This course focuses on analytical and quantitative methods aimed at supporting strategic decision-making processes in the energy sector. It presents frameworks for strategy formulation and industry analysis, with a focus on value creation and forces of competition. The course provides insights to understand how big data and artificial intelligence have changed strategic decisions and practical guidelines to manage big data challenges in decision-making.
This fourth contribution focuses on analytical and quantitative methods to support strategic decision-making processes in the energy sector. It presents fundamental frameworks for strategy formulation and industry analysis, with a focus on value creation and forces of competition. It provides pillars to understand how big data and artificial intelligence have changed strategic decisions and practical guidelines to manage big data challenges in decision-making.
No specific prerequisites are required. However, a basic knowledge of the fundamental concepts of strategic management is preferable.
The course adopts a predominantly distance-learning teaching mode. Distance-delivered teaching consists of recorded lectures covering the theoretical and conceptual content of the module. In-presence teaching consists of practical exercises, group activities and guided discussions aimed at stimulating interaction, consolidating analytical skills and encouraging the application of methods and tools to concrete cases from the energy sector.
Students who have valid certification of physical or learning disabilities and who wish to discuss possible accommodations or other circumstances regarding lectures, coursework and exams, should speak both with the instructor and with Professor Elena Lagomarsino elena.lagomarsino@unige.it, the Department's disability liaison.
The Strategic Decision-Making Process and Quantitative Methods course covers theories and techniques to support strategic decision-making processes, with a focus on the energy sector and ecological transition. The course combines a theoretical framing of strategic concepts with analytical and quantitative tools for context analysis, identification of competitive advantage, and the use of big data and artificial intelligence to support decisions.
Main contents
2.Strategic analysis and the competitive environment
3. Resources, capabilities and competitive advantage
4. SWOT analysis and portfolio strategies
5. Big data and artificial intelligence in decision-making processes
Grant, R. M. (2021). Contemporary strategy analysis. John Wiley & Sons. [Limited to the parts indicated by the lecturer]
Ahmad, T., Zhang, D., Huang, C., Zhang, H., Dai, N., Song, Y., & Chen, H. (2021). Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities. Journal of Cleaner Production, 289, 125834.
Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70, 263-286.
Ricevimento: The reception takes place every Wednesday from 12 noon to 1 p.m. at the Department of Economics (DIEC, 5 Francesco Vivaldi Street, 1st Floor). It is possible to request a reception via Teams. Reception in person or on Teams must be arranged in advance via email (andrea.ciacci@unige.it)
ANDREA CIACCI
STEFANO BRACCO (President Substitute)
STEFANO MASSUCCO (President Substitute)
MARTA NAI RUSCONE (President Substitute)
Classes begin in the first week of the second semester, according to the Department of Economics teaching calendar.
The final examination will consist of a written test designed to assess understanding of the theoretical concepts and application of the analytical tools covered in the course. Further guidance on assessment methods will be provided during the course.
The questions in the written test focus on the fundamental themes and concepts covered in the course. Case studies are designed to measure the students’ ability to apply knowledge to real-world scenarios. Assessment criteria include the degree of understanding of key concepts, decision-making scenarios and analytical ability, as well as the appropriate use of disciplinary language.
Students with certifications of DSA, disability or other special educational needs are invited to get in touch with the lecturer at the beginning of the course in order to agree on possible adaptations in the examination test that, while respecting the educational objectives of the teaching, take into account their specific learning modalities and provide for adequate compensatory tools.
