How artificial intelligence-induced job insecurity shapes knowledge dynamics: the mitigating role of artificial intelligence self-efficacy

Byung‐Jik Kim · Min‐Jik Kim

115 citations0/0 files storedJournal of Innovation & Knowledge

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Knowledge Management and Sharing Job Satisfaction and Organizational Behavior AI and HR Technologies

Abstract

This research examines the intricate relationships between artificial intelligence (AI)-induced job insecurity, psychological safety, knowledge-hiding behavior, and self-efficacy in AI learning within organizational contexts. As AI technologies increasingly permeate the workplace, comprehending their impact on employee behavior and organizational dynamics becomes crucial. Based on several theories, we use a time-lagged research design to propose and test a moderated mediation model. We collected data from 402 employees across various industries in South Korea at three different time points. Our findings reveal that AI-induced job insecurity positively relates to knowledge-hiding behavior, directly and indirectly, via reduced psychological safety. Moreover, we discover that self-efficacy in AI learning moderates the relationship between AI-induced job insecurity and psychological safety, such that high self-efficacy buffers the harmful influence of job insecurity on psychological safety. These results enhance the existing literature on organizational technological change by clarifying the psychological processes through which AI implementation influences employee behavior. Our study highlights the critical role of psychological safety as a mediator and self-efficacy as a moderator in this process. These insights present significant implications for managers and organizations navigating the challenges of AI integration. They emphasize the need for strategies that foster psychological safety and enhance members’ confidence in their ability to adapt to AI technologies. Our research underscores the significance of considering both the technical and human aspects of AI implementation within organizational contexts.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 115 citations exist upstream.

References

1991The theory of planned behavior 2003Common method biases in behavioral research: A critical review of the literature and recommended remedies.1988Structural equation modeling in practice: A review and recommended two-step approach.1997Self-efficacy: the exercise of control 1989Conservation of resources: A new attempt at conceptualizing stress.2011Sources of Method Bias in Social Science Research and Recommendations on How to Control It 1965Exchange and Power in Social Life.2001The job demands-resources model of burnout.

Cited by

2025Exploring the Dynamics of Artificial Intelligence Literacy on English as a Foreign Language Learners’ Willingness to Communicate: The Critical Mediating Roles of Artificial Intelligence Learning Self-Efficacy and Classroom Anxiety 2025AI literacy and higher education students’ digital entrepreneurial intention: A moderated mediation model of AI self-efficacy and digital entrepreneurial self-efficacy 2025Knowledge and artificial intelligence on employee behaviour advancing safe and respectful workplace 2025Association between Generative AI self-efficacy and Generative AI acceptance: The mediating role of Generative AI trust and the moderating role of Generative AI risk perception

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.