The Impact of Artificial Intelligence on Organizational Justice and Project Performance: A Systematic Literature and Science Mapping Review

Abstract

By adopting a systematic literature and science mapping review, this paper aims to explore the impact of artificial intelligence (AI) on organizational justice and project performance. A total of 47 bibliographic records from the Scopus database were analyzed. The results revealed the annual publication trends of research articles and relevant peer-reviewed journals in the studied domain. It was found that while AI technology has made significant progress in several fields, its application areas in project management and organizational justice are still relatively low. Moreover, it objectively discussed the co-occurrence analysis of keywords, co-authors, countries/regions, and documents in the fields, revealing the current research topics. The main research topics include the (1) AI’s influence on organizational justice, decision analysis, and digital transformation, (2) fostering organizational justice and AI’s role in enhancing project performance, and (3) improving organizational performance approaches. Furthermore, this paper proposed research gaps and future research directions, including (1) advancing business intelligence strategies, (2) unlocking AI technology potential on organizational justice and project performance, (3) the adaption of cultural, diversity, environmental, and social factors, (4) the impact of AI on complex and challenging leadership styles, and (5) developing a comprehensive understanding of the agile framework. The findings of this paper could contribute to a better understanding of how AI shapes project/construction management and organizational justice, providing practical solutions for innovative development for researchers and policymakers.

Fuzzy Integrated Delphi-ISM-MICMAC Hybrid Multi-Criteria Approach to Optimize the Artificial Intelligence (AI) Factors Influencing Cost Management in Civil Engineering

“…• Algorithm selection (F11): Selecting the right algorithm is key to accurate cost estimation and resource allocation. Proper algorithm choices ensure an efficient use of project resources, reduce financial risks, and enhance the likelihood of project success within budget constraints [21,22,46].…”

“…Machine learning models can optimize energy usage, reduce waste, and lower environmental impact, leading to more sustainable civil engineering practices. AI-driven sustainability efforts allow organizations to meet environmental goals while maintaining their project's cost-effectiveness and efficiency [36][37][38][39]45,46].…”

“…Social impact (F52): Social impact focuses on community engagement, stakeholder satisfaction, and reduced project disruptions, ultimately contributing to project success. Considering social impact supports sustainable development, mitigates reputational risks, and ensures positive outcomes for both the project and surrounding communities [39,41,42,46].…”

“…• Equity and fairness (F81): Equity and fairness ensure transparent decisionmaking, foster stakeholder trust, and reduce potential conflicts. Emphasizing these values in cost management encourages accountability and stakeholder participation and supports the social responsibility of civil engineering projects [45,46,49].…”

“…Ensuring privacy and data security prevents unauthorized access, preserves confidentiality, and supports compliance with legal and regulatory requirements, enhancing the integrity of cost management in civil engineering [33,40,45,46,49].…”