Paper Number
ICIS2025-1969
Paper Type
Short
Abstract
Survey-based research in Information Systems routinely measures latent constructs using items containing causal language (e.g., “I feel pressured due to ICTs”). However, causal wording may overemphasize the logical relationship between two constructs for which the researchers seek to confirm a causal relationship, potentially inflating or deflating the empirical relationship between them. This study conceptualizes causal language in measurement items and proposes an experiment to test the effects of causal language drawing on established measures and relationships from the technostress domain. Specifically, we compare items worded using causal and non-causal language to investigate the impact of this variation. The goal is to empirically measure a previously undiscovered bias effect that we call “causal language bias”. We expect that this research will add to the current literature seeking to enhance rigor in scale development research and survey research.
Recommended Citation
Fischer-Pressler, Diana; Bonaretti, Dario; and Klesel, Michael, "Does “Causal Language Bias” Exist? A Proposal to Test the Effect of Phrasing Measurement Scales Using Causal Language" (2025). ICIS 2025 Proceedings. 12.
https://aisel.aisnet.org/icis2025/is_researchmethods/is_researchmethods/12
Does “Causal Language Bias” Exist? A Proposal to Test the Effect of Phrasing Measurement Scales Using Causal Language
Survey-based research in Information Systems routinely measures latent constructs using items containing causal language (e.g., “I feel pressured due to ICTs”). However, causal wording may overemphasize the logical relationship between two constructs for which the researchers seek to confirm a causal relationship, potentially inflating or deflating the empirical relationship between them. This study conceptualizes causal language in measurement items and proposes an experiment to test the effects of causal language drawing on established measures and relationships from the technostress domain. Specifically, we compare items worded using causal and non-causal language to investigate the impact of this variation. The goal is to empirically measure a previously undiscovered bias effect that we call “causal language bias”. We expect that this research will add to the current literature seeking to enhance rigor in scale development research and survey research.
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25-Research