From Insights to Trust: A Review of AI-Driven Business Analytics Literature
References
- Adama, H.E., Popoola, O.A., Okeke, C.D., Akinoso, A.E.: Theoretical framework supporting it and business strategy alignment for sustained competitive advantage. International Journal of Management & Entrepreneurship Research 6(4), 1273-1287 (2024).
- Alduweib, E., Arqoub, M. A., & Alromema, W. (2024, February). Automated Machine Learning for Information Management and Information Systems: An Overview. In 2024 2nd International Conference on Cyber Resilience (ICCR) (pp. 1-5). IEEE.
- Alghamdi, N.A., Al-Baity, H.H.: Augmented analytics driven by ai: A digital transformation beyond business intelligence. Sensors 22(20), 8071 (2022)
- Ali, S., Abuhmed, T., El-Sappagh, S., Muhammad, K., Alonso-Moral, J. M., Confalonieri, R., ... & Herrera, F. (2023). Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence. Information fusion, 99, 101805.
- Alqhatani, A., Ashraf, M.S., Ferzund, J., Shaf, A., Abosaq, H.A., Rahman, S., Irfan, M., Alqhtani, S.M.: 360 retail business analytics by adopting hybrid machine learning and a business intelligence approach. Sustainability 14(19), 11942 (2022)
- Amin, D. M., & Rai, M. (2020). A Novel Framework for Enhancing QoS of Big Data. International Journal of Advanced Computer Science and Applications, 11(4).
- Amirineni, S. (2024). Enhancing Predictive Analytics in Business Intelligence through Explainable AI: A Case Study in Financial Products. Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023.
- Anand, T., & Mitchell, D. (2022). Objectives and curriculum for a graduate business analytics capstone: Reflections from practice. Decision Sciences Journal of Innovative Education, 20(4), 235-245.
- Arias-Pérez, J., Chacón-Henao, J., & López-Zapata, E. (2023). Unlocking agility: Trapped in the antagonism between co-innovation in digital platforms, business analytics capability and external pressure for AI adoption? Business Process Management Journal, 29(6), 1791-1809.
- Baabdullah, A. M. (2024). The precursors of AI adoption in business: Towards an efficient decision-making and functional performance. International Journal of Information Management, 75, 102745.
- Badmus, O., Rajput, S. A., Arogundade, J. B., & Williams, M. (2024). AI-driven business analytics and decision making. World Journal of Advanced Research and Reviews, 24(1), 616-633.
- Beinabadi, H. Z., Baradaran, V., & Komijan, A. R. (2024). Sustainable supply chain decision-making in the automotive industry: A data-driven approach. Socio-Economic Planning Sciences, 95, 101908.
- Bender, T. (2024). Towards a process selection method for embedded analytics. Information Systems and e-Business Management, 1-25.
- Biswas, B., & Sanyal, M. K. (2019, January). Soft intelligence approaches for selecting products in online market. In 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence) (pp. 432-437). IEEE.
- Brink, A., Benyayer, L. D., & Kupp, M. (2024). Decision-making in organizations: should managers use AI? Journal of Business Strategy, 45(4), 267-274.
- Borges, A. F., Laurindo, F. J., Spínola, M. M., Gonçalves, R. F., & Mattos, C. A. (2021). The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions. International journal of information management, 57, 102225.
- Borsatto, J. M. L. S., Marcolin, C. B., Abdalla, E. C., & Amaral, F. D. (2024). Aligning community outreach initiatives with SDGs in a higher education institution with artificial intelligence. Cleaner and Responsible Consumption, 12, 100160.
- Chander, B., John, C., Warrier, L., & Gopalakrishnan, K. (2024). Toward trustworthy artificial intelligence (TAI) in the context of explainability and robustness. ACM Computing Surveys.
- Chatterjee, S., Chaudhuri, R., & Vrontis, D. (2024). Does data-driven culture impact innovation and performance of a firm? An empirical examination. Annals of Operations Research, 333(2), 601-626.
- Chaudhuri, R., Chatterjee, S., Vrontis, D., & Thrassou, A. (2024). Adoption of robust business analytics for product innovation and organizational performance: the mediating role of organizational data-driven culture. Annals of Operations Research, 339(3), 1757-1791.
- Chen, Y., Rui, H., & Whinston, A. B. (2024). Conversation Analytics: Can Machines Read between the Lines in Real-Time Strategic Conversations? Information Systems Research.
- Chen, X., & Siau, K. (2020). Business analytics/business intelligence and IT infrastructure: impact on organizational agility. Journal of Organizational and End User Computing (JOEUC), 32(4), 138-161.
- Costa, E. (2024). Industry 5.0 and SDG 9: a symbiotic dance towards sustainable transformation. Sustainable Earth Reviews, 7(1), 4.
- Colosimo, B. M., & Centofanti, F. (2022). Model interpretability, explainability and trust for manufacturing 4.0. In Interpretability for Industry 4.0: Statistical and Machine Learning Approaches (pp. 21-36). Cham: Springer International Publishing.
- East, A., & Chastain, K. (2024). Automated Machine Learning: Revolutionizing Data Science and Decision-Making. Innovative Computer Sciences Journal, 10(1), 1-9.
- European Commission Artificial Intelligence Act (2022). https://artificialintelligenceact.eu.
- Farooqi, N. S., & Khozium, M. O. (2022). Implementation of Artificial Intelligence Based Analyzer Using Multi-Agent System Approach. Intelligent Automation & Soft Computing, 31(1).
- Giermindl, L. M., Strich, F., Christ, O., Leicht-Deobald, U., & Redzepi, A. (2022). The dark sides of people analytics: reviewing the perils for organizations and employees. European Journal of Information Systems, 31(3), 410-435.
- Giudici, P., Centurelli, M., & Turchetta, S. (2024). Artificial Intelligence risk measurement. Expert Systems with Applications, 235, 121220.
- Gómez-Caicedo, M. I., Gaitán-Angulo, M., Bacca-Acosta, J., Briñez Torres, C. Y., & Cubillos Díaz, J. (2022). Business analytics approach to artificial intelligence. Frontiers in Artificial Intelligence, 5, 974180.
- González-Carrasco, I., Jiménez-Márquez, J. L., López-Cuadrado, J. L., & Ruiz-Mezcua, B. (2019). Automatic detection of relationships between banking operations using machine learning. Information Sciences, 485, 319-346.
- Groenewald, C. A., Groenewald, E., Uy, F., Kilag, O. K., Rabillas, A., & Cabuenas, M. H. (2024). Organizational Agility: The Role of Information Technology and Contextual Moderators-A Systematic Review. International Multidisciplinary Journal of Research for Innovation, Sustainability, and Excellence (IMJRISE), 1(3), 32-38.
- Gubela, R. M., Lessmann, S., & Stöcker, B. (2024). Multiple treatment modeling for Target Marketing campaigns: A large-scale Benchmark Study. Information Systems Frontiers, 26(3), 875-898.
- Hasan, M. R., Gazi, M. S., & Gurung, N. (2024). Explainable AI in Credit Card Fraud Detection: Interpretable Models and Transparent Decision-making for Enhanced Trust and Compliance in the USA. Journal of Computer Science and Technology Studies, 6(2), 01-12.
- Hayajneh, J. A. M., Elayan, M. B. H., Abdellatif, M. A. M., & Abubakar, A. M. (2022). Impact of business analytics and π-shaped skills on innovative performance: Findings from PLS-SEM and fsQCA. Technology in Society, 68, 101914.
- Hoang, T. G., & Bui, M. L. (2023). Business intelligence and analytic (BIA) stage-of-practice in micro-, small-and medium-sized enterprises (MSMEs). Journal of Enterprise Information Management, 36(4), 1080-1104.
- Jabeur, S. B., Stef, N., & Arfi, W. B. (2024). Artificial intelligence for innovation: a bibliometric analysis and structural variation approach. International Journal of Innovation Management, 28(05n06), 2450020.
- Javed Awan, M., Mohd Rahim, M. S., Nobanee, H., Munawar, A., Yasin, A., & Zain, A. M. (2021). Social media and stock market prediction: a big data approach. MJ Awan, M. Shafry, H. Nobanee, A. Munawar, A. Yasin et al.,” Social media and stock market prediction: a big data approach,” Computers, Materials & Continua, 67(2), 2569-2583.
- Kaggwa, S., Eleogu, T. F., Okonkwo, F., Farayola, O. A., Uwaoma, P. U., & Akinoso, A. (2024). AI in decision making transforming business strategies. International Journal of Research and Scientific Innovation, 10(12), 423-444
- Karpa, M., Kitsak, T., Domsha, O., Zhuk, O., Акімова, Л., & Akimov, O. (2023). Artificial intelligence as a tool of public management of socio-economic development: Economic systems, smart infrastructure, digital systems of business analytics and transfers.AD ALTA: Journal of Interdisciplinary Research, 13(1),13–20. https://doi.org/10.33543/1301341320
- Kaur, R., Singh, R., Gehlot, A., Priyadarshi, N., & Twala, B. (2022). Marketing strategies 4.0: Recent trends and technologies in marketing. Sustainability, 14(24), 16356.
- Kharat, R., Mathur, A.: Bridging the trust gap in machine learning automation: Enhancing end-user confidence through generative ai-driven explanations in natural language. In: Workshop on e-Business. pp. 126–138. Springer (2023)
- Kulkov, I., Kulkova, J., Rohrbeck, R., Menvielle, L., Kaartemo, V., & Makkonen, H. (2024). Artificial intelligence‐driven sustainable development: Examining organizational, technical, and processing approaches to achieving global goals. Sustainable Development, 32(3), 2253-2267.
- Liu, S., Liu, O., & Chen, J. (2023). A review on business analytics: definitions, techniques, applications and challenges. Mathematics, 11(4), 899.
- Neiroukh, S., Aljuhmani, H. Y., & Alnajdawi, S. (2024, January). In the era of emerging technologies: discovering the impact of artificial intelligence capabilities on timely decision-making and business performance. In 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS) (pp. 1-6). IEEE.
- Niederman, F. (2021). Project management: openings for disruption from AI and advanced analytics. Information Technology & People, 34(6), 1570-1599.
- Odejide, O. A., & Edunjobi, T. E. (2024). AI in project management: exploring theoretical models for decision-making and risk management. Engineering Science & Technology Journal, 5(3), 1072-1085.
- Olateju, O., Okon, S. U., Olaniyi, O. O., Samuel-Okon, A. D., & Asonze, C. U. (2024). Exploring the concept of explainable AI and developing information governance standards for enhancing trust and transparency in handling customer data. Available at SSRN.
- Osasona, F., Amoo, O. O., Atadoga, A., Abrahams, T. O., Farayola, O. A., & Ayinla, B. S. (2024). Reviewing the ethical implications of AI in decision making processes. International Journal of Management & Entrepreneurship Research, 6(2), 322-335.
- Paramesha, M., Rane, N. L., & Rane, J. (2024). Big data analytics, artificial intelligence, machine learning, internet of things, and blockchain for enhanced business intelligence. Partners Universal Multidisciplinary Research Journal, 1(2), 110-133.
- Park, M. S., Son, H., Hyun, C., & Hwang, H. J. (2021). Explainability of machine learning models for bankruptcy prediction. Ieee Access, 9, 124887-124899.
- Power, D. J., Heavin, C., McDermott, J., & Daly, M. (2018). Defining business analytics: an empirical approach. Journal of Business Analytics, 1(1), 40-53.
- Rabia, M.A.B., Bellabdaoui, A.: A comparative analysis of predictive analytics tools with integrated what-if modules for transport industry. In: 2022 14th International Colloquium of Logistics and Supply Chain Management (LOGISTIQUA).pp. 1–6. IEEE (2022)
- Raghupathi, W., & Raghupathi, V. (2021). Contemporary business analytics: An overview. Data, 6(8), 86.
- Rana, N. P., Chatterjee, S., Dwivedi, Y. K., & Akter, S. (2022). Understanding dark side of artificial intelligence (AI) integrated business analytics: assessing firm’s operational inefficiency and competitiveness. European Journal of Information Systems, 31(3), 364-387.
- Ratia, M., Myllärniemi, J., & Helander, N. (2019). The potential beyond IC 4.0: the evolution of business intelligence towards advanced business analytics. Measuring Business Excellence, 23(4), 396-410.
- Salehin, I., Islam, M. S., Saha, P., Noman, S. M., Tuni, A., Hasan, M. M., & Baten, M. A. (2024). AutoML: A systematic review on automated machine learning with neural architecture search. Journal of Information and Intelligence, 2(1), 52-81.
- Salvetti, F., Bertagni, B., Negri, F., Bersanelli, M., Usai, R., Milani, A., & De Rosa, D. (2022). At Your Best! Artificial Intelligence, People and Business Analytics, Highly Realistic Avatars, Innovative Learning and Development. In Innovations in Learning and Technology for the Workplace and Higher Education: Proceedings of ‘The Learning Ideas Conference’2021 (pp. 280-289). Springer International Publishing.
- Sajid, S., Arachchige, J. J., Bukhsh, F. A., Abhishta, A., & Ahmed, F. (2025). Building Trust in Predictive Analytics: A Review of ML Explainability and Interpretability. International Journal of Computing Sciences Research, 9, 3364-3391.
- Schmitt, M.: Automated machine learning: Ai-driven decision making in business analytics. Intelligent Systems with Applications 18, 200188 (2023)
- Soundararajan, R., & Shenbagaraman, V. M. (2024). Enhancing financial decision-making through explainable AI and Blockchain integration: improving transparency and trust in predictive models. Educational Administration: Theory and Practice, 30(4), 9341-9351.
- Stradowski, S., & Madeyski, L. (2024). Interpretability/Explainability Applied to Machine Learning Software Defect Prediction: An Industrial Perspective. IEEE Software.
- Sun, Z., & Huo, Y. (2019, January). A managerial framework for intelligent big data analytics. In Proceedings of the 2nd International Conference on Software Engineering and Information Management (pp. 152-156).
- Sundaram, A., Abdel-Khalik, H. S., & Abdo, M. G. (2023). Preventing Reverse Engineering of Critical Industrial Data with DIOD. Nuclear Technology, 209(1), 37-52.
- Thakral, P., Srivastava, P. R., Dash, S. S., Jasimuddin, S. M., & Zhang, Z. (2023). Trends in the thematic landscape of HR analytics research: a structural topic modeling approach. Management Decision, 61(12), 3665-3690.
- Tiwari, R.: Explainable ai (xai) and its applications in building trust and understanding in ai decision making. International J. Sci. Res. Eng. Manag 7, 1–13 (2023)
- Vereschak, O., Bailly, G., & Caramiaux, B. (2021). How to evaluate trust in AI-assisted decision making? A survey of empirical methodologies. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW2), 1-39.
- Wisniewski, H. S. (2020). What is Business with AI? Preparing Future Decision Makers and Leaders. Technology & Innovation, 21(4), 1-14.
- Wissuchek, C., Zschech, P.: Prescriptive analytics systems revised: systematic literature review from an information systems perspective. Information Systems and e-Business Management pp. 1–75 (2024)
- Zeebaree, M., Ismael, G. Y., Nakshabandi, O. A., Saleh, S. S., & Aqel, M. (2020). Impact of innovation technology in enhancing organizational management. Studies of Applied Economics, 38(4).
- Žigienė, G., Rybakovas, E., Vaitkienė, R., & Gaidelys, V. (2022). Setting the grounds for the transition from business analytics to artificial intelligence in solving supply chain risk. Sustainability, 14(19), 11827.
DOI: https://doi.org/10.2478/picbe-2025-0017 | Journal eISSN: 2558-9652
Language: English
Page range: 184 - 200
Published on: Jul 24, 2025
Published by: Bucharest University of Economic Studies
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
Related subjects:
© 2025 Alina Cornelia Luchian, Vasile Alecsandru Strat, Ovidiu Jianu, Monica Dragoicea, published by Bucharest University of Economic Studies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.