The Effect of Technology-Driven Leadership on Human-Machine collaboration in Decision making in Ethiopia
No Thumbnail Available
Date
2025-11
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Addis Ababa University
Abstract
This research is about how emerging forms of technology-driven leadership are shaping the integration of human-machine collaboration in business strategies within Ethiopia’s financial sector. As technologies such as artificial intelligence (AI), big data, and intelligent systems become increasingly embedded in organizational processes, leaders are required to adapt their approaches to effectively align human capabilities with machine intelligence.
The study determinate the relationship between leaders’ digital competencies, trust in technology, and the organizational environment in facilitating machine integration. Based on data collected from 186 senior leaders in banks, insurance companies, and audit firms in Addis Ababa, the analysis utilized descriptive statistics, correlation, and multiple linear regression techniques.
Results indicate that leaders who demonstrate high levels of technology acceptance and digital competence are significantly more likely to adopt and support human-machine collaboration. Among all variables, technology acceptance was identified as the strongest predictor of collaboration success. While the infrastructure and strategic intent are generally in place, the findings highlight a need for improved training in data analytics and machine-based decision-making.
The study concludes that Ethiopia’s financial institutions are progressing toward digitally enabled leadership but must strengthen capacity-building efforts and ethical oversight to fully realize the benefits of AI-human collaboration. The research offers practical recommendations for advancing leadership readiness and organizational support in the age of digital transformation.
Description
Keywords
digital competencies: Leadership that uses digital technologies (such as AI and data systems) to drive organizational change, strategy, and innovation. Artificial Intelligence (AI) Adoption: The process of implementing AI technologies within organizations for automation, analysis, or enhanced decision-making. Multiple Linear Regression: A statistical method used to understand the relationship between one dependent variable and multiple independent variables.