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CALL FOR PAPERS
Special Issue on “AI-Driven Decision Sciences”
Submission Deadline: August 30, 2023
Special Issue Editors
Meng Li (
mli@bauer.uh.edu), University of Houston
Chao Liang (
chaoliang@ceibs.edu), China Europe International
Business School
Paul A. Pavlou (
pavlou@central.uh.edu), University of Houston
Artificial Intelligence (AI) is becoming the new operational
foundation of business that has transformed the very nature of how
companies operate and how they compete (Iansiti and Lakhani,
2020). AI isbelieved to facilitate smart services and automate
tasks traditionally performed by humans. As AI enables companies
to reach unprecedented levels of scale, scope, and learning speed,
organizations around the world are eager to participate in this AI
transformation. However, the rise of AI is posing new challenges
for business decision-making to understand how it works, when it
is the most powerful, and how to optimize their AI strategies.
AI has created new business opportunities and facilitated business
decision-making in numerous ways. For example, a chatbot is an AI
application that can automate basic, repeatable, standardized
interactions between customers and sellers. For instance, chatbots
such as Amazon’s Alexa and IKEA’s Anna use voice or texts to
automate communications and create personalized customer
experiences. The market size of chatbots expanded from $250
million in 2017 to an estimated $1.34 billion by 2024 (Pise,
2018), and the adoption of a chatbot is estimated to save
businesses $11 billion in annual costs by 2023 (Hampshire, 2018).
AI has also greatly impacted the procurement process in
business-to-business markets with automation and AI-assisted
sourcing decision making, which is referred to as cognitive
procurement (Loo and Santhiram, 2018). Surveys reveal that AI has
been adopted to automate the request-for-quotation process in 60%
of companies and to recommend new suppliers in 50% of companies
(Tata Consultancy Services, 2016).
Regardless of the numerous opportunities that AI offers, there are
many challenges for AI that presents enormous risks for business
decision-making. From an individual perspective, the accuracy of
AI algorithms is highly dependent on the training and analysis of
mass user data, and it needs to acquire a large amount of user
personal information to provide personalized and customized
services, thus increasing the risk of personal/individual
information leakage. From the firm’s perspective, the AI-driven
business decision-making surroundings will become more complex,
which raises the mistakes and variability for managers in the
realm of practical management decision-making process, thus
generating a certain impact on the healthy and stable development
of enterprises. Moreover, how to measure the value of and then
justify the AI adoption from the very beginning is also of great
interest to companies.
From a societal perspective, AI could potentially widen the gap amongst emerging and developed markets. The issue of potential job losses due to AI technologies has also received widespread attention. Therefore, considering the ubiquitous use of AI in digital business today, the significant beneficial or detrimental consequences of AI to operations decision-making remain to be examined and are worthy of further research attention.
In the academic literature, AI has attracted some initial
attention in business decision making with respect to its possible
applications in the field of operations management (e.g., Chen et
al., 2022), business analytics (e.g., Cui et al., 2022), marketing
(e.g., Simester et al., 2020), risk management (e.g., Araz et al.
2020), and financial management (e.g., Wu et al., 2022; Chod et
al., 2020). However, relative to the potential in business
practice, there is a relative dearth of academic research on AI,
particularly in decision sciences. We thus organize this special
issue of Decision Sciences Journal and encourage authors to
address this important but so far understudied topic in the
decision sciences – AI-Driven Decision Sciences. The main
objective of this special issue is to create a platform to address
the “AI-driven decision sciences” in digital networked business,
including social networks, electronic commerce, digital platforms,
procurement management, and intelligent manufacturing. Submissions
adopting multi-methodologies from individual, organizational,
and/or societal perspective, including analytical, empirical,
simulation, and behavioral approaches, are strongly encouraged.
Possible contributions may include, but are not limited to, the
following topics:
1. Multi-method research on decision quality, decision efficiency
and decision precision driven by AI.
2. Marketing and operations decision-making interaction driven by
AI.
3. Business operations model innovation driven by AI.
4. Business intelligence and digital transformation driven by AI.
5. Demand forecasting and customized services driven by AI.
6. Global sourcing management and risk control driven by AI.
7. Operations decision-making of public health driven by AI.
8. New challenges in human resource management caused by AI.
9. Intelligent manufacturing aided by AI.
10. Coordination between human-workers and
AI-assisted-robot-workers.
11. AI application in Industrial Internet.
12. Logistics management with AI.
13. Optimization of warehouse operations aided by AI.
14. Ethics and social supervision of business decision-making
driven by AI.
15. The potential harm engendered by the widespread use of AI in
business decision-making.
16. The measurement and balance between the benefits and the costs
of using AI in business decision-making.
17. Approaches to regulating and controlling dark side behaviors
and practices associated with AI usage in business
decision-making.
18. Studies on opportunities and challenges of AI in other
business decision-making settings.
The special issue co-editors welcome authors to contact them
directly to discuss other possible topics for their suitability to
the special issue. However, all submissions must fit the Journal’s
goal statements. All submissions will go through the same rigorous
review process as regular submissions to the Journal. The journal
has very specific expectations regarding submissions: “as of
January 1, 2020, we ask all researchers who prepare manuscripts
(via our Manuscript Preparation Guide and our first step in our
submission page) to visit with industry, practitioners, retailers,
users, consumers, government executives, and other constituents
and acquire first-hand knowledge of the respective
problems/challenges, conditions, assumptions, performance
measures, etc. This can be a very insightful exercise as it can
help motivate the specific topic and it can render more
credibility to the authors’ work. Who did you talk to that served
as motivation for this inquiry? What insight did you attain from
those discussions as it pertains to the realm of your study?
Manuscripts that are not motivated via direct interactions with
relevant constituents are typically “Desk Rejected” based on our
policy. Note that we are not asking for a full empirical inquiry
here just to motivate the topic or attain model assumptions etc.
We are looking for enough information that demonstrates that the
inquiry is driven by real challenges that keep the relevant
constituents at a quandary. The research would be more convincing,
and thus more suitable for our journal, if this inquiry benefited
from direct interactions that served as motivation as well as a
source for insights and assumptions.” Authors are expected to
document their direct interactions at such places as the
introduction to drive motivation, the assumptions/parameters used
for analytical purposes, and the discussion section for any
insights.
Manuscript Preparation and Submission
To prepare manuscripts, authors are asked to closely follow the
Author Guidelines
https://onlinelibrary.wiley.com/page/journal/15405915/homepage/forauthors.html
and
Manuscript Preparation Guide
https://mc.manuscriptcentral.com/societyimages/dsj/Decision%20Sciences%20Journal%20Manuscript
%20Preparation%20Guide_April%2022%202020.docx
Papers should be submitted via the Manuscript Central portal
(
https://mc.manuscriptcentral.com/dsj) no later than August 30,
2023 and designated as a “Special Issue” manuscript. In Step 1 of
the submission, authors should select “Special Issue” when asked
to select an “appropriate department,” and then select Professor
Meng Li as editor in Step 5. Submitted papers should not have been
previously published nor should they be currently under
consideration for publication elsewhere.
Any inquiries should be addressed to: Dr. Meng Li, University of
Houston,
mli@bauer.uh.edu
References
Araz O., Choi. T., Olson, D., & Salman, F. (2020). Role of
Analytics for Operational Risk Management in the Era of Big Data.
Decision Sciences, 51(6) 1320–1346.
Chen, X., Owen, Z., Pixton, C., & Simchi-Levi, D. (2022). A
Statistical Learning Approach to Personalization in Revenue
Management. Management Sciences, 68(3) 1923–1937.
Chod, J., Trichakis, N., Tsoukalas, G., Aspegren, H., & Weber,
M. (2020). On the Financing Benefits of Supply Chain Transparency
and Blockchain Adoption. Management Sciences, 66(10), 4378–4396.
Cui, R., Li, M., & Zhang, S. (2022). AI and Procurement.
Manufacturing & Service Operations Management, 24(2), 691–706.
Hampshire. (2018). Chatbots to Deliver $11bn in Annual Cost
Savings.
www.juniperresearch.com/press/press-releases/chatbots-to-deliver-11bn-cost-savings-2023<http://www.juniperresearch.com/press/press-releases/chatbots-to-deliver-11bn-cost-savings-2023>
(accessed, May 9, 2022).
Iansiti, M., & Lakhani, K. R. (2020). Competing in the Age of
AI: Strategy and Leadership when Algorithms and Networks Run the
World. Harvard Business Review Press, MA.
Loo, S. K., & Santhiram R. R. (2018). Emerging Technologies
for Supply Chain Management. WOU Press, Malaysia.
Pise, R. (2018). Chatbot Market Size is Set to Exceed USD 1.34
Billion by 2024.
www.clickz.com/chatbot-market-size-is-set-to-exceed-usd-1-34-billion-by-2024/215518
(accessed, May 9, 2022).
Simester, D., Timoshenko, A., & Zoumpoulis S. (2020).
Targeting Prospective Customers: Robustness of Machine-Learning
Methods to Typical Data Challenges. Management Sciences, 66(6),
2495–2522.
Tata Consultancy Services. (2016). Getting Smarter by the Day: How
Artificial Intelligence is Elevating the Performance of Global
Companies.
http://sites.tcs.com/artificial-intelligence/wp-content/uploads/TCS-GTS-how-AI-elevating-performance-global-companies.pdf
(accessed, May19, 2022).
Wu, W., Chen, J., Yang, Z., & Tindall M.L. (2021). A
Cross-Sectional Machine Learning Approach for
Hedge Fund Return Prediction and Selection. Management Sciences,
67(7), 4577–4601.
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