Designing A Web-Based Questionnaire with Incentive Verification and Behavioural Screening Mechanisms to Improve Survey Data Quality

Abstract

Web-based survey platforms such as Google Forms and SurveyMonkey have become widely adopted in academic and applied research due to their efficiency, accessibility, and low operational cost. However, open online survey environments introduce challenges related to respondent inattentiveness, satisficing behaviours, and data authenticity, which can compromise the reliability and validity of collected data. To address these issues, this study aims to design, develop, and evaluate a web-based questionnaire framework that integrates intervention techniques to enhance response quality. Specifically, the study identifies three practical intervention mechanisms—straightlining detection, consistency or logic checks, and attention check questions—and embeds them within an incentive-based survey system that securely collects respondent banking details for token distribution. The methodology comprises four phases: identification of intervention techniques through literature review; system design and proof of concept; data collection using the developed platform; and usability evaluation conducted with 11 survey instrument developers. Usability was assessed across five dimensions: interface usability, navigation, clarity, satisfaction, and overall experience. Results indicated strong usability performance, with average scores exceeding 80% across all dimensions. Clarity of instructions achieved the highest score (87%), while efficiency scored 80%, suggesting minor areas for optimization. The findings demonstrate that embedded intervention techniques do not detract from user experience while supporting attentive participation. Future work should focus on validating the effectiveness of these intervention techniques in detecting low-quality responses across diverse respondent populations and survey contexts, as well as exploring automation of real-time response filtering to further enhance data integrity.