Track Co-Chairs

Yaobin Lu
Professor
luyb@mail.hust.edu.cn
Huazhong University of Science & Technology, China

 

 

 

Ling Zhao
Associate Professor
lingzhao@mail.hust.edu.cn
Huazhong University of Science & Technology, China

 

 

 

Jiang Wu
Professor
jiangw@whu.edu.cn
Wuhan University

 

 

 

 

Brief Introduction

This track addresses emerging business innovations driven by artificial intelligence (AI). As a transformational technology, AI is now leading a new round of business innovation. It becomes quite common for individual consumers to access AI-driven products or services in their daily life, e.g., Intelligent Personal Assistant (IPA), Intelligent Customer Service and AI based education. Meanwhile, other typical applications of AI, such as facial recognition, natural language processing, are also widely applied. For companies who embrace AI for business innovation, are facing challenges from various aspects.
Some challenges are from the way how to utilize and mange AI in business innovation. It not only involve technical problems, but also complex managerial decisions (e.g., cost and benefit calculation). For instance, service innovation enables by AI implies the way how services designed, delivered and evaluated would be quite different with traditional services. Though AI implies more personalized recommendation and less human cost based on sophisticated algorithms from one side, increasing algorithm training cost and changing consumers’ behavior are still challenges from the other side. Moreover, organizations that adopt AI technologies also have to deal with issues related to allocation of AI and human resources, managerial challenges caused by synergy and dissynergy between AI and human employees.
Challenges from the environment, such as other competitors and policy uncertainty, are also critical issues that the companies need to take into account. For example, the implementation of GDPR, pandemic of the COVID-19 and the uncertain trend of globalization after the pandemic, provides both opportunities and risks for those companies.
Consumers are always the source of major challenges for those companies. For example, as Intelligent Customer Service are adopted by more and more companies to provide services, consumers have to interact with AI robots rather than human service personnel. Thus, it 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.
Issues related to IT ethics are the challenges that are not so easy to conquer for the companies, especially in the AI context. Privacy and information security become more serious than ever, cause vast personal data are collected from the consumers to improve algorithm accuracy from one side, while it also increase the vulnerabilities of consumers due to the possibility of privacy leak from the other side. Thus, it is a critical problem needed to be addressed by the innovated companies, and also the policy makers. In addition, the application of AI also increases concerns about AI explainability, rights of robot, discrimination caused by algorithm bias, and so on.
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.

 

Topics

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

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