
Abstract As a research method, the survey is known to be subject to various types of biases. Nevertheless, a survey is often the most direct and cost-effective way to solicit people's opinions. We emphasize that, regardless of the data collection method, it is impossible to remove all potential biases in a research setting. Aside from adhering to research design best practice, authors are responsible for providing sufficient transparency in the reporting of their work to enhance replicability and to allow others to evaluate the validity of their research. In this paper, we develop a reporting guideline for IS survey research. Researchers conducting IS survey research can use this guide as a checklist when they prepare their manuscripts, and peer reviewers can use it to evaluate research quality and the sufficiency of reporting. We hope that similar guidelines can be developed for other IS research methods and that their use and endorsement by research outlets can motivate researchers to pay greater attention to research design and data quality.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 10 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
