Wei Xu, Renmin University of China,
Email: [email protected]
Jianshan Sun, Hefei University of Technology
Email: [email protected]
Mengxiang Li, Hong Kong Baptist University
Email: [email protected]
Overview of Special Issue
In the era of big data, the proliferation of online behavior data enables the development of profound implications for both the scholars and practitioners alike in enhancing the effectiveness of business operations. Online behavior data varies in forms and quantities, thus signifying the importance of the application of advanced analytics approach to process data and generate meaningful results.
As one of the promising advanced analytical techniques, AI-enabled analytics with big data has gained notable attention in various fields. However, there is still a lack of research in examining interpretable AI-enabled data analytics in the extant literature. Thus, it is imperative to investigate the interpretable AI-enabled online behavior analytics because data analytics without creating an interpretable model/value/approach are difficult to make significant contributions and actionable implications to the field. Interpretable AI-enabled online behavior analytics should make direct benefits (Lau et al., 2018) or provide competitive advantages (Timoshenko and Hauser, 2019) to the stakeholders. The call for research on interpretable AI and the related application has also been echoed in other fields such as computer science (Rudin, 2019), and healthcare (Jia et al., 2019).
Therefore, the aim of this special issue is to deepen and broaden the current understanding of the embedded business value of the interpretable AI-enable analytics with online behavior data. The focus is on how the AI-enabled online behavior analytic methods are applied for supporting business operations, as well as how to demonstrate the real impact of AI-enabled online behavior analytics. We are interested in interpretable AI-enabled online behavior analytics in various contexts (e.g. online social media, e-commerce, and digital government), and its main impact (e.g., users’ reactions, customers’ experience, and government policy). All theoretical and empirical (both qualitative & quantitative) approaches are equally appreciated, and we particularly welcome multidisciplinary and interdisciplinary submissions that cover different issues relevant to management, marketing, finance, and communication.
Topics of interest include, but are not limited to:
• The benefits and challenges of interpretable AI on online behavior analytics
• The impact of online behavior analytics on user decision making
• The impact of online behavior analytics on customers’ experience
• The impact of online behavior analytics on social governance
• The impact of online behavior analytics on business innovation
• The dark side of using interpretable AI on online behavior analytics
• The efficiency and effectiveness of interpretable AI-enabled online behavior analytics
• The particular technologies (e.g., blockchain, big data, deep learning) and online behavior analytics
• Business redesign through interpretable AI-enabled online behavior analytics
• Interpretable AI-based online behavior prediction
• Interpretable AI-based online recommendation
• Multimodal-based online behavior analytics
• Social media analytics for online behavior
Deadline and Submission Details
*** We recommend prospective author(s) submit abstract prior to the full paper submission deadline. The submission of abstract is fully optional, and it will not affect editorial decisions afterward. ***
Full Paper Submission: August 30th, 2020
Author Notification: October 30th, 2020
Revised Version: December 30th, 2020
Final Notification: February 26th, 2021
Camera Ready Version: March 30th, 2021
Abstract format and submission
If the prospective author(s) intend to submit abstract to the guest editor(s), they shall provide the following items in the abstract: title, author(s), 1-page synopsis of the content of the article including methodology and results.
For submission, prospective author(s) are advised to submit the abstract to Dr. Mengxiang Li ([email protected]) via email.
View the author guidelines on the journal's page.
Please submit your manuscript via our review website
Editorial Review Board
Haider Abbas - Associate Professor, National University of Sciences and Technology, Pakistan
Abhijith Anand – Assistant Professor, University of Arkansas, USA
Kaigui Bian - Associate Professor, Peking University, China
Yiyang Bian - Assistant Professor, Nanjing University, China
Tingru Cui - Senior Lecturer, University of Melbourne, Australia
Hepu Deng - Professor, RMIT University, Australia
Danhuai Guo - Associate Professor, Chinese Academy of Sciences, China
Daning Hu - Associate Professor, Southern University of Science and Technology, China
Eric T.K. Lim - Senior Lecturer, University of New South Wales, Australia
Libo Liu - Lecturer, University of Melbourne, Australia
Ou Liu - Associate Professor, Aston University, UK
Qi Liu - Associate Professor, University of Science and Technology of China, China
Xiao Liu - Associate Professor, Deakin University, Australia
Yi Liu - Associate Professor, Rennes Business School, France
Wei Shang - Associate Professor, Chinese Academy of Sciences, China
Jun Shen - Associate Professor, University of Wollongong, Australia
Thushari Silva - Associate Professor, University of Moratuwa, Sri Lanka
Xiaohui Tao - Associate Professor, University of Southern Queensland, Australia
Chong Wang - Associate Professor, Peking University, China
Hai Wang - Professor, Saint Mary's University, Canada
Kanliang Wang - Professor, Renmin University of China, China
Ji Wu - Associate Professor, Sun Yat-sen University, China
Jin Xiao - Professor, Sichuan University, China
Jun Yan - Associate Professor, University of Wollongong, Australia
Zhijun Yan - Professor, Beijing Institute of Technology, China
Ji Zhang - Associate Professor, University of Southern Queensland, Australia
Yin Zhang - Associate Professor, Zhongnan University of Economics and Law, China
 Jia, X., Ren, L., & Cai, J. Clinical implementation of AI technologies will require interpretable AI models. Medical Physics, 2019.
 Lau, R., Zhang, W., & Xu, W. Parallel aspect-oriented sentiment analysis for sales forecasting with big data. Production and Operations Management, 2018, 27, 1775-1794.
 Rudin, C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature: Machine Intelligence, 2019, 1(5), 206-215.
 Timoshenko, A., & Hauser, J. R. Identifying customer needs from user-generated content. Marketing Science, 2019, 38 (1), 1–20.