Obenauer, W. G., & Kreamer, L. M. (2026). A practical guide to maximizing the benefits of experimental vignette methodology. Journal of Business and Psychology. https://doi.org/10.1007/s10869-026-10138-8
Podsakoff, P. M., Podsakoff, N. P., & Shao, Y. (2026). Experimental approaches for testing mediation effects models: A review, assessment, and recommended practices. Journal of Business and Psychology, 41, 1-26. https://doi.org/10.1007/s10869-025-10089-6
French, K. A., Storey, R., Egdom, D. V., & Spitzmüller, C. (2026). Micro-level quantitative archival data sets: A review and development of empirically grounded recommendations. Journal of Business and Psychology. https://doi.org/10.1007/s10869-026-10109-z
Roebke, M. A., Bowling, N. A., & Burns, G. N. (2026). The effects of careless responding warnings on the construct validity of self-report measures. Journal of Business and Psychology, 41, 265-283. https://doi.org/10.1007/s10869-025-10055-2
Sturman, M. C., Richardson, H. A., Simmering, M. J., & Ukhov, A. (2025). Simplifying common method variance mitigation: The role of additional variables. Journal of Business and Psychology. https://doi.org/10.1007/s10869-025-10074-z
Lee, P., Son, M., & Jia, Z. (2026). AI-powered automatic item generation for psychological tests: A conceptual framework for an LLM-based multi-agent AIG system. Journal of Business and Psychology, 41, 71-99. https://doi.org/10.1007/s10869-025-10067-y
Banks, G. C., Tonidandel, S., Dou, W., Gerson, M. J., Xu, D., & Yavorsky, J. E. (2025). How to reduce bias in the life cycle of a data science project. Journal of Business and Psychology, 40, 1253-1273. https://doi.org/10.1007/s10869-025-10022-x
Härtel, T. M. (2026). In the blink of an AI: Exploring large language models' capability to infer traits from LinkedIn. Journal of Business and Psychology, 41, 47-69. https://doi.org/10.1007/s10869-025-10087-8