The paper on scientometric analysis contains the study about artificial intelligence (AI)- improved mobile and data-driven business decision-making and its impact on the Sustainable Development Goals (SDG 8: Decent Work and Economic Growth, and SDG 9: Innovation and Infrastructure). It is based on 2,443 articles in Scopus (2010–2025) and combines the metrics of performance, co-citation networks, and co-occurrence mapping of keywords to understand the intellectual basis and the research hotspots. The results show that the number of publications has been increasing since 2016, a sign of increasing application of AI and mobile systems in industry decision support. Based on the co-citation analysis, it identifies four intellectual clusters, including big data and analytics, smart cities, knowledge systems, and IoT infrastructures. The keyword analysis targets the cross-sectoral application of AI in business, healthcare, and digital governance. Since AI-guided mobile systems will enable making realtime, predictive, and inclusive decisions, the problems of data bias, ethical leadership, and digital disparities persist. The research creates awareness of the socio-technical systems theory and the theory of affordance, with the need for future research that combines interdisciplinary insights and explores Global South contexts and the actual application of AI-based decisions.

This study presents a bibliometric analysis of English-language, Scopus-indexed scholarship on cultural bias and ethical concerns in artificial intelligence (AI)-driven communication, covering 1,919 documents published between 2015 and 2025. Using co-citation and co-word analyses conducted in VOSviewer, the study maps the intellectual structure and thematic development of this interdisciplinary field. The findings indicate exponential growth in scholarly output, particularly from 2023 onward, coinciding with the widespread adoption of generative AI tools and large language models. Co-citation analysis identified five thematic clusters: fairness toolkits and justice frameworks; algorithmic bias and word embeddings; explainable AI and algorithmic accountability; critical studies of race and inequality; and philosophical and filtering-based sources of bias. Co-word analysis reveals that while terms such as "artificial intelligence”, “algorithmic bias”, and “machine learning” dominate the literature, the cultural dimensions of bias are frequently addressed in isolation from technical and ethical frameworks, suggesting a fragmented research landscape in which technical optimisation tends to receive more attention than deeper socio-cultural analysis. This study is limited to English-language publications indexed in Scopus and does not include grey literature, non-English scholarship, or other databases; findings should therefore be interpreted as reflecting English-language, Scopus-indexed research rather than the full body of global scholarship on the topic. The study contributes a structured, data-driven map of a rapidly expanding field and identifies gaps that future interdisciplinary research may address, including the need for integrated frameworks that bridge technical, cultural, and ethical dimensions of AI in communication.