International Journal of Human Capital in Urban Management

Abstract BACKGROUND AND OBJECTIVES: As artificial intelligence increasingly transforms business potential, its adoption in small businesses has become a growing interest. This study explores college students’ perceptions of artificial intelligence in the context of small businesses, comparing insights from Kuwait and Malaysia. With small businesses playing a pivotal role in the economic growth of both countries, understanding how future employees perceive artificial intelligence can provide valuable guidance for educators, entrepreneurs, and policymakers. The research aims to investigate key factors such as artificial intelligence awareness, perceived benefits and challenges, willingness to adopt artificial intelligence technologies, and the perceived effectiveness of artificial intelligence in improving business operations. In addition, a bibliometric analysis was conducted to compare the research trends and national emphasis on artificial intelligence in business within the two countries.
METHODS: A quantitative research approach was employed using a structured survey distributed to a total of 834 university students from Kuwait and Malaysia. The survey collected data on various dimensions, including artificial intelligence awareness, perceived utility, anticipated challenges, and readiness to engage with artificial intelligence in small business settings. The data were statistically analyzed to determine significant differences and similarities across the two national contexts. Also, a bibliometric analysis was carried out to explore the scholarly output and collaboration trends related to artificial intelligence and business in both countries.
FINDINGS: Statistical analysis of the survey data from 834 students (433 from Malaysia and 401 from Kuwait) revealed high mean scores across all constructs, ranging from 3.90 to 4.08 on a 5-point Likert scale, indicating overall positive perceptions of AI’s role in small businesses. Independent-samples t-tests showed no significant differences (p > 0.05) between the two countries regarding AI awareness, perceived benefits, anticipated challenges, willingness to adopt AI, and perceived impact on SMEs. Reliability coefficients (Cronbach’s α) for the constructs ranged from 0.73 to 0.86, confirming internal consistency. Additionally, bibliometric analysis showed that Malaysia produced 105 relevant publications from 2010–2024, with 1,872 total citations, while Kuwait produced 10 publications from 2018–2024, with 234 citations. Despite fewer publications, Kuwait demonstrated higher average citations per paper (23.4 vs. 17.83), indicating a high-impact research contribution.
CONCLUSION: This study underscores the universal recognition of artificial intelligence's value in small businesses among young professionals in Kuwait and Malaysia. The findings highlight the need for context-sensitive policies, focused artificial intelligence education, and hands-on support mechanisms for small enterprises. Cross-cultural collaboration and targeted research initiatives are recommended to responsibly harness artificial intelligence’s potential and drive sustainable innovation in the small business sector.

Abstract BACKGROUND AND OBJECTIVES: Human Resource Analytics has become a strategic enabler in modern workforce management, particularly amid digital transformation. However, scholarly research in Human Resource Analytics remains fragmented across regions and disciplines. This study employs bibliometric analysis to map global research output in Human Resource Analytics from 2012 to 2024, identifying trends, influential contributors, thematic concentrations, and underexplored areas.
METHODS: A bibliometric analysis approach was applied using data retrieved from the Scopus database. The dataset covered publication metadata, including authorship, institutional affiliation, geographic origin, keywords, sources, and citation counts. VOSviewer software was used to construct visual maps illustrating keyword co-occurrence, co-authorship networks, and citation clusters, enabling a comprehensive overview of research activity and intellectual structure in the Human Resource Analytics.
FINDINGS: A total of 211 publications are identified and analysed over 13 years, involving 602 contributors. The analysis highlights 2,674 citations, with an average of 12.67 citations per paper. The corresponding h-index and g-index are 24 and 47, respectively. The results reveal three research themes. They present an increasing global interest, with leading contributions from high-income countries. The three core themes are workforce analytics, Artificial Intelligence in Human Resource Management, and strategic decision-making. Notably, low representation from developing regions and limited use of advanced predictive analytics highlight critical research gaps.
CONCLUSION: This study provides a comprehensive quantitative overview of the evolution of Human Resource Analytics scholarship. It guides future research by identifying emerging priorities, regional disparities, and methodological developments, offering valuable insights for advancing data-driven human resource practices and fostering more strategic, evidence-based decision-making in HR management and policy development globally.