Call for Papers TBMS ̓ 2025
3d. International Symposium TBMS’2025 on:
Big Data Analytics Technologies for Strategic Management :
Innovation and Competitiveness.
in OCTA’2025 Multi-Conference Event
November 6-7, 2025 Tunis (Tunisia)
| Important Dates | Deadline |
|---|---|
| Paper Submission Deadline | August 7th, 2025 |
| Notification of Acceptance | September 7th, 2025 |
| Final Paper & Camera-Ready Submission | October 7th, 2025 |
| Author Registration Deadline | October 15th, 2025 |
| TBMS’2025 in OCTA’2025 Multi-Conference Dates | November 6-7, 2025 |
| Best Paper Awards Ceremony | November 7th, 2025 |
TBMS’2025 “ Big-Data-Analytics Technologies for Strategic Management ” :
Innovation, and Competitiveness.
Governments, companies, and organizations are undergoing significant transformations in response to the vast amounts of information generated in today’s digital age. This marks the advent of Big Data and, more recently, Generative Artificial Intelligence (Generative AI). Various economic, industrial, and social actors are fundamentally restructuring their activities around the management and utilization of Big Data, now enhanced by AI-driven generative models. Examples include the emergence of the “Chief Data Officer” in the United States, the “Chief Digital Officer” responsible for the “Government Digital Service” in the United Kingdom, and the “General Data Administrator” in France. These public administrators play a crucial role in ensuring that data is leveraged as a valuable resource, representing both tangible and intangible growth opportunities that have been either unexplored or only partially utilized.
The academic world has embraced Big Data and Generative AI with even greater enthusiasm. Following in the footsteps of pioneering institutions such as the Massachusetts Institute of Technology’s (MIT) Media Lab (established in 1985), many universities have established dedicated research centers focused on the evolving field of Data Science and AI. In the humanities and social sciences, the emergence of “Big Data and Society” seeks to analyze Big Data and AI-driven advancements and their impact on societal structures. These developments highlight the practical implications of Big Data and Generative AI, reconfiguring relationships, expertise, methodologies, concepts, and academic knowledge across various sectors, including social, professional, and business domains.
While Big Data, AI and Generative AI have garnered significant interest, they have also sparked multidisciplinary debate. Not everyone shares the enthusiasm of its proponents. The repeated disclosures of mass surveillance programs have raised concerns in both academic and civil society circles regarding the potential risks associated with these technologies. AI and Big Data can quickly evolve into powerful governance tools, serving economic, political, and ideological interests in an instrumental manner.
We now have access to more data than at any other point in human history. Between 1987 and 2007, global data volumes increased a hundredfold and have since doubled annually on average. This expansion surpasses even the transformative impact of the printing press, which led to a doubling of available data over a period of 50 years.
Comprehensive analysis of extensive datasets, powered by Generative AI, has the potential to fundamentally reshape our understanding of the world. The rise of Big Data and AI contributes to advancing reason and rationality in an increasingly complex environment. Traditionally, the scientific method relies on deriving concrete hypotheses from abstract theories, which are then tested using empirical data. Big Data and AI challenge and extend these epistemological foundations, not to undermine scientific rationality, but to elevate it to a more complex, comprehensive, and accurate interpretation of reality.
In this transdisciplinary context, Big Data and Generative AI enable researchers and practitioners to move beyond predefined analytical categories, allowing data itself to reveal patterns and classifications that more accurately reflect reality.
