Response burden in business surveys has long been a concern for National Statistical Institutes (NSIs) for three types of reasons: political reasons, because response burden is part of the total administrative burden governments impose on businesses; methodological reasons, because an excessive response burden may reduce data quality and increase data-collection costs; and strategic reasons, because it affects relations between the NSIs and the business community. This article investigates NSI practices concerning business response burden measurement and reduction actions based on a survey of 41 NSIs from 39 countries. Most NSIs monitor at least some burden aspects and have implemented some actions to reduce burden, but large differences exist between NSIs’ methodologies for burden measurement and actions taken to reduce burden. Future research should find ways to deal with methodological differences in burden conceptualization, operationalization, and measurement, and provide insights into the effectiveness and efficiency of burden-reduction actions.
Increasing reluctance of businesses to participate in surveys often leads to declining or low response rates, poor data quality and burden complaints, and suggests that a driving force, that is, the motivation for participation and accurate and timely response, is insufficient or lacking. Inspiration for ways to remedy this situation has already been sought in the psychological theory of self-determination; previous research has favored enhancement of intrinsic motivation compared to extrinsic motivation. Traditionally however, enhancing extrinsic motivation has been pervasive in business surveys. We therefore review this theory in the context of business surveys using empirical data from the Netherlands and Slovenia, and suggest that extrinsic motivation calls for at least as much attention as intrinsic motivation, that other sources of motivation may be relevant besides those stemming from the three fundamental psychological needs (competence, autonomy and relatedness), and that other approaches may have the potential to better explain some aspects of motivation in business surveys (e.g., implicit motives). We conclude with suggestions that survey organizations can consider when attempting to improve business survey response behavior.
Collecting data from businesses faces ever-larger challenges, some of them calling for an overhaul of underlying methodology, e.g. motivation for participating is low; technology is shaping data collection processes; response processes within businesses are imperfectly understood while alternative data sources originating from digitalization processes push the response process (thus also response quality) further out of our sight. The paper reviews these challenges, discusses them in light of new developments in the field, and proposes directions for future research. This review may help those that collect data from businesses (e.g. national statistical institutes, academia, and private statistical agencies) to reconsider their current approaches in light of what promises to work (or not) in today’s environment and to build their toolkit of business data collection methods.
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