رضایی رحیمی متین، مدینه؛ حسینزاده، شهربانو؛ آذرپرا، کبری، و مظلوم، زهراء(1403). تأثیر هوش مصنوعی، واقعیت مجازی و واقعیت افزوده بر روشهای تدریس و یادگیری. اولین همایش ملی نگرشهای نوین در مسائل آموزش و پرورش، رامشیر.
زمانی، آرزو؛ خمسه، عباس، و ایرانیانفرد، سیدجواد(1402). انتقال تکنولوژی در عصر صنعت 5.0: مدل یکپارچه هوش مصنوعی و مولفههای انسانی. مدیریت نوآوری، 12(4)، 140-111.
سعدآبادی، علیاصغر، و رحیمیراد، زهره(1399). کاربست نوآوری اجتماعی جهت افزایش مشارکت اجتماعی در اسناد بالادستی علم و فناوری: مطالعه موردی نقشه جامع علمی کشور. سیاستگذاری عمومی، 6(2)، 73-51.
سهرابی، بابک؛ خلیلی جعفرآبادی، احمد، و رودی، امیر(1396). کشف ویژگیهای حوزههای تحقیقاتی نوظهور با استفاده از روش فراترکیب. سیاست علم و فناوری، 10(4)، 30-15.
شانظری، حامد؛ شهرامنیا، امیرمسعود؛ مسعودنیا، حسین، و هرسیج، حسین(1403). بررسی استفاده از هوشمصنوعی در پویایی سیاستگذاری عمومی. سیاستگذاری عمومی، 10(4)، 53-37.
عبدیوند، مهران؛ شمسی، خسرو، و شمسی، سعید(1402). استفاده از هوش مصنوعی برای ریشهکنی فقر و گرسنگی، تأمین آب پاکیزه، بهداشت و سلامت عمومی در سایه مشارکت جهانی، هشتمین همایش بینالمللی دانش و فناوری مهندسی برق، کامپیوتر و مکانیک ایران، تهران.
کریمی اسبوئی، سمانه؛ ثقفی، فاطمه، و قاضینوری، سپهر(1401). گونهشناسی گذارهای فنی- اجتماعی با رویکرد فراتحلیل محتوا-تطبیق مسیرهای جدید با شواهد. مدیریت نوآوری، 11(4)، 54-23.
کریمی جعفری، فاطمه؛ دانشور، مریم، و عباسزاده سورمی، زهرا(1403). شناسایی کارکردهای مدیریت منابع انسانی در اقتصاد گیگ با رویکرد فراترکیب. مدیریت نوآوری، 13(2)، 250-197.
کریممیان، زهره؛ محمدی، مهدی؛ قاضینوری، سپهر، و ذوالفقارزاده، محمدمهدی(1398). طبقهبندی ویژگیهای حکمرانی از طریق شبکههای خطمشی با استفاده از روش فراترکیب. مدیریت دولتی، 11(3)، 402-377.
عزتی آراستهپور، فائزه؛ علیاحمدی، علیرضا؛ پیشوایی، میرسامان، و پارسانژاد، محمدرضا(1402). شناسایی و تحلیل عرصهها و مضامین شکلدهنده نوآوری اجتماعی با استفاده از رویکرد فراتلفیق. مدیریت نوآوری، 12(2)، 194-149.
مرکز پژوهشهای مجلس شورای اسلامی(1403). وضعیت فقر و ویژگیهای فقرا در دهه گذشته (دهه 90)، بازیابی از:
https://rc.majlis.ir/fa/report/show/1775550
محسنی کیاسری، مصطفی؛ محمدی، مهدی؛ جعفرنژاد، احمد؛ مختارزاده، نیما، و اسدی فرد، رضا(1396). دستهبندی ابزارهای سیاست نوآوری تقاضامحور با استفاده از رویکرد فراترکیب. مدیریت نوآوری، 6(2)، 138-109.
هزارجریبی، جعفر، و مرادینژاد، الهام(1403). صدای فقر همهگستر در جامعه (فراترکیب مطالعات فقر در ایران: بازه زمانی سالهای 1390-1402). مسائل اجتماعی ایران، ۱۵(۱)، 287-257.
Abiri, R., Rizan, N., Balasundram, S. K., Shahbazi, A. B., & Abdul-Hamid, H. (2023). Application of digital technologies for ensuring agricultural productivity. Heliyon, 9(12).
https://doi.org/10.1016/j.heliyon.2023.e22601
Abou-Foul, M., Ruiz-Alba, J. L., & López-Tenorio, P. J. (2023). The impact of artificial intelligence capabilities on servitization: The moderating role of absorptive capacity-A dynamic capabilities perspective. Journal of Business Research, 157, 113609.
https://doi.org/10.1016/j.jbusres.2022.113609
Adel, H. M., Khaled, M., Yehya, M. A., Elsayed, R., Ali, R. S., & Ahmed, F. E. (2024). Nexus among artificial intelligence implementation, healthcare social innovation, and green image of hospitals’ operations management in Egypt. Cleaner Logistics and Supply Chain, 11, 100156.
https://doi.org/10.1016/j.clscn.2024.100156
Ajaj, R., Buheji, M., & Hassoun, A. (2024). Optimizing the readiness for industry 4.0 in fulfilling the Sustainable Development Goal 1: focus on poverty elimination in Africa. Frontiers in Sustainable Food Systems, 8, 1393935.
https://doi.org/10.3389/fsufs.2024.1393935
Akter, S., Sultana, S., Gunasekaran, A., Bandara, R. J., & Miah, S. J. (2024). Tackling the global challenges using data-driven innovations. Annals of Operations Research, 333(2), 517-532.
https://doi.org/10.1007/s10479-024-05875-z
Alsharkawi, A., Al-Fetyani, M., Dawas, M., Saadeh, H., & Alyaman, M. (2022). Improved Poverty Tracking and Targeting in Jordan Using Feature Selection and Machine Learning. Ieee Access, 10, 86483-86497.
https://doi.org/10.1109/ACCESS.2022.3198951
Aromolaran, O., Ngepah, N., & Saba, C. S. (2024). Macroeconomic determinants of poverty in South Africa: the role of investments in artificial intelligence. Access Journal, 5(2), 288-305.
https://doi.org/10.46656/access.2024.5.2(7)
Ayob, N., Teasdale, S., & Fagan, K. (2016). How social innovation ‘came to be’: Tracing the evolution of a contested concept. Journal of Social Policy, 45(4), 635-653.
https://doi.org/10.1017/S004727941600009X
Badea, L., Șerban-Oprescu, G. L., Iacob, S. E., Mishra, S., & Stanef, M. R. (2024). Artificial Intelligence and the Future of Work—A Sustainable Development Perspective. Amfiteatru Econ, 26, 1031-1047.
https://doi.org/10.24818/EA/2024/S18/1031
Beltramo, T. P., Calvi, R., De Giorgi, G., & Sarr, I. (2023). Child poverty among refugees. World Development, 171, 106340.
https://doi.org/10.1016/j.worlddev.2023.106340
Bharadwaj, A., El Sawy, O. A., Pavlou, P. A., & Venkatraman, N. V. (2013). Digital business strategy: toward a next generation of insights. MIS quarterly, 471-482. Bjola, C. (2022). AI for development: Implications for theory and practice. Oxford Development Studies, 50(1), 78-90.
https://doi.org/10.1080/13600818.2021.1960960
Bokhari, S. A. A., & Myeong, S. (2022). Use of artificial intelligence in smart cities for smart decision-making: A social innovation perspective. Sustainability, 14(2), 620.
https://doi.org/10.3390/su14020620
Cabanillas-Carbonell, M., Perez-Martinez, J., & Zapata-Paulini, J. (2023). Contributions of the 5G Network with Respect to Poverty (SDG1), Systematic Literature Review. Sustainability, 15(14), 11301.
https://doi.org/10.3390/su151411301
Calzada, I. (2024). Artificial intelligence for social innovation: beyond the noise of algorithms and datafication. Sustainability, 16(19), 8638.
https://doi.org/10.3390/su16198638
Clarke, V., & Braun, V. (2013). Teaching thematic analysis: Overcoming challenges and developing strategies for effective learning. The psychologist, 26(2).
Corral, P., Henderson, H., & Segovia, S. (2025). Poverty mapping in the age of machine learning. Journal of Development Economics, 172, 103377.
https://doi.org/10.1016/j.jdeveco.2024.103377
Cowls, J. (2021). ‘AI for social good’: whose good and who’s good? Introduction to the special issue on artificial intelligence for social good. Philosophy & Technology, 34(Suppl 1), 1-5.
https://doi.org/10.1007/s13347-021-00466-3
Davis, F. D. (1989). Technology acceptance model: TAM. Al-Suqri, MN, Al-Aufi, AS: Information Seeking Behavior and Technology Adoption, 205(219), 5.
De Filippi, P.; Mannan, M.; Cossar, S.; Merk, T.; Kamalova, J. Blockchain Technology and Polycentric Governance. European University Institute. 2024. Available online:
https://www.eui.eu (accessed on 15 September 2024).
Dionisio, M., de Souza Junior, S. J., Paula, F., & Pellanda, P. C. (2024). The role of digital social innovations to address SDGs: A systematic review. Environment, Development and Sustainability, 26(3), 5709-5734.
https://doi.org/10.1007/s10668-023-03038-x
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International journal of information management, 57, 101994.
https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Fabregas, R., Kremer, M., & Schilbach, F. (2019). Realizing the potential of digital development: The case of agricultural advice. Science, 366(6471), eaay3038.
https://doi.org/10.1126/science.aay3038
Fazal, A., Ahmed, A., & Nisar, S. (2023). Artificial Intelligence and Financial Inclusion: A Systematic Literature Review. Journal of Asian Development Studies, 12(3), 158-168.
https://doi.org/10.62345/jads.2023.12.3.11
Fuentes-Penna, A., & Ibarra, J. D. D. G. (2024). Personalized Education and Artificial Intelligence. International Journal of Combinatorial Optimization Problems and Informatics, 15(2), 1.
https://doi.org/10.61467/2007.1558.2024.v15i2.431
Gosselink, B. H., Brandt, K., Croak, M., DeSalvo, K., Gomes, B., Ibrahim, L., ... & Manyika, J. (2024). AI in action: Accelerating progress towards the Sustainable Development Goals. arXiv preprint arXiv:2407.02711.
Goralski, M. A., & Tan, T. K. (2022). Artificial intelligence and poverty alleviation: Emerging innovations and their implications for management education and sustainable development. The International Journal of Management Education, 20(3), 100662.
https://doi.org/10.1016/j.ijme.2022.100662
How, M. L., Cheah, S. M., Khor, A. C., & Chan, Y. J. (2020). Artificial intelligence-enhanced predictive insights for advancing financial inclusion: A human-centric ai-thinking approach. Big Data and Cognitive Computing, 4(2).
https://doi.org/10.3390/bdcc4020008
Heeks, R. (2006). Theorizing ICT4D research. Information Technologies & International Development, 3(3), pp-1.
Jean, N., Burke, M., Xie, M., Alampay Davis, W. M., Lobell, D. B., & Ermon, S. (2016). Combining satellite imagery and machine learning to predict poverty. Science, 353(6301), 790-794.
https://doi.org/10.1126/science.aaf7894
Jiang, Y., Zhang, L., Li, Y., Lin, J., Li, J., Zhou, G., ... & Xiao, Z. (2021). Evaluation of county level poverty alleviation progress by deep learning and satellite observations. Big Earth Data, 5(4), 576-592.
https://doi.org/10.1080/20964471.2021.1967259
Kakeu, C. B. P., Wendji, C. M., Kouhomou, C. Z., & Kamdoum, G. C. M. (2024). Can technological innovations contribute to more overcome the issue of poverty reduction in africa?. Technology in Society, 76, 102463.
https://doi.org/10.1016/j.techsoc.2024.102463
Khan, R. U., Richardson, C., & Salamzadeh, Y. (2022). Spurring competitiveness, social and economic performance of family-owned SMEs through social entrepreneurship; a multi analytical SEM & ANN perspective. Technological Forecasting and Social Change, 184, 122047.
https://doi.org/10.1016/j.techfore.2022.122047
Kim, E., Jang, G. Y., & Kim, S. H. (2022). How to apply artificial intelligence for social innovations. Applied Artificial Intelligence, 36(1), 2031819.
https://doi.org/10.1080/08839514.2022.2031819
Lamichhane, B. R., Isnan, M., & Horanont, T. (2025). Exploring machine learning trends in poverty mapping: A review and meta-analysis. Science of Remote Sensing,100200.
https://doi.org/10.1016/j.srs.2025.100200
Lis-Gutiérrez, J. P., Gaitán-Angulo, M., & Cubillos-Diaz, J. (2020). Spending Level of Displaced Population Returned to La Palma, Cundinamarca (2018): A Machine Learning Application. Migration Letters, 17(5), 639-649.
https://doi.org/10.33182/ml.v17i5.693
Luan, H., Geczy, P., Lai, H., Gobert, J., Yang, S. J., Ogata, H., ... & Tsai, C. C. (2020). Challenges and future directions of big data and artificial intelligence in education. Frontiers in psychology, 11, 580820.
https://doi.org/10.3389/fpsyg.2020.580820
Meghraoui, K., Sebari, I., Pilz, J., Ait El Kadi, K., & Bensiali, S. (2024). Applied deep learning based crop yield prediction: A systematic analysis of current developments and potential challenges. Technologies, 12(4), 43.
https://doi.org/10.3390/technologies12040043
Mhlanga, D. (2021). Artificial intelligence in the industry 4.0, and its impact on poverty, innovation, infrastructure development, and the sustainable development goals: Lessons from emerging economies?. Sustainability, 13(11), 5788.
https://doi.org/10.3390/su13115788
Mishra, S., Satapathy, S. K., Cho, S. B., Mohanty, S. N., Sah, S., & Sharma, S. (2024). Advancing COVID-19 poverty estimation with satellite imagery-based deep learning techniques: a systematic review. Spatial Information Research, 32(5), 583-592.
https://doi.org/10.1007/s41324-024-00584-y
Mulgan, G., Tucker, S., Ali, R. & Sanders, B. (2007). Social Innovation: what it is, why it matters, how it can be accelerated. London: University of Oxford, Young Foundation. Retrieved June 08, 2020 from
https://youngfoundation.org/wp-content/uploads/2012/10/Social-Innovation-what-it-is-why-it-matters-how-it-can-be-accelerated-March-2007.pdf
Pandey, P. C., & Pandey, M. (2023). Highlighting the role of agriculture and geospatial technology in food security and sustainable development goals. Sustainable Development, 31(5), 3175-3195.
https://doi.org/10.1002/sd.2600
Pérez-Durán, I., Acebillo-Baqué, M., & Comellas-Bonsfills, J. M. (2024). Teaching social inclusion, public policy and governance through active learning and educational games. Teaching Public Administration, 01447394241307506.
https://doi.org/10.1177/01447394241307506
Purroy Vasquez, R., Aguilar Lasserre, A. A., Meza Palacios, R., & Fernández Lambert, G. (2024). Artificial neural network (ANN) in forecasting of poverty line and economic-energetic efficiencies into the maize-based agroecosystems. Archives of Agronomy and Soil Science, 70(1), 1-17.
https://doi.org/10.1080/03650340.2023.2287751
Qi, Z., Pan, J., & Feng, Y. (2024). Spatial Identification and Distribution Pattern of the Complexity of Rural Poverty in China Using Multisource Spatial Data. Complexity, 2024(1), 7012402.
https://doi.org/10.1155/2024/7012402
Raqib, M., & George, P. N. (2024). TechCare: Transformative Innovations in Addressing the Psychosocial Challenges of Cancer Care in Kerala, India. Indian Journal of Medical and Paediatric Oncology, 45(03), 256-262.
https://doi.org/10.1055/s-0044-1787150
Ravallion, M. (2020). On measuring global poverty. Annual Review of Economics, 12(1), 167-188.
https://doi.org/10.1146/annurev-economics-081919-022924
Sadabadi, A. A., Rahimirad, Z., & Nikijoo, I. (2024). Enhancing cross-sector partnerships in energy saving through social entrepreneurship: a social network analysis approach. Energy Research & Social Science, 109, 103412.
https://doi.org/10.1016/j.erss.2024.103412
Sanchez-Martinez, M., & Davis, P. (2014). A review of the economic theories of poverty. National Institute of Economic and Social Research (NIESR) Discussion Papers, (435).
Sandelowski, M., Barroso, J., & Voils, C. I. (2007). Using qualitative metasummary to synthesize qualitative and quantitative descriptive findings. Research in nursing & health, 30(1), 99-111.
SDG Indicators. (2024). Retrieved May 2, 2025, from
https://unstats.un.org/sdgs/report/2024/
Sen, A. (2010). The mobile and the world. Information Technologies & International Development, 6(SE), pp-1.
Shin, Y., & Kim, J. (2018). Data-centered persuasion: Nudging user's prosocial behavior and designing social innovation. Computers in Human Behavior, 80, 168-178.
https://doi.org/10.1016/j.chb.2017.11.009
Van der Have, R. P., & Rubalcaba, L. (2016). Social innovation research: An emerging area of innovation studies?. Research Policy, 45(9), 1923-1935.
https://doi.org/10.1016/j.respol.2016.06.010
Wang, L. (2021). Improving the performance of precision poverty alleviation based on big data mining and machine learning. Journal of Intelligent & Fuzzy Systems, 40(4), 6617-6628.
https://doi.org/10.3233/JIFS-189498
Westley, F., Antadze, N., Riddell, D. J., Robinson, K., & Geobey, S. (2014). Five configurations for scaling up social innovation: Case examples of nonprofit organizations from Canada. The Journal of Applied Behavioral Science, 50(3), 234-260.
https://doi.org/10.1177/00218863145329
Woolcock, M. (1998). Social capital and economic development: Toward a theoretical synthesis and policy framework. Theory and society, 27(2), 151-208.
Yang, D., Luan, W., Yang, J., Xue, B., Zhang, X., Wang, H., & Pian, F. (2022). The contribution of data-driven poverty alleviation funds in achieving mid-21st-Century multidimensional poverty alleviation planning. Humanities and Social Sciences Communications, 9(1).
https://doi.org/10.1057/s41599-022-01180-x
Yu, L., & Zhai, X. (2024). Use of artificial intelligence to address health disparities in low-and middle-income countries: a thematic analysis of ethical issues. Public Health, 234, 77-83.
https://doi.org/10.1016/j.puhe.2024.05.029
Zaman, B., Sharma, A., Ram, C., Kushwah, R., Muradia, R., Warjri, A., ... & Lyngdoh, M. K. (2023). Modeling education impact: a machine learning-based approach for improving the quality of school education. Journal of Computers in Education, 1-34.
https://doi.org/10.1007/s40692-023-00297-5
Zhang, W., Lei, T., Gong, Y., Zhang, J., & Wu, Y. (2022). Using explainable artificial intelligence to identify key characteristics of deep poverty for each household. Sustainability, 14(16), 9872.
https://doi.org/10.3390/su14169872
Zhongchen, G., Jie, H., & Chen, C. (2023). Intelligent transformation of financial services of agricultural cooperatives based on edge computing and deep learning. Soft Computing, 1-10.
https://doi.org/10.1007/s00500-023-08538-6