing the effectiveness of invitation design in business survey participation. Keywords: Adaptive Randomization, Reinforcement Learning, Nonresponse, Email Invitation, Web Survey, Firm Survey, Organizational SurveyThis replication package contains the experimental data and code from a study investigating how elements of a survey invitation message targeted to businesses influence their participation in a self-administered web survey. The experiment was conducted in collaboration with the German Business Panel (GBP) during its fifth survey wave, spanning from August 16, 2022, to November 25, 2022. A full factorial design was implemented, varying five key components of the email invitation. Unlike conventional experimental setups with static group assignments, the study employed adaptive randomization, wherein a Bayesian learning algorithm sequentially allocated more observations to invitation messages exhibiting higher survey starting rates. Over the 15-week experimental period, 738,598 invitation messages were distributed to business contacts, of which 176,000 were opened within one week. A total of 7,833 recipients initiated the survey, and 3,733 completed it. The dataset includes detailed records of message distribution, survey engagement metrics, and adaptive randomization adjustments, providing a comprehensive basis for analyzing the effectiveness of invitation design in business survey participation. Keywords: Adaptive Randomization, Reinforcement Learning, Nonresponse, Email Invitation, Web Survey, Firm Survey, Organizational Survey GermanyGermany All legal entities active in Germany as defined by the Federal Statistical Office in the statistical business register Longitudinal (panel study)Longitudinal (panel study) Probability Sample - Simple random SampleProbability Sample - Simple random Sample Web-based experimentExperiment.WebBasedWeb-basiertes ExperimentExperiment.WebBased Free access (without registration) - The research data can be downloaded directly by anyone without further limitations.Freier Zugang (ohne Registrierung) - Die Forschungsdaten können von jedem direkt heruntergeladen werden. http://dbkapps.gesis.org/dbkoai/oai.asp oai:dbk.gesis.org:SDN/10.7802_2836 2025-02-19 ddi:codebook:2_5