Abstract

Objectives: The extensive adoption of AI applications in many industries raises concerns over their potential to significantly transform employment in various aspects, including job creation, automation, and decision-making procedures. The impact that AI has on employment dynamics presents an array of obstacles and opportunities for the fields of business administration and management. Given all these advancements, the present study conducts a thorough examination of postgraduate theses, recognizing them as a systematic representation of the expanding influence of AI in the fields of business administration and management. Postgraduate research, which entails a thorough academic investigation, provides a crucial perspective for understanding the present condition and future course of AI use in business environments. Through an in-depth examination of postgraduate theses, this research attempts to reveal trends in the application of AI technologies in various organizational contexts. Design/methodology/approach: The 73 master's and doctoral theses used in this study were obtained from the repository of the Council of Higher Education National Thesis Center. The sample spans a range of institutions, demonstrating the diversity and depth of AI research across the academic landscape of management studies. To comprehensively examine and understand the information categories encompassed in these theses, the research utilizes two techniques: document analysis and descriptive content analysis. Document analysis systematically examines the theses as data sources, enabling a thorough investigation of the material that focuses on the identification, assessment, and synthesis of information pertaining to AI in business administration and management. Descriptive content analysis classifies and facilitates a methodical examination of the data. The distribution of theses subjected to document analysis was based on thesis type, universities and sub-disciplines, publication year and language, sample characteristics, methodology, theories, AI application areas, and keywords. Results: The findings show that studies on AI have gained momentum in the last four years, with a high percentage of theses focusing on management, organization, and marketing. The quantitative research method has been the most preferred for postgraduate theses. Additionally, human resource management, machine learning, and artificial neural networks constitute most of the research focus. The data indicates that most master's theses concentrate on human resource management and marketing. The finance and information technology sectors are predominant in terms of industry focus. Practical implications: The findings have the potential to significantly assist practitioners in understanding the ongoing research on AI, enabling them to align their strategic planning with the latest discoveries and approaches in higher education. The prevalence of machine learning and artificial neural networks signifies an inclination towards increasingly complex AI implementations within organizations. Organizations may leverage this understanding to increase innovation, enhance decision-making procedures, and sustain a competitive advantage through the implementation of cutting-edge AI. Furthermore, this study can assist researchers, such as master's and doctoral students, with the topic, research question, data source, data collection tool, and analysis type selection in future studies. Originality/value: The study is expected to provide insights into research focus, scope, and methodology for future research by revealing the current state of AI research in business administration and management domains in Turkey. Through the examination of postgraduate theses, this study not only highlights the increasing academic attention towards AI but also establishes a foundation upon which subsequent research can be built. This study represents an initial effort to gain deeper insights into AI research in Turkey, specifically focusing on knowledge production within universities.

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