Cross-sectional research: A critical perspective, use cases, and recommendations for IS research. (June 2023)

• Record Type: Journal Article
• Authors: Maier, Christian; Thatcher, Jason Bennett; Grover, Varun; Dwivedi, Yogesh K.
• Is Part Of: International journal of information management. Volume 70(2023)
• Publication Date: 2023-06
• Subjects: Cross-sectional research -- Cross-sectional study design -- Cross-sectional survey -- Cross-sectional survey design -- Survey -- Survey research design -- Causality; Social sciences -- Information services -- Periodicals; Social sciences -- Research -- Periodicals; Information science -- Periodicals; Management information systems -- Periodicals; Knowledge management -- Periodicals; Sciences sociales -- Documentation, Services de -- Périodiques; Sciences sociales -- Recherche -- Périodiques; Sciences de l'information -- Périodiques; Systèmes d'information de gestion -- Périodiques; Information science; Management information systems; Social sciences -- Information services; Social sciences -- Research; Periodicals; Electronic journals; 025.52068
• DOI: 10.1016/j.ijinfomgt.2023.102625
• Languages: English
• ISSNs: 0268-4012

Abstract

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. Highlights: Cross-sectional study designs are predominant in Information Systems research. Cross-sectional research follows an efficient and inexpensive execution. Cross-sectional data limit statements related to temporal causality. Use configurational or mixed-method studies when having cross-sectional data. We offer four recommendations for when and how to use cross-sectional data.