# The forgotten middle: How moderate self-efficacy amplifies the threat of AI through job insecurity

## TL;DR

This paper explores how AI affects job performance by causing job insecurity, with moderate self-efficacy leading to the strongest negative effects.

## Contribution

It introduces a nonlinear relationship between self-efficacy and AI-induced job insecurity, extending COR theory.

## Key findings

- AI's positive empowerment is offset by psychological threats, forming a suppression structure.
- Job insecurity mediates AI's impact on performance, with self-efficacy moderating this in an inverted U-shape.
- Moderate self-efficacy leads to highest insecurity and strongest negative performance effects.

## Abstract

Artificial intelligence (AI) has sparked a paradox in organizational behavior research: while it promises productivity gains, it simultaneously generates psychological strain and inconsistent performance outcomes.

Drawing on Conservation of Resources (COR) theory and technology empowerment theory, this study investigates how AI adoption affects employee job performance through job insecurity and how self-efficacy shapes this relationship in a nonlinear way. Using multi-source paired data from 392 employees and their supervisors in China’s cross-border e-commerce sector, the study tests a suppression-based mediation model.

The results reveal that AI’s positive technological empowerment is fully offset by its negative psychological threat, forming a suppression structure. Job insecurity mediates the relationship between AI application and performance, while self-efficacy moderates this effect in an inverted U-shaped manner—employees with moderate self-efficacy experience the highest insecurity and the strongest indirect negative effect.

These findings advance COR theory by conceptualizing self-efficacy as a finite resource and highlight how psychological mechanisms determine whether AI empowers of undermines employees.