ANALYZING THE AREAS OF APPLICATION OF INNOVATIONS IN ASSESSING BUSINESS ENTITIES: A SYSTEMATIC LITERATURE REVIEW
DOI:
https://doi.org/10.55640/Keywords:
business entity assessment, innovation, artificial intelligence, blockchain, big data analytics, financial valuation, systematic reviewAbstract
The rapid digital transformation of the global economy has necessitated innovative approaches to assessing business entities. Traditional valuation and performance evaluation methods are increasingly supplemented or replaced by advanced technologies such as artificial intelligence (AI), machine learning (ML), blockchain, big data analytics, and the Internet of Things (IoT). This study systematically reviews the literature to identify and analyze the primary areas of application of these innovations in business assessment. Following PRISMA guidelines, 142 peer-reviewed articles published between 2015 and 2025 were analyzed from Scopus, Web of Science, and Google Scholar. Key findings reveal that AI and ML dominate applications in financial forecasting, risk management, and fraud detection, while blockchain enhances transparency in auditing and ESG reporting. Big data analytics and IoT enable real-time operational and strategic assessments. Quantitative data indicate that organizations adopting these innovations report up to 66% gains in productivity and efficiency, with AI investment in the financial sector projected to reach USD 60 billion by 2025. The study categorizes applications into five core areas, highlights empirical benefits and challenges, and proposes a conceptual framework for integrated innovation-driven business assessment. Implications for practitioners, policymakers, and future research are discussed, emphasizing the need for ethical governance and hybrid human-AI models.
References
1. Akter, S., Michael, K., Uddin, M. R., McCarthy, G., & Rahman, M. (2020). Transforming business using digital innovations: The role of artificial intelligence, blockchain, cloud and data analytics. Annals of Operations Research. https://doi.org/10.1007/s10479-020-03620-w
2.Cao, Y., et al. (2025). Using AI and big data analytics to support entrepreneurial decision-making. Scientific Reports. https://doi.org/10.1038/s41598-025-20871-4
3.Farahani, M. S. (2024). Analysis of business valuation models with AI emphasis. Sustainable Economies Journal.
4.Geertsema, P., & Lu, H. (2023). Relative valuation with machine learning. Journal of Accounting Research, 61(2), 329–376.
5.Hanelt, A., Bohnsack, R., Marz, D., & Antunes Marante, C. (2021). A systematic review of the literature on digital transformation. Journal of Management Studies, 58(5), 1159–1197.
6.Jorzik, P., et al. (2024). AI-driven business model innovation: A systematic review and research agenda. Journal of Business Research, 182, 114764.
7.Kayikci, S., & Khoshgoftaar, T. M. (2024). Blockchain meets machine learning: A survey. Journal of Big Data.
8.Magableh, K. N. Y., et al. (2024). Adoption of blockchain technology and big data analytics. Sustainability.
9.Meiryani, M., Warganegara, D. L., & Andini, V. (2023). Big data, machine learning, artificial intelligence and blockchain in corporate governance. Foresight and STI Governance, 17(4), 69–78.
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