Challenges and Opportunities of Integrating Service Science with Data Science and Artificial Intelligence

Dear Colleagues,

The global economy and numerous organizations are evolving to become more service-oriented. More research has recently been conducted on the provision of services, particularly with a cross-disciplinary approach. Beyond the service-oriented architecture (SOA), scientific knowledge from various domains is essential to achieve service excellence for the increasingly complex requirements of a rapidly evolving global environment, including computer science (CS), data science (DS), artificial intelligence (AI), industrial and systems engineering, management sciences, marketing sciences, as well as culture transformation and integration methods based on beliefs, assumptions, principles, and values among different organizations and humanities. In particular, with the current facilitation of big data and massive data integration opportunities, data science has fueled AI with the necessary sources of information as the basis for reasoning, while AI has provided the analysis techniques for DS to excel. Much of such cross-disciplinary knowledge has been widely applied for the excellence of service provision, which is currently one of the biggest global business sectors that emphasizes segmentation, customization, and personalization.

This Topic intentionally seeks scientists, engineers, educators, industry people, policymakers, decision-makers, and others with insight, vision, and an understanding of the significant challenges and opportunities involved. We also aim to help communicate and disseminate relevant recent research across disciplines, cultures, communities, and applications. This Topic welcomes an array of approaches and epistemologies, including qualitative, quantitative, and mixed methods, as well as established methodologies such as action, participatory, evaluation, design, and development. Topics of interest include, but are not limited to, the following:

• Big data and social media analytics for service science;
• Intelligent analytics for business, marketing, logistics, organizations, and customer services;
• Socio-data analytics, bibliometrics, ontology, and linked data;
• Sustainability issues with data science and service science;
• Data science applications in education, social sciences, humanities, and other emerging domains;
• Intelligent analytics and knowledge engineering;
• Recommendation systems, information retrieval, and reputation/rating systems;
• Data-driven technology innovation and service design;
• Digitalization for intelligent analytics;
• Machine learning, neural networks, deep learning, and large language models for service excellence;
• Data Science for the Internet of Things, blockchain, the cloud, service computing, and other emerging service paradigms;
• Adoption, diffusion, applications, innovations, management, and governance of data science and service science;
• Security, privacy, reliability, education, information integrity, system development, adoption, and policies in data science and service science;
• Large Language Models (LLM) approaches to big data processing and analytics.

Dr. Dickson K. W. Chiu
Dr. Stuart So
Topic Editors