Background

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In recent years, artificial intelligence (AI) has been deeply integrated with customer service and supply chain optimization, reshaping the e-commerce with great changes. Based on its powerful analysis competence and excellent learning ability, AI presents opportunities to gain useful insights from big data, promote information management, and perform several tasks autonomously that were previously performed by humans. For example, AI-powered recommendation systems and chatbots can help to provide precise, convenient, and personalized customer services (Hoyer, Kroschke, Schmitt, Kraume, & Shankar, 2020), thus improving customers’ experience (Ameen, Tarhini, Reppel, & Anand, 2021) and employees’ work efficiencies. Although increasing research focusing on AI-powered E-Commerce has explored the impact of AI on the behavior of customers and employees, the emerging Generative Artificial Intelligence (GAI) such as ChatGPT (Paul, Ueno, & Dennis, 2023) is calling into question our existing assumptions due to the tendency of transforming from weak AI to strong AI.

 

Given the ubiquitous use of AI in e-commerce today, there are many fascinating phenomena and research questions that are understudied. For example, the horrendously accurate recommendation algorithms may bring information cocoons problem (Nechushtai & Lewis, 2019), limiting customers obtain diverse information. It may also give customers a sense of being monitored, raising privacy concerns. For employees, AI increasingly threatens their work processes and even replace their jobs (Mirbabaie, Brünker, Möllmann (Frick), & Stieglitz, 2021). These phenomena highlight the significant negative or detrimental consequences of AI to customers, employees, and organizations that are worthy of further research attention. Therefore, in the context of AI-powered e-commerce, we call for a need to understand the role of AI’s characteristics like autonomy, to investigate the resources and mechanisms of the ethics issues like privacy, and to explore how better human-AI interactions and information management can be achieved by interaction designs.

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Possible Topics of Submissions

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Based on this context, the topics of this special issue tentatively contain, but are not limited to the following areas:

• AI-powered information management, analysis, and optimization

• Customer behaviors and experience in AI-powered e-commerce

• Employee behaviors and performance in human-AI collaboration

• Organization strategies and business model innovations in AI-powered e-commerce

• Design for human-AI interaction

• Explainability, robustness, responsible AI in e-commerce

• Algorithmic bias and fairness issues

• Information cocoons and elimination

• Ethics issues (e.g., trust, privacy, and accountability) and governance

• The impact of GAI in e-commerce

• Emerging e-commerce and information management issues in the age of AI

• AI and platform economy

• The social and regulations issues related to AI

• Intelligent applications in industries (e.g., healthcare, e-commerce, and manufacturing)

 

Data Science and Management

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Data Science and Management (DSM) is a quarterly SCOPUS Indexed international journal. It is also on the journal list of FMS Journal Rating Guide. DSM is a peer-reviewed journal for original research articles, review articles and technical reports related to all aspects of data science and its application in the field of business, engineering, and social management. For further information, refer to the Science Direct at https://www.sciencedirect.com/journal/data-science-and-management.

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Forms of Submission

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This special issue will consist of:

(1) The papers from an open call selected from the ICEC2024 (International Conference on Electronic Commerce 2024) as posted at http://www.icec.net;     

(2) Voluntary submitted papers according to the due date; and

(3) Invited papers that are requested from the editorial members.

All submitted papers and invited papers will go through peer review.

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The submission due date for ICEC2024 is February 28, 2024, and conference dates are May 29-31, 2024 at Seoul as posted at https://www.icec.net/cfp.

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Submission Instruction

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Manuscripts are recommended to submit to ICEC 2024 submission systems first. Then, accepted manuscripts will be selected and invited to revise to submit via the fast-track submissions system of Data Science and Management (https://www.editorialmanager.com/dsm/default2.aspx). The submission schedule of revised papers and new voluntary and invited submission are:

• Submission Deadline: 31th July, 2024.

• Notification of first round reviews and acceptance: 30th September, 2024.

• Revised manuscripts due: 30th November, 2024.

• Notification of second round reviews and acceptance: 15th January 2025.

• Final manuscript due: 28th February, 2025

• Last date for final acceptance: 15th April, 2025

 

Guest Editors

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Zhaohua Deng (zh-deng@hust.edu.cn)

Huazhong University of Science and Technology, China

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Zhaohua Deng is a Professor of School of Management at Huazhong University of Science and Technology.  She serves as an Associate Editor of Data Science and Management. Her research interests include AI, information management, e-health, and knowledge management. Her research has appeared in journals such as Information Systems Journal, Information & Management, Journal of Association of Information Systems and Technology, International Journal of Information Management, Information Systems Frontiers.

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Shan Liu (shanliu@xjtu.edu.cn)

Xi’an Jiaotong University, China

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Shan Liu is a Professor at the School of Management in Xi’an Jiaotong University. His research interests focus on artificial intelligence, IT-enabled supply chain management, and platform economy. He has published more than 80 refereed publications including papers that have appeared in Journal of Operations Management, Information Systems Journal, European Journal of Information Systems, European Journal of Operational Research, Information & Management, and IEEE Transactions on Engineering Management. He currently serves as an Executive Editor of Data Science and Management, and Associate Editor of Decision Support Systems and Electronic Commerce Research and Applications.

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Kyoung Jun Lee (klee@khu.ac.kr)

Kyung Hee University, South Korea

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Kyoung Jun Lee is a professor of AI and Business at Kyung Hee University in Seoul, South Korea. He is the director of Research Institute of UCAI (User-Centric AI) Forum and the Humanitas Big Data Research Center. Lee received his B.S., M.S., and Ph.D. degrees in Management Science from the Korea Advanced Institute of Science and Technology (KAIST), as well as a M.S. and Ph.D. in Public Administration from Seoul National University. Lee has won the Innovative Applications of Artificial Intelligence Awards from the American Association for Artificial Intelligence (AAAI) in 1995, 1997, and 2020. He has also served as a visiting scientist and professor at Carnegie Mellon University, the Massachusetts Institute of Technology, and the University of California at Berkeley. Lee was the President of the Korean Intelligent Information Systems Society in 2017. He is a Vice President of the Korean Academic Society for Business Administration and the Korean Management Information Systems Society. He received 2018 Presidential Award for his contribution to e-government of Korea. He was a member of the Government 3.0 Committee of Korea government. He advised LG CNS, MINDS Lab, Harex Infotech, Riiid, Naver, Samsung C&T, LG Electronics, Samsung Electronics, KT, SK Telecom, and BC Card etc. He is now the 4th Industrial Revolution Policy Advisor for the Busan city. He selected the Korea AI Start-up 25 in 2020, and has been serving as the chair of the Selection Committee for Korea AI Start-up 100 in 2021 and 2022.

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Haibing Lu (hlu@scu.edu)

Santa Clara University, USA

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Haibing Lu is a Professor and Department Co-Chair of Information Systems & Analytics at Santa Clara University. He joined Santa Clara University in fall 2011, right after receiving his Ph.D. in Management (Information Technology) from Rutgers University. He earned his B.S. and M.S. degrees both in mathematics from Xi’an Jiaotong University, China, in 2002 and 2005 respectively. He is a frequent recipient of the SCU Leavey Business School’s Extraordinary Research, Teaching and Service Awards. He has expertise in information privacy & security, and data analytics. He is particularly interested in applying state-of-art technologies to real-world problems, e.g., private keyword search in cloud computing, reinforcement learning for sales representative engagement, analytics for smart energy, privacy-preserving textual analytics, machine learning fairness. He has published over 50 well-cited technical papers in leading journals, including IEEE Transactions on Dependable and Secure Computing, IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Big Data, ACM Transactions on Management Information Systems, INFORMS Journal on Computing, Manufacturing Service Operations Management, and premium computer science conferences, including KDD, S&P, ICDM, and ICDE. His research has been supported by companies and organizations, e.g., GEIRI North America, Ultimate Software, AKTANA, and Markkula Center. His research is reported by the United Nations, Forbes, and WIRED Magazine.

 

References

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Ameen, N., Tarhini, A., Reppel, A., & Anand, A. (2021). Customer experiences in the age of artificial intelligence. Computers in Human Behavior, 114(September 2020), 106548. https://doi.org/10.1016/j.chb.2020.106548

Hoyer, W. D., Kroschke, M., Schmitt, B., Kraume, K., & Shankar, V. (2020). Transforming the Customer Experience Through New Technologies. Journal of Interactive Marketing, 51, 57–71. https://doi.org/10.1016/j.intmar.2020.04.001

Mirbabaie, M., Brünker, F., Möllmann (Frick), N. R. J., & Stieglitz, S. (2021). The rise of artificial intelligence – understanding the AI identity threat at the workplace. Electronic Markets, (0123456789). https://doi.org/10.1007/s12525-021-00496-x

Nechushtai, E., & Lewis, S. C. (2019). What kind of news gatekeepers do we want machines to be? Filter bubbles, fragmentation, and the normative dimensions of algorithmic recommendations. Computers in Human Behavior, 90(January 2018), 298–307. https://doi.org/10.1016/j.chb.2018.07.043

Paul, J., Ueno, A., & Dennis, C. (2023). ChatGPT and consumers: Benefits, Pitfalls and Future Research Agenda. International Journal of Consumer Studies, 47(4), 1213–1225. https://doi.org/10.1111/ijcs.12928