Abstract
Cross-sectional data is pervasive in information systems (IS) research. This editorial reviews cross-sectional studies, summarizes their strengths and limitations, and derives use cases of when cross-sectional data is and is not useful in answering research questions. We raise concerns about assertions of temporal causality using data collected employing cross-sectional methods with no temporal order, which makes cause and effect difficult to establish. Based on our discussion of research using cross-sectional data and its limitations, we offer four recommendations for when and how to use such data: (1) improve credibility by reporting research in detail and transparently, (2) ensure appropriate sampling, (3) take configurational perspectives, and (4) integrate cross-sectional data into mixed- or multi-method designs. By doing so, we help IS researchers position and use cross-sectional studies appropriately within their methodological repertoire.
| Original language | English |
|---|---|
| Article number | 102625 |
| Journal | International Journal of Information Management |
| Volume | 70 |
| DOIs | |
| State | Published - Jun 2023 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2023 Elsevier Ltd
Keywords
- Causality
- Cross-sectional research
- Cross-sectional study design
- Cross-sectional survey
- Cross-sectional survey design
- Survey
- Survey research design
ASJC Scopus subject areas
- Management Information Systems
- Information Systems
- Computer Networks and Communications
- Information Systems and Management
- Marketing
- Library and Information Sciences
- Artificial Intelligence
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