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International Journal of Management Science
Published by Tinta Emas Publisher
ISSN : -     EISSN : 29880475     DOI : https://doi.org/10.59535/ijms
Core Subject : Science,
The International Journal of Management Science aims to provide a platform for researchers, academics, and practitioners to contribute to the advancement of knowledge in the field of management science. The journal welcomes original research articles, theoretical papers, empirical studies, case analyses, and literature reviews that address various aspects of management across different industries and organizational settings. The focus and scope of The International Journal of Management Science include, but are not limited to, the following areas: Organizational Behavior: Studies examining individual and group behavior within organizations, including topics such as motivation, leadership, decision-making, and workplace diversity. Strategic Management: Research on the formulation, implementation, and evaluation of organizational strategies, including strategic planning, competitive analysis, and corporate governance. Operations Management: Investigations into the efficient and effective management of processes, resources, and supply chains, covering topics such as production planning, quality management, and logistics. Human Resource Management: Studies related to the management of human capital within organizations, including recruitment, training and development, performance appraisal, and employee relations. Marketing Management: Research on marketing strategies, consumer behavior, market research, branding, advertising, and product development. Financial Management: Analysis of financial decision-making, investment strategies, capital budgeting, risk management, and corporate finance. Innovation Management: Studies focusing on innovation processes, technology management, entrepreneurship, and organizational creativity. The International Journal of Management Science encourages interdisciplinary approaches and welcomes research that combines management science with other fields such as economics, sociology, psychology, and information systems. The journal emphasizes the practical implications and managerial relevance of research findings, aiming to bridge the gap between theory and practice in the field of management.
Articles 31 Documents
Hybrid Intelligence Between Humans and AI in Modern Business Operations: An Integrative Literature Review Van Nhan Nguyen; Nguyen Van Luc
International Journal of Management Science Vol. 4 No. 1 (2026): January-June
Publisher : Tinta Emas Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59535/ijms.v4i1.658

Abstract

Artificial intelligence (AI) and generative AI are reshaping business operations by enabling Hybrid Intelligence, in which human judgment and machine intelligence are mobilized for organizational decision-making. Yet prior research remains fragmented, emphasizing automation, efficiency, and digital transformation more than the mechanisms through which humans and AI collaborate, how leadership roles change, and how such collaboration should be governed, particularly in the Vietnamese enterprise context. This study develops an integrative synthesis of the literature on Hybrid Intelligence, digital leadership, and AI-enabled business governance. An integrative literature review was conducted using structured document screening inspired by PRISMA guidelines. Publications from Scopus, Web of Science, Dimensions, Google Scholar, and PubMed were reviewed for the period 2016–April 2026. The synthesis shows that Hybrid Intelligence emerges from the complementary integration of AI’s capabilities in data processing, prediction, pattern recognition, and knowledge acceleration with human critical reasoning, strategic intuition, creativity, ethical judgment, and empathy. The findings also indicate a shift in leadership from hierarchical control toward ecosystem orchestration, where leaders define goals, interpret AI outputs, manage risks, and ensure responsible human–AI collaboration. Based on the reviewed literature, the study proposes a conceptual Hybrid Intelligence governance model comprising five components: AI capability, human capability, collaboration mechanisms, leadership transformation, and sustainable organizational performance. It also identifies key governance challenges, including hallucination, algorithmic bias, skill displacement, and overdependence on automated systems. Overall, the paper argues that sustainable enterprise value in the AI era depends not on replacing humans, but on designing accountable systems that integrate human and machine intelligence.

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