Abstract
Background: Knowledge-based risk processes are turning into a major aspect inside establishments because they can limit the likelihood and effect of information technology (IT) project threats and catch opportunities amid the life cycle of an IT project. Objectives: The objective of the article is to propose a model that describes the amalgamation of knowledge-based risk processes (knowledge-based risk capture and knowledge-based risk discovery) and knowledge-based risk repositories to support risk identification. Method: This article adopts a quantitative research approach using surveys, with questionnaires composed from 135 employees in 10 IT companies in Jordan. The researchers employed smart partial least squares analysis to examine all of the hypotheses. Results: The results propose that the adoption of knowledge-based risk capture and knowledge-based risk discovery has a positive impact on risk identification, which is mediated by knowledge-based risk repositories. Conclusion: Knowledge-based risk processes and knowledge-based risk repositories are generally utilised as a part of IT projects to effectively and successfully support risk identification.
Highlights
In the present profoundly aggressive and quickly changing worldwide economy, the world is seeing real turbulence
This study examines the relationship between knowledgebased risk processes and knowledge-based risk repositories and their influence on risk identification
Tying in with a previous study, we suggest a model in this article to highlight the causal relationships between two knowledge-based risk processes and knowledgebased risk repositories to effectively support risk identification
Summary
The objective of the article is to propose a model that describes the amalgamation of knowledge-based risk processes (knowledge-based risk capture and knowledge-based risk discovery) and knowledge-based risk repositories to support risk identification. Method: This article adopts a quantitative research approach using surveys, with questionnaires composed from 135 employees in 10 IT companies in Jordan. The researchers employed smart partial least squares analysis to examine all of the hypotheses
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