Prof. Thang Le Dinh
Department of Marketing and Information Systems
University of Québec a Trois-Rivieres, Canada
E-mail: thang.ledinh@uqtr.ca


Dr. Thanh Thoa Pham Thi
School of Management
Technological University Dublin, Ireland
E-mail: thoa.pham@tudublin.ie


Dr. Cuong Pham-Nguyen
Information Systems Department
University of Science, Hochiminh city, Vietnam
E-mail: pncuong@fit.hcmus.edu.vn

Smart services are emerging as the evolution of service solutions in the big data era. Smart services have the capacity of selfdetecting of user needs and self-adaptation to user needs to render the services to users without explicit user requests. In this respect, user contexts, practices and knowledge play a key role to enable smart services. Big data, Cloud computing and the Internet of Things provide a huge source of knowledge that allows to determine user contexts, including location contexts, social contexts or personal contexts. The more user contexts are determined; the more accurate and suitable smart services are rendered. The challenges are how to select and gather information and knowledge that relate to their different contexts, and how to organize them in a way that can help enabling smart services. Knowledge-intensive smart services can be a potential solution that re-conciliates the service science perspective and collective intelligence perspective to design and implement smart services.

The KISSTA 2022 Special Session at the 14th International Conference on Computational Collective Intelligence (ICCCI 2022) aims at expanding our knowledge regarding the adoption of knowledge-intensive smart services and their applications in organizations to accelerate the digital transformation. This session provides a forum for exchanging research ideas and best practices related to new models, technologies and applications of smart services within the context of digital transformation. The scope of the KISSTA 2022 includes, but is not limited to the following topics:

• Theory, approaches and applications for design, development and deployment of knowledge-intensive smart services
• Smart services for industry 4.0
• Enabling smart services with collective intelligence
• Predicting user intentions
• Self-detecting, geolocation-based services
• User knowledge management, user context in knowledge-intensive smart rvices
• Enabling smart services with Big data, Cloud computing and the ternet of Things
• Smart service evolution and adaptation
• Smart services, smart service systems and value co-creation networks
• Artificial intelligence for a smart world, smart cities and smart mmunities
• Smart services for crisis management