Keywords

COVID-19, event-driven models, indirect effect size, mediation models, prediction, segmentation mediation, transmittal mediation

Abstract

The event-driven model (EDM) is an emerging concept in human behavioural research, and understanding how EDMs can promote theory development remains a fundamental quest of predictive science. Traditionally, researchers have heavily depended upon theory confirmation and the inclusion of mediating constructs to clarify uncertainty associated with plausible events (e.g. political, socio-economic, technological, environmental). Though this approach has pushed the field forward, it has also steered mediation research towards largely ignoring the fundamental role of prediction as a key for better understanding future events represented by EDMs. Additionally, emerging research using partial least squares structural equation modelling to execute prediction-oriented analysis continues to overlook problematic endogeneity bias and plausible type IV errors due to omitted paths and neglect of indirect effect size estimation in mediation models that embrace the transmittal or segmentation mediation approaches. We aim to introduce prediction as a fundamental option for estimating EDMs and recommend that researchers employ the segmentation mediation approach when estimating EDMs. We further emphasize a novel direct and indirect (v) effect size measure, types of prediction and cases when they are useful. Best practices and practical implications are provided to foster a more useful interpretation of findings.

Original Publication Citation

Ogbeibu, S., & Gaskin, J. (2022). Back from the Future: Mediation and Prediction of Events Uncertainty through Event-Driven Models (EDMs). FIIB Business Review, 12(1), 10-19. https://doi.org/10.1177/23197145221121084

Document Type

Peer-Reviewed Article

Publication Date

2022

Publisher

FIIB Business Review

Language

English

College

Marriott School of Business

Department

Information Systems Management

University Standing at Time of Publication

Full Professor

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