EXPERTS IN BUSINESS EDUCATION

Research Projects

Research Projects

Research Projects

The below detailed research projects aim to provide practical and actionable insights in each thematic area, contributing to the development of a more robust financial and economic ecosystem, fostering innovation and entrepreneurship, understanding the implications of AI and machine learning, and addressing sustainability and integral security challenges.

Finance & Economics

  • Cryptocurrency Regulation Impact:

    • Objective: Evaluate the impact of specific regulatory measures on the cryptocurrency market.
    • Methodology: Analyze market data before and after regulatory changes, considering factors such as trading volumes, price volatility, and investor sentiment.
    • Outcomes: Identify patterns and trends to inform policymakers and market participants about the effectiveness and unintended consequences of cryptocurrency regulations.
  • Behavioral Economics in Investment Decisions:

    • Objective: Understand how behavioral biases influence investment decisions.
    • Methodology: Conduct experiments or surveys to assess how cognitive biases, such as loss aversion and overconfidence, affect investment choices.
    • Outcomes: Develop behavioral models that can be integrated into investment strategies, contributing to the development of more resilient and adaptive financial systems.
  • Financial Inclusion and Economic Development:

    • Objective: Examine the relationship between financial inclusion and economic development.
    • Methodology: Use econometric analysis to assess the impact of financial inclusion initiatives on key economic indicators in developing regions.
    • Outcomes: Provide evidence-based recommendations for policymakers on effective strategies to promote economic development through increased financial inclusion.

Innovation & Entrepreneurship

  • Digital Transformation in Traditional Industries:

    • Objective: Identify challenges and opportunities in the digital transformation of traditional industries.
    • Methodology: Conduct case studies on companies that successfully embraced digital transformation, analyzing the strategies, technologies, and organizational changes involved.
    • Outcomes: Develop a framework for guiding traditional businesses through the digital transformation process, addressing common obstacles.
  • Startup Ecosystem Analysis:

    • Objective: Understand the key factors contributing to the success of startup ecosystems.
    • Methodology: Combine quantitative analysis of startup performance with qualitative research on ecosystem dynamics, including interviews with entrepreneurs, investors, and policymakers.
    • Outcomes: Provide insights for policymakers and ecosystem builders on effective strategies for fostering a vibrant startup environment.
  • Impact of Open Innovation Models:

    • Objective: Assess the impact of open innovation on business innovation and competitiveness.
    • Methodology: Compare the innovation outcomes of companies adopting open innovation practices with those relying on traditional closed models.
    • Outcomes: Provide recommendations for organizations seeking to leverage open innovation for sustained business growth.

Artificial Intelligence & Machine Learning

  • Ethical Considerations in AI Adoption:

    • Objective: Investigate ethical challenges associated with the widespread adoption of AI in business.
    • Methodology: Conduct interviews with industry experts and stakeholders to identify ethical concerns and potential solutions.
    • Outcomes: Develop ethical guidelines for businesses adopting AI, promoting responsible and transparent use of AI technologies.
  • Explainability in Machine Learning Models:

    • Objective: Enhance the explainability of complex machine learning models.
    • Methodology: Explore and compare various explainability techniques, such as LIME or SHAP, and assess their effectiveness in different contexts.
    • Outcomes: Provide a toolkit for practitioners to improve the interpretability of their machine learning models, addressing concerns related to bias and trust.
  • AI for Financial Forecasting:

    • Objective: Develop and evaluate machine learning models for accurate financial forecasting.
    • Methodology: Train models using historical financial data and assess their performance against traditional forecasting methods.
    • Outcomes: Provide insights into the potential of AI for enhancing financial decision-making, with practical guidelines for implementation.

Sustainability and Integral Security

  • Circular Economy Implementation:

    • Objective: Evaluate the challenges and benefits of implementing circular economy principles in specific industries.
    • Methodology: Conduct life cycle assessments and economic analyses to quantify the environmental and economic impacts of transitioning to circular business models.
    • Outcomes: Offer industry-specific recommendations for achieving sustainable and circular practices.
  • Cybersecurity in Sustainable Supply Chains:

    • Objective: Assess the cybersecurity risks in sustainable and circular supply chains.
    • Methodology: Identify potential vulnerabilities and conduct risk assessments, proposing strategies to enhance cybersecurity resilience in eco-friendly supply chain operations.
    • Outcomes: Provide a framework for integrating cybersecurity measures into sustainable business practices.
  • Health and Environmental Impact Assessment:

    • Objective: Conduct a comprehensive assessment of the health and environmental impacts of business operations.
    • Methodology: Integrate health impact assessments and environmental impact assessments into traditional business impact assessments, providing a holistic view of corporate activities.
    • Outcomes: Develop guidelines for businesses to incorporate sustainability and integral security considerations into their decision-making processes.