Empowering Sustainable Business Practices Through AI, Data Analytics and Blockchain: A Multi-Industry Perspectives

Md Mizanur Rahaman; Md Rashedul Islam; Mohammad Muzahidur Rahman Bhuiyan; Md Munna Aziz; Mia Md Tofayel Gonee Manik; Inshad Rahman Noman

Md Mizanur Rahaman
Md Rashedul Islam
Mohammad Muzahidur Rahman Bhuiyan
Md Munna Aziz
Mia Md Tofayel Gonee Manik
Inshad Rahman Noman

Keywords: Artificial Intelligence, Blockchain Technology, Sustainable Business, Industry Analysis

Abstract

This study examines the adoption of artificial intelligence (AI) and blockchain technology across multiple industries along with their impact on sustainability and performance improvement. AI Adoption Rate vs Sustainability Score scatter plot shows a strong positive correlation, indicating that firms with higher AI adoption rates achieve better sustainability outcomes, with adoption rates ranging from 50% to 110% and sustainability scores ranging from 5 to 10. AI and Blockchain Adoption by Industry bar chart reveals that AI adoption is highest in the technology sector (81.3%), followed by finance (69.7%) and healthcare (64.8%), whereas blockchain adoption lags, particularly in retail (35.3%) and manufacturing (41.1%). The Diversity of AI & Blockchain Adoption chart illustrates that the technology sector has the highest combined adoption rate (80% AI and 60% blockchain), while the retail sector shows the lowest diversity, with only 50% of firms adopting either technology. The heatmap demonstrates weak to moderate correlations between AI and blockchain adoption and sustainability or performance improvements, with AI adoption showing a slight negative correlation with sustainability improvement (-0.096) and performance improvement (-0.043). These results suggest that while AI adoption is correlated with improved sustainability, both AI and blockchain technologies may require more time and integration to show significant improvements in performance metrics. Future studies should focus on how complementary strategies can enhance the long-term impact of these technologies on business sustainability and overall performance.

References

Aziz, M. M., Rahaman, M. M., Bhuiyan, M. M. R., & Islam, M. R. (2023). Integrating sustainable IT solutions for long-term business growth and development. Journal of Business and Management Studies, 5(6), 152–159. https://doi.org/10.32996/jbms.2023.5.6.12
Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173-1182.
Bhuiyan, M. M. R., Rahaman, M. M., Aziz, M. M., Islam, M. R., & Das, K. (2023). Predictive analytics in plant biotechnology: Using data science to drive crop resilience and productivity. Journal of Environmental and Agricultural Studies, 4(3), 77–83. https://doi.org/10.32996/jeas.2024.4.3.11
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101.
Brown, L., & Taylor, M. (2023). Blockchain and Its Role in Securing Financial Transactions. Journal of Financial Innovation, 10(2), 101-121.
Cao, L. (2017). Data science: Challenges and directions. Communications of the ACM, 60(8), 59-68.
Chen, Y., Lee, T., & Patel, R. (2023). AI and data analytics in sustainable agriculture. Journal of Agricultural Technology, 15(1), 45-60.
Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). Sage Publications.
Delen, D., & Zolbanin, H. M. (2018). The analytics paradigm in business research. Journal of Business Research, 90, 186-195.
Denzin, N. K. (2017). The research act: A theoretical introduction to sociological methods. Transaction Publishers.
Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). Sage Publications.
Ford, P., Zhang, L., & Smith, J. (2023). AI-Driven Business Intelligence: The New Frontier. Journal of Business Research, 12(3), 58-78.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.
Islam, M. R., Rahaman, M. M., Bhuiyan, M. M. R., & Aziz, M. M. (2023). Machine learning with health information technology: Transforming data-driven healthcare systems. Journal of Medical and Health Studies, 4(1), 89–96. https://doi.org/10.32996/jmhs.2023.4.1.11
Jones, R., & Lee, S. (2021). Predictive Analytics in Business Using Machine Learning. Journal of Business and Technology, 8(4), 102-115.
Koens, T., & Poll, E. (2018). What blockchain alternative is perfect for your use case? Ledger, 3, 39-52.
Noman, I. R., Bortty, J. C., Bishnu, K. K., Aziz, M. M., & Islam, M. R. (2022). Data-driven security: Improving autonomous systems through data analytics and cybersecurity. Journal of Computer Science and Technology Studies, 4(2), 182–190. https://doi.org/10.32996/jcsts.2022.4.2.22
Palinkas, L. A., Horwitz, S. M., Green, C. A., Wisdom, J. P., Duan, N., & Hoagwood, K. (2015). Purposeful sampling for qualitative data collection and analysis in mixed method implementation research. Administration and Policy in Mental Health and Mental Health Services Research, 42(5), 533-544.
Patel, V. (2022). E-commerce Innovation Through Blockchain: Enhancing Transparency. International Journal of E-commerce Studies, 18(2), 123-139.
Provost, F., & Fawcett, T. (2013). Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking. O'Reilly Media, Inc.
Rahaman, M. M., Rani, S., Islam, M. R., & Bhuiyan, M. M. R. (2023). Machine learning in business analytics: Advancing statistical methods for data-driven innovation. Journal of Computer Science and Technology Studies, 5(3), 104–111. https://doi.org/10.32996/jcsts.2023.5.3.8
Smith, D. (2022). Machine Learning in Modern Business: Optimizing Decision-Making Processes. Business Analytics Review, 11(3), 67-82.
Smith, D., Jones, R., & Lee, S. (2021). The role of AI in modern business: An empirical investigation. Journal of Business Technology, 18(2), 88-101.
Tashakkori, A., & Teddlie, C. (2010). SAGE handbook of mixed methods in social & behavioral research (2nd ed.). SAGE Publications.
Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53-55.
Williams, A. (2021). AI and Sustainability: Leveraging Technology for Green Solutions. Sustainable Business Review, 9(1), 88-105.
Zhang, X. (2020). Blockchain and Sustainable Supply Chains: A Review. Journal of Supply Chain Innovation, 7(2), 34-47.
Zheng, Z., Xie, S., Dai, H., Chen, X., & Wang, H. (2018). Blockchain challenges and opportunities: A survey. International Journal of Web and Grid Services, 14(1), 34-53.