Track Co-Chairs

Yaobin Lu
Huazhong University of Science & Technology, China




Ling Zhao
Associate Professor
Huazhong University of Science & Technology, China




Jiang Wu
Wuhan University




Brief Introduction

This track addresses emerging business innovations driven by Artificial intelligence & IoT(AIoT). Typical applications of AI, such as facial recognition, natural language processing, are widely applied in various products or services for consumers. For companies who embrace AI for business innovation, are facing challenges while exploiting the potential of this new technology.
These challenges are from the way how to utilize and mange AI in business innovation. For example, organizations that adopt AI technologies need to deal with issues related to allocation of AI and human resources, managerial challenges caused by synergy and dissynergy between AI and human employees. The application of AI also raises new research questions related to changing consumer behavior under AI context, e.g, how consumer interact with AI, how consumer make decisions assisted by AI, how AI persuade consumers, how to evaluate and improve the AI service quality, and so on. Moreover, privacy and information security become more serious than ever, cause vast personal data are collected from the consumers to improve AI algorithm accuracy from one side, while it also increase the vulnerabilities of consumers due to the possibility of privacy leak from the other side. In sum, it not only involves technical problems, but also complex managerial decisions, consumers experience, IT ethics issues, human-AI collaboration and etc.
Thus, this track focuses on the research questions related to business innovations enabled by AI. We welcome research from any empirical and theoretical standpoint. We welcome research that uses a wide variety of methods, including qualitative methods, large-scale data analysis, surveys, digital field experiments, simulations and multi-methods. We are particularly interested in papers that raise interesting questions and challenge current IS related conceptualizations which might not be applicable in the AI context.


1. Exploratory research of typical cases of AI enabled business innovation
2. Theories and methodologies of AI enabled service/product innovations, including smart product development and software development
3. Data analysis methods and algorithms in the context of AI enabled business innovation
4. Consumer psychology and behavior in AI context, e.g., human-machine interactions, consumer decision making assisted by AI
5. Paradoxical effects of AI technologies on organizational activity
6. Emergence of collaboration between human service staff and AI and their impact on working and organizing
7. AI explainability in AI enabled business innovation
8. Emergence and evolution of platforms, ecosystems, and markets shaped by AI technologies
9. The competition and collaboration between firms in the AI-driven ecosystems
10. Privacy and information security in the context of AI enabled business innovation
11. Ethical and moral issues in AI enabled business innovation