Mustafa Akben, Ph.D.

Mustafa Akben

I work as an assistant professor of management at Elon University. My primary area of research is centered around deep learning and generative artificial intelligence and how they affect human cognition. I am particularly interested in exploring how generative AI may transform, enhance, or even (maybe!) replace human cognitive action in the workplace. Additionally, I develop machine learning tools and methods to advance social science research while also focusing on AI policy and institutional readiness.

My AI research projects encompass the creation of a recurrent neural network aimed at detecting careless responses in survey data with user biometrics, a transformer-based neural network for identifying leadership skills and conducting analyses on AI readiness and AI policy in educational institutions with social network analysis.

Another area of interest for me is understanding how people engage in “wise” proactive behaviors that are accepted by their normative landscapes and result in positive outcomes. This includes behaviors such as voicing opinions, taking initiative, crafting one’s own job, and leading personal innovation. By understanding the context in which these behaviors occur, including relational, normative, and self-regulatory elements at work, I hope to provide guidance for individuals to effectively fulfill their goals, managers to constructively guide employees’ proactivity, and organizations to avoid negative repercussions of proactivity. 

To achieve my overall research goal, I work collaboratively with other researchers and industry professionals. I am open to designing and implementing experiments that provide valuable insights into the interaction between AI technologies and human cognition. 

I have published award-winning research papers and introduced new concepts and ideas into the management literature. I am proficient in a wide range of statistical methods and research design, including path analysis, structural equation modeling, multivariate analysis, multilevel modeling, meta-analysis, social network analysis, experimental design, and machine learning models (deep learning). I also have experience working with qualitative methods such as content analysis and grounded theory.

Ultimately, my goal is to contribute to the academic community and society by developing innovative solutions that address the challenges posed by the rapidly evolving landscape of work and technology and understanding human nature within it. 

If you’re interested in learning more about my research, email me at

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