Optimizing Survey Engagement: Factors Influencing Questionnaire Breakoff and Respondent Segmentation

Abstract

This study examines questionnaire breakoff – respondents’ early survey discontinuation – and its implications for data quality in marketing research. Using telephone interviews with three independent random sub-samples of Hungarian adults (n = 1040; 1028; 988), we applied K-means cluster analysis to segment respondents based on prior breakoff experiences and atti-tudes toward questionnaire characteristics. Chi-square, ANOVA, and Friedman tests identify the key drivers of discontinuation. Findings show that question-naire length, perceived topic irrelevance, and poorly structured items signif-icantly increase breakoff risk. Based on the results, three distinct respondent segments emerged – Discerning Evaluators, Experience Seekers, and Noncha-lant Responders – each exhibiting different engagement preferences and tol-erance thresholds. Trust in research, age, and educational attainment further shape breakoff propensities across segments. Practically, the results support segment-specific survey design strategies that optimize length, structure, and topic salience while incorporating trust-building elements. Conceptually, the study extends leverage–saliency theory by introducing a segmentation frame-work that accounts for heterogeneity in survey discontinuation risk and is adaptable to multinational research settings.