Daniel Mandel Gandrita, PhD, is a university lecturer, researcher, author, and
management professional with a strong academic and professional background in
strategic management, organizational strategy, leadership, artificial intelligence,
and organizational transformation.
He holds a PhD in Management from Universidade Europeia, completed in 2024, with
a doctoral thesis entitled Rethinking the Strategic Planning Paradigm: A Dynamic
Capabilities Perspective. He also holds a Master's degree in Management and Business
Strategy and a Bachelor's degree in Management, specializing in Business Management.
Daniel currently teaches management-related subjects at the Portuguese Military
Academy and has also taught at Universidade Lusófona, including Strategic
Management, Management Planning and Control, Human Resources Management,
Research Methods, and Introduction to Management. He is also a researcher at Intrepid
Lab and CETRAD – Centre for Transdisciplinary Development Studies, where his
research interests focus particularly on strategy, innovation, leadership, organizational
performance, and the impact of artificial intelligence on organizations.
His research has been published in international academic journals, including the
European Business Review, Thunderbird International Business Review,
Administrative Sciences, Technology in Society, EuroMed Journal of Business, and
Journal of Financial Reporting and Accounting. His work examines topics such as
strategic planning, dynamic capabilities, AI integration, workplace inclusion, leadership,
employee attitudes, and organizational decision-making.
He is also co-author of the book The AI Leadership: Finding Algorithm between Head
and Heart and has contributed to international scientific publications addressing the
evolving role of leadership and artificial intelligence in management.
In addition to his academic work, Daniel serves as a reviewer for several international
scientific journals, including Long Range Planning, Thunderbird International
Business Review, Acta Astronautica, Behavioral Sciences, Sustainability, and
Nature, and is a member of the editorial boards of several management and business
journals.
His academic and professional profile combines research, teaching, management
practice, and strategic analysis, with a particular interest in understanding how
organizations can strengthen their strategic capabilities and adapt to rapidly changing
technological and competitive environments.

Title: When Does Workplace AI Improve Job Satisfaction? A Configurational Perspective on Self-Evaluation and Social Comparison - Abstract: The rapid advancement of technology has rendered artificial intelligence (AI)
increasingly ubiquitous, presenting significant challenges for the workforce and the
ability to determine job satisfaction. The purpose of this research is to determine whether
and how the use of artificial intelligence (AI) in the workplace influences employees’ job
satisfaction, with focus on the role of self-evaluations and social comparison processes.
A quantitative research design was employed via online survey using a convenience
sample among European employees and a cross-sectional approach testing deductive-
hypothesis techniques. We analyzed 250 valid questionnaires using hybrid analysis
measurements (PLS-SEM and FsQCA) representing a 49,8% response rate. Furthermore,
reliability, convergent validity, and discriminant validity were assessed. Additionally,
mediation effects on self-evaluation and social comparison on the relationship between
workplace AI and job satisfaction were examined. Our findings. The investigation
contributes theoretically by explaining the mechanisms linking AI to employee
satisfaction and the focus on self-evaluations and offers practical guidance for companies
seeking the implementation of AI without compromising employee satisfaction and the
evaluations format.