Skip to content

Building organizational trust in generative AI in female-led cybersecurity startups: dual pathways of ethical responsibility and privacy assurance

Unknown authors
Sep 2026 · Journal of Enterprise Information Management · 0 citations · 34 references

TL;DR

A dual-pathway moderated mediation model of organizational trust in generative AI is developed and tested, showing how ethical responsibility and privacy assurance jointly shape trust under specific organizational and regulatory conditions.

Abstract

This study examines how organizational trust in generative AI is built in high-stakes entrepreneurial settings, particularly female-led cybersecurity startups, through ethical responsibility and privacy assurance as complementary governance pathways. Survey data were collected from 288 mid- and senior-level managers in female-led cybersecurity startups across North America and Europe. The model was estimated using partial least squares structural equation modeling (PLS-SEM) in SmartPLS 3.2.8 with 5,000 bootstrap subsamples. Responsible entrepreneurial leadership and cybersecurity innovation practices both positively predict organizational trust in generative AI. Perceived ethical responsibility and perceived privacy assurance carry significant indirect effects, respectively. The conditional indirect effects increase across higher levels of gender diversity climate and regulatory environment, and both indices of moderated mediation have 95% BCa confidence intervals that exclude zero. The study relies on cross-sectional perceptual data from a specific organizational context. Future research should test the model longitudinally, incorporate objective governance indicators and examine broader organizational and institutional settings. Organizations can strengthen trust in generative AI by institutionalizing ethical responsibility and developing credible privacy assurance practices. Supportive diversity climates and clear regulatory conditions further enhance the effectiveness of these trust-building mechanisms. The findings highlight the importance of responsible and privacy-protective AI governance in high-stakes domains, where trustworthy deployment can reduce misuse concerns and strengthen stakeholder confidence. The study develops and tests a dual-pathway moderated mediation model of organizational trust in generative AI, showing how ethical responsibility and privacy assurance jointly shape trust under specific organizational and regulatory conditions.

View source

Similar papers

#artificial intelligence Conference Open access Apr 2020

ECCOLA - a Method for Implementing Ethically Aligned AI Systems

The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.

Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson · 64 citations · ⚡6
#computer vision Review Apr 2024

AI-powered Code Review with LLMs: Early Results

The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.

Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al. · 62 citations · ⚡3
#computer vision Open access Mar 2024

LLM-based agents for automating the enhancement of user story quality: An early report

The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.

Zheying Zhang, M. Rayhan, Tomas Herda et al. · 48 citations · ⚡4
#computer vision Review Mar 2024

System for systematic literature review using multiple AI agents: Concept and an empirical evaluation

This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.

Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al. · 44 citations · ⚡2
#computer vision Feb 2024

Can Large Language Models Serve as Data Analysts? A Multi-Agent Assisted Approach for Qualitative Data Analysis

The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.

Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al. · 41 citations

Related blog posts

GPT-Lab Sep 17, 2026

Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering

AI is making software generation faster, but speed does not remove the need for expertise. As more work is delegated to AI, tacit knowledge may become one of the most important human advantages in software engineering. The post Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering appeared first on GPT-Lab.

MIT News · Artificial Intelligence Sep 14, 2026

New method enables AI for safety-critical situations

The “HardFlow” algorithm could help generative AI models produce high-quality outputs that obey strict requirements when “pretty close” doesn’t cut it.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.