Artificial Intelligence (AI) for management decision-making processes: From measurement to strategy
The topic of Artificial Intelligence (AI) is often debated by researchers worldwide. The subject of business, management & accounting is not excluded from this debate. In fact, following the approach developed by Massaro et al. (2016) and applied by Secinaro et al. (2021), we used the keywords: "Artificial Intelligence" and "Decision-mak*” searching within the abstract, title, and keywords of management-related articles. A total of 1,612 articles were identified. The literature shows an increase in the topic in the last three years (2019, 2020, and 2021), and most of the published articles show the potential of AI to support strategic decisions.
In recent years, strategic decision-making has hybridized with technology (Garousi Mokhtarzadeh et al., 2020). New computing capabilities, the availability of new data sources, as well as the decreasing cost of technological tools, can provide an increasing number of users with novel decision variables and processes, and AI is one of the most promising technologies (Secinaro et al., 2021). According to Shrestha et al. (2019), AI creates necessary changes in the decision-making process by supporting new ways of identifying the key variables of the specific decision space, the interpretation of the decision-making process and outcome, the analysis of alternatives, the speed of the decision, and replicability of the process reducing time and costs which opens exciting avenues for research and practice scenarios.
AI technologies enhance the decision-making process of managers, but they might also be used to provide new products and services to customers. For example, in the case of personalized financial planning and banking facilities, AI might provide customer decision support. Interestingly, these new opportunities represent an unexplored research context to deepen the role of know-how. The data collected and analyzed by companies are becoming part of the value offered to the customer (Bagnoli et al., 2019). Similarly, AI also imposes new research opportunities in terms of customers’ relationships. For example, in the retail industry, the entire online and physical shopping experience is being revolutionized (Grewal et al., 2017) as AI prediction algorithms enable companies to project demand accurately and reach the right target customers through new forms of distribution and communication channels, such as personalized marketing and advertising (Talwar & Koury, 2017). Furthermore, AI provides data-driven insights across the supply chain network to create intelligent knowledge bases for joint problem-solving, namely routing/scheduling issues, location planning, commodity consolidation, and inventory management (Min, 2009). In healthcare and medicine, AI algorithms enhance clinical decision-making by providing clinicians with valuable information and predictions about, for example, mortality, morbidity, and the risk of side effects or infections (Byerly et al., 2021; Loftus et al., 2020). Therefore, AI can be used to manage data and support decision-making processes for managers but might also be incorporated into products to facilitate customer needs.
According to the review of Loureiro et al. (2021), AI will induce new research opportunities such as challenges to internal and external business stakeholders which will impact the introduction of recent trends, including the interaction between multiple digital automated systems and the emergence of research avenues on business ethics and law on multiple research strands. Additionally, AI can promote flexibility and speed in different domains by changing the decision-making process by promoting complete delegation mechanisms from human to AI, hybrid or aggregated, by choosing the best human-machine interaction and combination (Jarrahi, 2018; Loureiro et al., 2021). These opportunities will have to contemplate mechanisms of validation and ethicality on the part of humans. As stated by Pope Francis, "artificial intelligence is at the root of the changing epoch we live through. Robotics can make a better world possible if it is united with the common good. Because if technological progress increases inequality, it is not real progress." (L’osservatore Romano, 2020). In all, AI provides a new background for studying more traditional theories such as stakeholder engagement theory, value creation theory, resource-based view theory, and the basis for new approaches such as personal behavior theory (Massaro et al., 2017).
List of topic areas
AI is poised to have a huge transformational impact on traditional decision-making processes, providing a new research context to challenge conventional theories in decision science and across multiple domains. In the next decade, the effects of AI will intensify, as virtually every field of business will need to rethink its core processes and decision-making methods for business management. Building on these new research trends, this special issue aims to develop a deeper understanding of the new opportunities and risks in business decision management fostered by AI. It specifically refers to the disruptive potential embedded in AI technologies as a critical driver for developing innovative solutions.
This special issue welcomes theoretical and empirical research articles on the following (and not limited to) topics:
- AI and new ways of managing decision-making processes
- AI systems within organizations
- AI algorithms for decision-making
- Knowledge tools for improving managerial decisions with AI
- AI and future skills for students
- AI and value creation
- Human-machines interaction in management decisions
- AI and operative resources in the production processes
- New AI-based products and services
- The dark side of AI in managing decision-making processes
- AI and ethical concerns in managing decision-making processes.
Prof. Maurizio Massaro (Managing Guest Editor),
Ca’ Foscari University of Venice, Italy,
Prof. Silvana Secinaro,
University of Turin, Italy,
Prof. Carlo Bagnoli,
Ca’ Foscari University of Venice, Italy,
Prof. Davide Calandra,
University of Turin, Italy,
Each manuscript will be screened and reviewed by at least two anonymous reviewers.
Submissions are made using ScholarOne Manuscripts. Registration and access are available at: http://mc.manuscriptcentral.com/md
Author guidelines must be strictly followed. Please see: https://www.emeraldgrouppublishing.com/journal/md#author-guidelines
Authors should select (from the drop-down menu) the special issue title at the appropriate step in the submission process, i.e. in response to “Please select the issue you are submitting to”.
Submitted articles must not have been previously published, nor should they be under consideration for publication anywhere else, while under review for this journal.
For any queries, please feel free to contact the guest editors.
Opening date for manuscript submissions: 4 January 2023
Closing date for manuscripts submission: 27 October 2023
Bagnoli, C., Dal Mas, F., & Massaro, M. (2019). The 4th industrial revolution: Business models and evidence from the field. International Journal of E-Services and Mobile Applications, 11(3), 34–47. https://doi.org/10.4018/IJESMA.2019070103
Byerly, S., Maurer, L. R., Mantero, A., Naar, L., An, G., & Kaafarani, H. M. A. (2021). Machine Learning and Artificial Intelligence for Surgical Decision Making. Surgical Infections, 22(6), 626–634. https://doi.org/10.1089/SUR.2021.007
Garousi Mokhtarzadeh, N., Amoozad Mahdiraji, H., Jafari-Sadeghi, V., Soltani, A., & Abbasi Kamardi, A. A. (2020). A product-technology portfolio alignment approach for food industry: a multi-criteria decision making with z-numbers. British Food Journal, 122(12), 3947–3967. https://doi.org/10.1108/BFJ-02-2020-0115/FULL/XML
Grewal, D., Roggeveen, A. L., & Nordfält, J. (2017). The Future of Retailing. Journal of Retailing, 93(1), 1–6. https://doi.org/10.1016/J.JRETAI.2016.12.008
Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586. https://doi.org/10.1016/J.BUSHOR.2018.03.007
Loftus, T. J., Tighe, P. J., Filiberto, A. C., Efron, P. A., Brakenridge, S. C., Mohr, A. M., Rashidi, P., Upchurch, G. R., & Bihorac, A. (2020). Artificial Intelligence and Surgical Decision-making. JAMA Surgery, 155(2), 148–158. https://doi.org/10.1001/JAMASURG.2019.4917
L’osservatore Romano. (2020). La robotica al servizio del bene comune - L’Osservatore Romano. https://www.osservatoreromano.va/it/news/2020-11/quo-257/la-robotica-al-servizio-del-bene-comune.html
Loureiro, S. M. C., Guerreiro, J., & Tussyadiah, I. (2021). Artificial intelligence in business: State of the art and future research agenda. Journal of Business Research, 129, 911–926. https://doi.org/10.1016/J.JBUSRES.2020.11.001
Massaro, M., Dumay, J., & Bagnoli, C. (2017). When the investors speak: intellectual capital disclosure and the Web 2.0. Management Decision, 55(9), 1888–1904. https://doi.org/10.1108/MD-10-2016-0699/FULL/XML
Min, H. (2009). Artificial intelligence in supply chain management: theory and applications. International Journal of Logistics Research and Applications, 13(1), 13–39. https://doi.org/10.1080/13675560902736537
Secinaro, S., Calandra, D., Secinaro, A., Muthurangu, V., & Biancone, P. (2021). The role of artificial intelligence in healthcare: a structured literature review. BMC Medical Informatics and Decision Making, 21(1), 1–23. https://doi.org/10.1186/S12911-021-01488-9/FIGURES/12
Shrestha, Y. R., Ben-Menahem, S. M., & von Krogh, G. (2019). Organizational Decision-Making Structures in the Age of Artificial Intelligence. California Management Review, 61(4), 66–83. https://doi.org/10.1177/0008125619862257
Talwar, R., & Koury, A. (2017). Artificial intelligence – the next frontier in IT security? Network Security, 2017(4), 14–17. https://doi.org/10.1016/S1353-4858(17)30039-9