ASYMMETRIC EVOLUTION OF ARTIFICIAL INTELLIGENCE IN BUSINESS MANAGEMENT: TRENDS AND CHALLENGES 2020-2024

Data for: Asymmetric Evolution of Artificial Intelligence in Business Management: Trends and Challenges 2020-2024 ────────────────────────────────────────────────────── This dataset comprises underlying data supporting a bibliometric and thematic analysis of artificial intelligence adoption in business management across three temporal periods (2020-2022, 2023, 2024). The research systematically reviews the landscape of AI technologies in business contexts, with particular emphasis on emerging markets and small-to-medium enterprises (SMEs). The dataset includes: 1. Comprehensive list of 30 articles selected for thematic analysis - representing the top 10 most relevant and cited documents from each temporal period (2020-2022, 2023, 2024). 2. Complete documentation of the selection process following PRISMA 2020 guidelines, including: - Initial Scopus search strategy and search terms - Inclusion and exclusion criteria applied - Detailed rationale for the 419 documents excluded from analysis - PRISMA 2020 checklist confirming methodological compliance 3. Thematic coding framework and analysis results showing: - Temporal evolution of research emphasis across the three periods - Identification of asymmetric adoption patterns between developed and developing economies - Key thematic clusters related to AI implementation in business The analysis employed Biblioshiny software with Spinglass algorithm for network clustering and co-occurrence analysis. This dataset demonstrates significant asymmetry in both research production (China displacing US as leading collaborator) and practical adoption barriers (Latin America constrained by infrastructure, talent, and institutional factors). Research questions addressed:- How has the scientific production on AI in business management evolved thematically during 2020-2024?- What asymmetries exist in research emphasis between developed and developing economies?- What temporal patterns characterize the emergence of AI adoption in business contexts? The data are available under Creative Commons Attribution 4.0 (CC-BY 4.0) license, supporting reproducibility and reuse in future research on AI adoption, business innovation, and regional technology disparities. Research Purpose and Objectives This systematic review compiles and critically analyzes existing literature on the asymmetric evolution of artificial intelligence adoption in business management contexts. The research examines how AI technologies have been integrated into business operations across three distinct temporal periods (2020-2022, 2023, and 2024), with particular emphasis on emerging markets and small-to-medium enterprises (SMEs) in Latin America and developing economies. The review emphasizes core thematic areas, including operational efficiency optimization, predictive analytics implementation, decision-making enhancement, supply chain innovation, and organizational risk management. A central focus is the identification of asymmetric patterns: the geographic and thematic disparities in how AI research priorities and organizational adoption capabilities differ between developed economies (emphasizing strategic agility and advanced applications) and developing economies (addressing foundational infrastructure and capability barriers). The primary aim is to identify dominant AI implementation trends across the three temporal periods, examine the geographical distribution of research and adoption, evaluate organizational barriers to AI integration in emerging markets, and assess how policy, infrastructure, and institutional support shape AI diffusion in business contexts. The review was conducted in accordance with the PRISMA 2020 guidelines for systematic reviews in management and information systems research. BACKGROUND AND CONTEXT The rapid advancement of artificial intelligence technologies has generated significant transformations in social and business processes globally. However, the adoption and implementation of AI in organizational contexts demonstrates considerable geographic, sectoral, and temporal heterogeneity. Developed economies have focused on optimization and strategic applications of AI, while developing economies face foundational barriers including technological infrastructure limitations, talent scarcity, and institutional constraints. This research responds to three critical gaps in the literature: (1) the lack of comprehensive analysis of how AI research priorities have evolved temporally, (2) insufficient documentation of asymmetric adoption patterns between developed and developing regions, and (3) limited understanding of the specific barriers constraining AI implementation in emerging markets and SMEs. METHODOLOGY This systematic review analyzed 263 articles from Scopus (2020-2024), applying PRISMA 2020 guidelines. Through iterative filtering based on publication type, temporal relevance, and thematic alignment, 30 studies were selected for detailed analysis: the top 10 most relevant articles from each of three periods (2020-2022, 2023, 2024). Analysis was conducted using Biblioshiny software, employing Spinglass algorithm for network clustering to identify thematic co-occurrence patterns and temporal shifts. Thematic coding categorized findings into three dimensions: AI application trends, mechanisms of strategic impact, and implementation barriers. KEY FINDINGS 1. Temporal asymmetry: Research emphasis shifted from operational efficiency (2020-2022) to predictive analytics (2023) to decision-making and risk management (2024). 2. Geographic asymmetry: China and India have displaced the United States in international collaboration on AI research, while Latin America remains marginalized with persistent adoption barriers. 3. Implementation gaps: Developing economies emphasize foundational challenges (infrastructure, talent, regulatory frameworks), while advanced economies pursue strategic optimization. RESEARCH IMPACT This analysis contributes to understanding how geographic context shapes both AI research agendas and organizational capacity for technology adoption. Findings support development of context-sensitive AI policies and targeted capacity-building initiatives for emerging markets and SMEs.