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Special issue call for papers from Internet Research
Guest Editors
Wei Xu, Renmin University of China,
Email:
weixu@ruc.edu.cn
Jianshan Sun, Hefei University of Technology
Email:
sunjs9413@hfut.edu.cn
Mengxiang Li, Hong Kong Baptist University
Email:
mengxiangli@hkbu.edu.hk
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. ***
Abstract Submission: February 1st, 2020 (optional)
Abstract Notification: March 1st, 2020 (optional)
Full Paper Submission: June 1st, 2020
Author Notification: August 1st, 2020
Revised Version: October 1st, 2020
Final Notification: November 1st, 2020
Camera Ready Version: December 1st, 2020
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 (
mengxiangli@hkbu.edu.hk) via email.
Submission Details
To view the author guidelines for this journal, please visit:
https://www.emeraldgrouppublishing.com/products/journals/author_guidelines.htm?id=intr
Please submit your manuscript via our review
website:
https://mc.manuscriptcentral.com/intr
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
References
[1] Jia, X., Ren, L., & Cai, J. Clinical implementation of AI
technologies will require interpretable AI models. Medical
Physics, 2019.
[2] 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.
[3] 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.
[4] Timoshenko, A., & Hauser, J. R. Identifying customer needs
from user-generated content. Marketing Science, 2019, 38 (1),
1–20.
Best Regards,
Mengxiang
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