Measuring Sustainable Progress: AI-Driven Psychometrics for Development Analysis
Abstract
This study presents the quantitative outcomes of applying AI-driven psychometric analysis to investigate the nexus between psychological factors and sustainable practices. Utilizing a sample cohort of 1000 participants, the research unveiled a robust positive correlation (r = 0.78, p < 0.001) between metrics assessing psychological well-being and active engagement in sustainable behaviors. The integration of Machine Learning models yielded promising results, demonstrating an 85% accuracy in predicting sustainable conduct based on psychometric evaluations. These quantitative findings underscore the pivotal role of AI-infused methodologies in comprehending and forecasting the interplay between psychological aspects and sustainable development initiatives, offering insights crucial for informed policy-making and interventions.
References
Stern, P. C. (2000). Toward a coherent theory of environmentally significant behavior. Journal of Social Issues, 56(3), 407-424.
Whitmarsh, L. (2009). Behavioural responses to climate change: Asymmetry of intentions and impacts. Journal of Environmental Psychology, 29(1), 13-23.
Montag, C., Elhai, J. D., & Montag, C. (Eds.). (2021). Digital phenotyping and mobile sensing: New developments in psychoinformatics (Vol. 15). Springer.
Oreg, S., & Katz-Gerro, T. (2006). Predicting proenvironmental behavior cross-nationally: Values, the theory of planned behavior, and value-belief-norm theory. Environment and Behavior, 38(4), 462-483.
Schmitt, M. T., Mackay, T. G., Gollwitzer, M., & Alcalde, C. (2018). The role of the self in predicting environmentally friendly behavior: A comparison of three models. Journal of Environmental Psychology, 55, 10-19.
Dignum, V., van Riemsdijk, M. B., & Dignum, F. (2020). Responsible AI and Ethics: Towards Trustworthy AI. Springer.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
Schultz, P. W., & Kaiser, F. G. (2012). Promoting pro-environmental behavior. Oxford University Press.
Gifford, R. (2011). The dragons of inaction: Psychological barriers that limit climate change mitigation and adaptation. American Psychologist, 66(4), 290-302.
Steg, L., & Vlek, C. (2009). Encouraging pro-environmental behaviour: An integrative review and research agenda. Journal of Environmental Psychology, 29(3), 309-317.
Fishbein, M., & Ajzen, I. (2010). Predicting and changing behavior: The reasoned action approach. Psychology Press.
Thøgersen, J. (2012). The psychology of green consumption. Routledge.
Bamberg, S., & Möser, G. (2007). Twenty years after Hines, Hungerford, and Tomera: A new meta-analysis of psycho-social determinants of pro-environmental behaviour. Journal of Environmental Psychology, 27(1), 14-25.
Gatersleben, B., & Vlek, C. (1998). Measurement and determinants of environmentally significant consumer behavior. Environment and Behavior, 30(6), 818-840.
Peddireddy, K. (2023, October 20). Effective Usage of Machine Learning in Aero Engine test data using IoT based data driven predictive analysis. IJARCCE, 12(10). https://doi.org/10.17148/ijarcce.2023.121003
Peddireddy, A., & Peddireddy, K. (2023, March 30). Next-Gen CRM Sales and Lead Generation with AI. International Journal of Computer Trends and Technology, 71(3), 21–26. https://doi.org/10.14445/22312803/ijctt-v71i3p104
Ghosh, D., & Irani, D. (2016). A survey of machine learning algorithms for big data analytics. Journal of Big Data, 3(1), 1-32.
Peddireddy, K. (2023, May 11). Streamlining Enterprise Data Processing, Reporting and Realtime Alerting using Apache Kafka. 2023 11th International Symposium on Digital Forensics and Security (ISDFS). https://doi.org/10.1109/isdfs58141.2023.10131800.
Martellini, M., & Rule, S. (2016). Cybersecurity: The Insights You Need from Harvard Business Review. Harvard Business Review Press.
Peddireddy, K. (2023, May 18). Kafka-based Architecture in Building Data Lakes for Real-time Data Streams. International Journal of Computer Applications, 185(9), 1–3. https://doi.org/10.5120/ijca2023922740
Downloads
Published
Issue
Section
License
Copyright (c) 2023 International Journal of Sustainable Development Through AI, ML and IoT
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.