IDEAS home Printed from https://ideas.repec.org/a/eee/jbrese/v200y2025ics0148296325003996.html

Advancements in Artificial Intelligence-based prescriptive and cognitive analytics for business performance: a special issue editorial

Author

Listed:
  • Charles, Vincent
  • Emrouznejad, Ali
  • Kunz, Werner H.

Abstract

The rapid advancement of Artificial Intelligence (AI) is transforming business decision-making across industries. AI-based prescriptive and cognitive analytics offer significant potential to enhance decision-making, optimise performance, and create new avenues for value creation. This special issue explores the state-of-the-art advancements in these analytics and their business implications. We introduce the Analytics Onion as a conceptual foundation, comprising three interrelated layers: Perspective Analytics, Responsible Analytics, and the Descriptive-Diagnostic-Predictive-Prescriptive-Cognitive Analytics framework. The Analytics Onion captures the interplay between human judgment, ethics, analytical rigour, and AI techniques. The featured papers exemplify these layers through various topics, namely humanoid service robots, business location optimisation, ESG evaluation, energy efficiency, customer churn, prediction-led prescription, innovation culture, user satisfaction with AI, responsible AI in business models, and executives’ emotions influencing firm value. We highlight emerging opportunities and challenges and offer a forward-looking research agenda to guide future developments in this evolving field.

Suggested Citation

  • Charles, Vincent & Emrouznejad, Ali & Kunz, Werner H., 2025. "Advancements in Artificial Intelligence-based prescriptive and cognitive analytics for business performance: a special issue editorial," Journal of Business Research, Elsevier, vol. 200(C).
    • Handle: RePEc:eee:jbrese:v:200:y:2025:i:c:s0148296325003996
    DOI: 10.1016/j.jbusres.2025.115576
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0148296325003996
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jbusres.2025.115576?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Thanos, Ioannis C., 2023. "The complementary effects of rationality and intuition on strategic decision quality," European Management Journal, Elsevier, vol. 41(3), pages 366-374.
    2. Kwon, Ohbyung & Lee, Namyeon & Shin, Bongsik, 2014. "Data quality management, data usage experience and acquisition intention of big data analytics," International Journal of Information Management, Elsevier, vol. 34(3), pages 387-394.
    3. Feng, Yi & Yin, Yunqiang & Wang, Dujuan & Ignatius, Joshua & Cheng, T.C.E. & Marra, Marianna & Guo, Yihan, 2024. "Enhancing e-commerce customer churn management with a profit- and AUC-focused prescriptive analytics approach," Journal of Business Research, Elsevier, vol. 184(C).
    4. Duan, Yanqing & Edwards, John S. & Dwivedi, Yogesh K, 2019. "Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda," International Journal of Information Management, Elsevier, vol. 48(C), pages 63-71.
    5. Song, Christina Soyoung, 2024. "Prescriptive multi-group networks: Humanoid service robots’ value co-creation and co-destruction potentials in apparel stores," Journal of Business Research, Elsevier, vol. 183(C).
    6. Ghasemaghaei, Maryam & Calic, Goran, 2020. "Assessing the impact of big data on firm innovation performance: Big data is not always better data," Journal of Business Research, Elsevier, vol. 108(C), pages 147-162.
    7. Sariyer, Gorkem & Kumar Mangla, Sachin & Chowdhury, Soumyadeb & Erkan Sozen, Mert & Kazancoglu, Yigit, 2024. "Predictive and prescriptive analytics for ESG performance evaluation: A case of Fortune 500 companies," Journal of Business Research, Elsevier, vol. 181(C).
    8. Gnekpe, Christian & Tchuente, Dieudonné & Nyawa, Serge & Dey, Prasanta Kumar, 2024. "Energy Performance of Building Refurbishments: Predictive and Prescriptive AI-based Machine Learning Approaches," Journal of Business Research, Elsevier, vol. 183(C).
    9. Dimitris Bertsimas & Nathan Kallus, 2020. "From Predictive to Prescriptive Analytics," Management Science, INFORMS, vol. 66(3), pages 1025-1044, March.
    10. Kopalle, Praveen K. & Kuusela, Hannu & Lehmann, Donald R., 2023. "The role of intuition in CEO acquisition decisions," Journal of Business Research, Elsevier, vol. 167(C).
    11. Berman, Alexander & de Fine Licht, Karl & Carlsson, Vanja, 2024. "Trustworthy AI in the public sector: An empirical analysis of a Swedish labor market decision-support system," Technology in Society, Elsevier, vol. 76(C).
    12. Schäfers, A. & Bougioukos, V. & Karamatzanis, G. & Nikolopoulos, K., 2024. "Prediction-led prescription: Optimal Decision-Making in times of turbulence and business performance improvement," Journal of Business Research, Elsevier, vol. 182(C).
    13. Lepenioti, Katerina & Bousdekis, Alexandros & Apostolou, Dimitris & Mentzas, Gregoris, 2020. "Prescriptive analytics: Literature review and research challenges," International Journal of Information Management, Elsevier, vol. 50(C), pages 57-70.
    14. Duan, Yanqing & Cao, Guangming & Edwards, John S., 2020. "Understanding the impact of business analytics on innovation," European Journal of Operational Research, Elsevier, vol. 281(3), pages 673-686.
    15. Yoganathan, Vignesh & Osburg, Victoria-Sophie, 2024. "The mind in the machine: Estimating mind perception's effect on user satisfaction with voice-based conversational agents," Journal of Business Research, Elsevier, vol. 175(C).
    16. David Horneber & Sven Laumer, 2023. "Algorithmic Accountability," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 65(6), pages 723-730, December.
    17. Rafi M. M. I. Chowdhury, 2019. "The Moral Foundations of Consumer Ethics," Journal of Business Ethics, Springer, vol. 158(3), pages 585-601, September.
    18. Han, Shuihua & Chen, Linlin & Su, Zhaopei & Gupta, Shivam & Sivarajah, Uthayasankar, 2024. "Identifying a good business location using prescriptive analytics: Restaurant location recommendation based on spatial data mining," Journal of Business Research, Elsevier, vol. 179(C).
    19. Wang, Qiping & Yiu Keung Lau, Raymond & Xie, Haoran & Liu, Hongyan & Guo, Xunhua, 2024. "Social Executives’ emotions and firm value: An empirical study enhanced by cognitive analytics," Journal of Business Research, Elsevier, vol. 175(C).
    20. Kanungo, Rama Prasad & Liu, Rui & Gupta, Suraksha, 2024. "Cognitive analytics enabled responsible artificial intelligence for business model innovation: A multilayer perceptron neural networks estimation," Journal of Business Research, Elsevier, vol. 182(C).
    21. Li, Wanqing & Yu, Jiang & Chen, Feng, 2025. "Linking firm performance with innovation culture: An algorithmic approach towards theory building," Journal of Business Research, Elsevier, vol. 187(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Yang & Wang, Yongheng & Yang, Pengyu, 2025. "Does artificial intelligence impact corporate ESG performance? Evidence from a quasi-natural experiment in China," Energy Economics, Elsevier, vol. 151(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Korayim, Diana & Chotia, Varun & Jain, Girish & Hassan, Sharfa & Paolone, Francesco, 2024. "How big data analytics can create competitive advantage in high-stake decision forecasting? The mediating role of organizational innovation," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    2. Latinovic, Zoran & Chatterjee, Sharmila C., 2022. "Achieving the promise of AI and ML in delivering economic and relational customer value in B2B," Journal of Business Research, Elsevier, vol. 144(C), pages 966-974.
    3. Leven J. Zheng & Justin Zuopeng Zhang & Huan Wang & Jacky F. L. Hong, 2025. "Exploring the impact of Big Data Analytics Capabilities on the dual nature of innovative activities in MSMEs: A Data-Agility-Innovation Perspective," Annals of Operations Research, Springer, vol. 350(2), pages 699-727, July.
    4. Herhausen, Dennis & Ludwig, Stephan & Abedin, Ehsan & Haque, Nasim Ul & de Jong, David, 2025. "From words to insights: Text analysis in business research," Journal of Business Research, Elsevier, vol. 198(C).
    5. Christopher Wissuchek & Patrick Zschech, 2025. "Prescriptive analytics systems revised: a systematic literature review from an information systems perspective," Information Systems and e-Business Management, Springer, vol. 23(2), pages 279-353, June.
    6. Oesterreich, Thuy Duong & Anton, Eduard & Teuteberg, Frank & Dwivedi, Yogesh K, 2022. "The role of the social and technical factors in creating business value from big data analytics: A meta-analysis," Journal of Business Research, Elsevier, vol. 153(C), pages 128-149.
    7. Tobias Bender, 2024. "Towards a process selection method for embedded analytics," Information Systems and e-Business Management, Springer, vol. 22(3), pages 501-525, September.
    8. Sabeen Hussain Bhatti & Wan Mohd Hirwani Wan Hussain & Jabran Khan & Shahbaz Sultan & Alberto Ferraris, 2024. "Exploring data-driven innovation: What’s missing in the relationship between big data analytics capabilities and supply chain innovation?," Annals of Operations Research, Springer, vol. 333(2), pages 799-824, February.
    9. Sel, Burakhan & Minner, Stefan, 2022. "A hedging policy for seaborne forward freight markets based on probabilistic forecasts," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 166(C).
    10. Marisa Schirmer & Selina Fußwinkel & Susanne Mütze-Niewöhner & Verena Nitsch & Rüdiger von Nitzsch, 2026. "Quality of Decision-Making Processes in Teams: An Analysis of the Effects of Supportive Organizational Culture, Perceived Decision Relevance and a Potential Use of AI," Group Decision and Negotiation, Springer, vol. 35(1), pages 1-30, March.
    11. Filom, Siyavash & Amiri, Amir M. & Razavi, Saiedeh, 2022. "Applications of machine learning methods in port operations – A systematic literature review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    12. Hao, Xinyue & Demir, Emrah & Eyers, Daniel, 2024. "Exploring collaborative decision-making: A quasi-experimental study of human and Generative AI interaction," Technology in Society, Elsevier, vol. 78(C).
    13. Nijat Mehdiyev & Maxim Majlatow & Peter Fettke, 2025. "Quantifying and explaining machine learning uncertainty in predictive process monitoring: an operations research perspective," Annals of Operations Research, Springer, vol. 347(2), pages 991-1030, April.
    14. Hayajneh, Jamal Abdelrahman .M. & Elayan, Malek Bakheet Haroun & Abdellatif, Mamdouh Abdallah Mohamed & Abubakar, A. Mohammed, 2022. "Impact of business analytics and π-shaped skills on innovative performance: Findings from PLS-SEM and fsQCA," Technology in Society, Elsevier, vol. 68(C).
    15. Schäfers, A. & Bougioukos, V. & Karamatzanis, G. & Nikolopoulos, K., 2024. "Prediction-led prescription: Optimal Decision-Making in times of turbulence and business performance improvement," Journal of Business Research, Elsevier, vol. 182(C).
    16. Steffen Kurpiela & Frank Teuteberg, 2024. "Linking business analytics affordances to corporate strategic planning and decision making outcomes," Information Systems and e-Business Management, Springer, vol. 22(1), pages 33-60, March.
    17. Djonata Schiessl & Helison Bertoli Alves Dias & José Carlos Korelo, 2022. "Artificial intelligence in marketing: a network analysis and future agenda," Journal of Marketing Analytics, Palgrave Macmillan, vol. 10(3), pages 207-218, September.
    18. Choi, Tsan-Ming & Sun, Xuting, 2025. "Prescriptive analytics for sustainable supply chain operations: The PASO framework for Industry 5.0," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 201(C).
    19. Mahdi Jahangard & Ying Xie & Yuanjun Feng, 2025. "Leveraging machine learning and optimization models for enhanced seaport efficiency," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 27(4), pages 710-751, December.
    20. Agag, Gomaa & Shehawy, Yasser Moustafa & Almoraish, Ahmed & Eid, Riyad & Chaib Lababdi, Houyem & Gherissi Labben, Thouraya & Abdo, Said Shabban, 2024. "Understanding the relationship between marketing analytics, customer agility, and customer satisfaction: A longitudinal perspective," Journal of Retailing and Consumer Services, Elsevier, vol. 77(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jbrese:v:200:y:2025:i:c:s0148296325003996. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jbusres .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.