Journal of Information Systems Engineering and Management

A Framework for AI-driven Rural Revitalization Strategies: Balancing Brand Image, Cultural Compliance and Consumer Behavior Focusing on Agri Products Packaging Designs
Chen Tao 1 2, Mohamed Razeef Abdul Razak 3 * , Yuyang Xia 4, Mingqian Peng 5
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1 Ph.D candidate, College of Creative Arts, Universiti Teknologi MARA, Shah Alam, Malaysia
2 Assistant Professor (LuXun Academy of Arts), Yancheng Kindergarten Teachers College, Yancheng, China
3 Doctor, College of Creative Arts, Universiti Teknologi MARA, Shah Alam, Malaysia
4 Bachelor, Academy of Humanities, Central Academy of Fine Arts, Beijing, China
5 Bachelor, Academy of Fine Arts, Jiangsu Second Normal University, Nanjing, China
* Corresponding Author
Research Article

Journal of Information Systems Engineering and Management, 2024 - Volume 9 Issue 4, Article No: 27319
https://doi.org/10.55267/iadt.07.15214

Published Online: 27 Sep 2024

Views: 127 | Downloads: 80

How to cite this article
APA 6th edition
In-text citation: (Tao et al., 2024)
Reference: Tao, C., Abdul Razak, M. R., Xia, Y., & Peng, M. (2024). A Framework for AI-driven Rural Revitalization Strategies: Balancing Brand Image, Cultural Compliance and Consumer Behavior Focusing on Agri Products Packaging Designs. Journal of Information Systems Engineering and Management, 9(4), 27319. https://doi.org/10.55267/iadt.07.15214
Vancouver
In-text citation: (1), (2), (3), etc.
Reference: Tao C, Abdul Razak MR, Xia Y, Peng M. A Framework for AI-driven Rural Revitalization Strategies: Balancing Brand Image, Cultural Compliance and Consumer Behavior Focusing on Agri Products Packaging Designs. J INFORM SYSTEMS ENG. 2024;9(4):27319. https://doi.org/10.55267/iadt.07.15214
AMA 10th edition
In-text citation: (1), (2), (3), etc.
Reference: Tao C, Abdul Razak MR, Xia Y, Peng M. A Framework for AI-driven Rural Revitalization Strategies: Balancing Brand Image, Cultural Compliance and Consumer Behavior Focusing on Agri Products Packaging Designs. J INFORM SYSTEMS ENG. 2024;9(4), 27319. https://doi.org/10.55267/iadt.07.15214
Chicago
In-text citation: (Tao et al., 2024)
Reference: Tao, Chen, Mohamed Razeef Abdul Razak, Yuyang Xia, and Mingqian Peng. "A Framework for AI-driven Rural Revitalization Strategies: Balancing Brand Image, Cultural Compliance and Consumer Behavior Focusing on Agri Products Packaging Designs". Journal of Information Systems Engineering and Management 2024 9 no. 4 (2024): 27319. https://doi.org/10.55267/iadt.07.15214
Harvard
In-text citation: (Tao et al., 2024)
Reference: Tao, C., Abdul Razak, M. R., Xia, Y., and Peng, M. (2024). A Framework for AI-driven Rural Revitalization Strategies: Balancing Brand Image, Cultural Compliance and Consumer Behavior Focusing on Agri Products Packaging Designs. Journal of Information Systems Engineering and Management, 9(4), 27319. https://doi.org/10.55267/iadt.07.15214
MLA
In-text citation: (Tao et al., 2024)
Reference: Tao, Chen et al. "A Framework for AI-driven Rural Revitalization Strategies: Balancing Brand Image, Cultural Compliance and Consumer Behavior Focusing on Agri Products Packaging Designs". Journal of Information Systems Engineering and Management, vol. 9, no. 4, 2024, 27319. https://doi.org/10.55267/iadt.07.15214
ABSTRACT
In the contemporary era of technological evolution, the integration of artificial intelligence (AI) in rural development, specifically within the agri-products packaging sector, remains a crucial yet underexplored domain. This research navigates through this uncharted territory, seeking to unravel the complexities and opportunities that arise when AI intersects with rural environments. Employing a qualitative research design, this study engages a diverse array of stakeholders, including farmers, agro-processors, distributors, consumers, and policymakers. Through in-depth interviews, the research delves into real-world examples and case studies to capture the richness of experiences and perspectives. The findings of this research illuminate the complex interplay between AI, rural communities, and agri-product packaging. Stakeholder perspectives reveal diverse attitudes toward AI applications, while the exploration of packaging innovations showcases the transformative potential of technology in influencing consumer behavior. The study uncovers themes of economic empowerment, socio-cultural preservation, and the need for inclusive policies within rural contexts. This research is innovative in its synthesis of stakeholder perspectives, bridging the gap between technological assessments and social dynamics in rural environments. It contributes to the existing literature by offering a more comprehensive understanding of AI's impact on rural development and consumer behavior. The significance lies in its potential to inform policymakers, industry practitioners, and communities, fostering a more responsible and effective integration of AI technologies.
KEYWORDS
REFERENCES
  • Ab Manaf, N., Sa’at, N. H., A Rahim, N. A. A., Kamaruddin, S. N. A. A., Abdullah, S. S., & Omar, K. (2023). Assessing wellbeing: Profiling and socioeconomic status of Kenyir Lakeside community, Malaysia. Heliyon, 9(6), e16399.
  • Adewoye, J. O., & Olugbenga, B. (2018). Evaluation of the effects of E-HRM on customer deposits in selected deposit money banks in Nigeria. Noble International Journal of Economics and Financial Research ISSN, 03(09), 95-105.
  • Ai, T., Zhang, J., & Shao, J. (2023). Study on the coordinated poverty reduction effect of agricultural insurance and agricultural credit and its regional differences in China. Economic Analysis and Policy, 78, 835-844.
  • Ancín, M., Pindado, E., & Sánchez, M. (2022). New trends in the global digital transformation process of the agri-food sector: An exploratory study based on Twitter. Agricultural Systems, 203, 103520.
  • Ashraf, S. A., Siddiqui, A. J., Elkhalifa, A. E. O., Khan, M. I., Patel, M., Alreshidi, M., . . . Adnan, M. (2021). Innovations in nanoscience for the sustainable development of food and agriculture with implications on health and environment. Science of the Total Environment, 768, 144990.
  • Assadi, N. B., Samari, D., Farajollah Hosseini, S. J., & Omidi Najafabadi, M. (2021). The development model for palm processing industries with an emphasis on total innovation management (TIM) in Kerman province. Heliyon, 7(7), e07587.
  • Barbedo, J. G. A. (2023). A review on the combination of deep learning techniques with proximal hyperspectral images in agriculture. Computers and Electronics in Agriculture, 210, 107920.
  • Boix-Fayos, C., & de Vente, J. (2023). Challenges and potential pathways towards sustainable agriculture within the European Green Deal. Agricultural Systems, 207, 103634.
  • Chen, Y., He, M., & Xu, Y. (2023). Sustainable development of the mining sector for achieving common prosperity in Chinese rural areas. Resources Policy, 87, 104325.
  • Fenz, S., Neubauer, T., Friedel, J. K., & Wohlmuth, M. L. (2023). AI-and data-driven crop rotation planning. Computers and Electronics in Agriculture, 212, 108160.
  • Gholian-Jouybari, F., Hajiaghaei-Keshteli, M., Smith, N. R., Calvo, E. Z. R., Mejía-Argueta, C., & Mosallanezhad, B. (2024). An in-depth metaheuristic approach to design a sustainable closed-loop agri-food supply chain network. Applied Soft Computing, 150, 111017.
  • He, S., & Zhang, Y. (2022). Reconceptualizing the rural through planetary thinking: A field experiment of sustainable approaches to rural revitalisation in China. Journal of Rural Studies, 96, 42-52.
  • Hu, G., & You, F. (2023). An AI framework integrating physics-informed neural network with predictive control for energy-efficient food production in the built environment. Applied Energy, 348, 121450.
  • Huguet, J., Chassard, C., Lavigne, R., Irlinger, F., Souchon, I., Marette, S., . . . Pénicaud, C. (2023). Dataset about the Life Cycle Assessment of new fermented food products mixing cow milk and pea protein sources. Data in Brief, 48, 109263.
  • Huo, D., Malik, A. W., Ravana, S. D., Rahman, A. U., & Ahmedy, I. (2024). Mapping smart farming: Addressing agricultural challenges in data-driven era. Renewable and Sustainable Energy Reviews, 189, 113858.
  • Kalyanaraman, A., Burnett, M., Fern, A., Khot, L., & Viers, J. (2022). Special report: The AgAID AI institute for transforming workforce and decision support in agriculture. Computers and Electronics in Agriculture, 197, 106944.
  • Karpavičė, J., Hafith, I. A., Tambo, T., Chinello, F., Venytė, I., & Gegeckienė, L. (2023). Experimental approaches to NFC-enabled packaging for UX / CX of physical artefacts: A technology maturity study. Procedia Computer Science, 219, 577-585.
  • Kopka, A., & Grashof, N. (2022). Artificial intelligence: Catalyst or barrier on the path to sustainability?. Technological Forecasting and Social Change, 175, 121318.
  • Li, C., Ahmad, S. F., Ayassrah, A. Y. B. A., Irshad, M., Telba, A. A., Awwad, E. M., & Majid, M. I. (2023). Green production and green technology for sustainability: The mediating role of waste reduction and energy use. Heliyon, 9(12). https://doi.org/10.1016/j.heliyon.2023.e22496
  • Liang, D., Cao, W., Zhang, Y., & Xu, Z. (2024). A two-stage classification approach for AI technical service supplier selection based on multi-stakeholder concern. Information Sciences, 652, 119762.
  • Lintz, J. (2023). Provider satisfaction with artificial intelligence-based hand hygiene monitoring system during the COVID-19 pandemic: Study of a rural medical center. Journal of Chiropractic Medicine, 22(3), 197-203.
  • Liu, Q., Zhou, N., Cao, H., & Hong, X. (2020). Family socioeconomic status and Chinese young children’ social competence: Parenting processes as mediators and contextualizing factors as moderators. Children and Youth Services Review, 118, 105356.
  • Macht, J., Klink-Lehmann, J., & Venghaus, S. (2023). Eco-friendly alternatives to food packed in plastics: German consumers’ purchase intentions for different bio-based packaging strategies. Food Quality and Preference, 109, 104884.
  • Mbunge, E., Batani, J., Gaobotse, G., & Muchemwa, B. (2022). Virtual healthcare services and digital health technologies deployed during coronavirus disease 2019 (COVID-19) pandemic in South Africa: A systematic review. Global Health Journal, 6(2), 102-113.
  • Mishra, S., & Sharma, S. K. (2023). Advanced contribution of IoT in agricultural production for the development of smart livestock environments. Internet of Things, 22, 100724.
  • Mouratiadou, I., Lemke, N., Chen, C., Wartenberg, A., Bloch, R., Donat, M., . . . Bellingrath-Kimura, S. D. (2023). The Digital Agricultural Knowledge and Information System (DAKIS): Employing digitalisation to encourage diversified and multifunctional agricultural systems. Environmental Science and Ecotechnology, 100274. https://doi.org/10.1016/j.ese.2023.100274
  • Nath, P. C., Mishra, A. K., Sharma, R., Bhunia, B., Mishra, B., Tiwari, A., . . . Sridhar, K. (2024). Recent advances in artificial intelligence towards the sustainable future of agri-food industry. Food Chemistry, 138945. https://doi.org/10.1016/j.foodchem.2024.138945
  • Oliveira, A. C. L. de, Renato, N. dos S., Martins, M. A., Mendonça, I. M. de, Moraes, C. A., & Lago, L. F. R. (2023). Renewable energy solutions based on artificial intelligence for farms in the state of Minas Gerais, Brazil: Analysis and proposition. Renewable Energy, 204, 24-38.
  • Partel, V., Kakarla, S. C., & Ampatzidis, Y. (2019). Development and evaluation of a low-cost and smart technology for precision weed management utilizing artificial intelligence. Computers and electronics in agriculture, 157, 339-350.
  • Pettoello-Mantovani, C., & Olivieri, B. (2022). Food safety and public health within the frame of the EU legislation. Global Pediatrics, 2, 100020.
  • Polas, M. R. H., Kabir, A. I., Jahanshahi, A. A., Sohel-Uz-Zaman, A. S. M., Karim, R., & Tabash, M. I. (2023). Rural entrepreneurs behaviors towards green innovation: Empirical evidence from Bangladesh. Journal of Open Innovation: Technology, Market, and Complexity, 9(1), 100020.
  • Raymond, L., Castonguay, A., Doyon, O., & Paré, G. (2022). Nurse practitioners’ involvement and experience with AI-based health technologies: A systematic review. Applied Nursing Research, 66, 151604.
  • Real, F. J., Whitehead, M., Ollberding, N. J., Rosen, B. L., Meisman, A., Crosby, L. E., . . . Herbst, R. (2023). A virtual reality curriculum to enhance residents’ behavioral health anticipatory guidance skills: A pilot trial. Academic Pediatrics, 23(1), 185-192
  • Robert, F. C., Frey, L. M., & Sisodia, G. S. (2021). Village development framework through self-help-group entrepreneurship, microcredit, and anchor customers in solar microgrids for cooperative sustainable rural societies. Journal of Rural Studies, 88, 432-440.
  • Ruf, J., Emberger-Klein, A., & Menrad, K. (2022). Consumer response to bio-based products—A systematic review. Sustainable Production and Consumption, 34, 353-370.
  • Saberi Riseh, R., Vatankhah, M., Hassanisaadi, M., & Kennedy, J. F. (2023). Chitosan-based nanocomposites as coatings and packaging materials for the postharvest improvement of agricultural product: A review. Carbohydrate Polymers, 309, 120666.
  • Schmitt, M. (2023). Deep learning in business analytics: A clash of expectations and reality. International Journal of Information Management Data Insights, 3(1), 100146.
  • Thapa, C., & Camtepe, S. (2021). Precision health data: Requirements, challenges and existing techniques for data security and privacy. Computers in Biology and Medicine, 129, 104130.
  • Tu, J., Aznoli, F., Jafari Navimipour, N., & Yalcin, S. (2022). A new service recommendation method for agricultural industries in the fog-based Internet of Things environment using a hybrid meta-heuristic algorithm. Computers & Industrial Engineering, 172, 108605.
  • Vo, V., Chen, G., Aquino, Y. S. J., Carter, S. M., Do, Q. N., & Woode, M. E. (2023). Multi-stakeholder preferences for the use of artificial intelligence in healthcare: A systematic review and thematic analysis. Social Science & Medicine, 338, 116357.
  • Whitehead, D., Cowell, C. R., Lavorgna, A., & Middleton, S. E. (2021). Countering plant crime online: Cross-disciplinary collaboration in the FloraGuard study. Forensic Science International: Animals and Environments, 1, 100007.
  • Yigitcanlar, T., Kankanamge, N., Regona, M., Ruiz Maldonado, A., Rowan, B., Ryu, A., . . . Li, R. Y. M. (2020). Artificial intelligence technologies and related urban planning and development concepts: How are they perceived and utilized in Australia?. Journal of Open Innovation: Technology, Market, and Complexity, 6(4), 187.
  • Yin, X., Chen, J., & Li, J. (2022). Rural innovation system: Revitalize the countryside for a sustainable development. Journal of Rural Studies, 93, 471-478.
  • Zahlan, A., Ranjan, R. P., & Hayes, D. (2023). Artificial intelligence innovation in healthcare: Literature review, exploratory analysis, and future research. Technology in Society, 74, 102321.
  • Zhang, S. (2022). Research on energy-saving packaging design based on artificial intelligence. Energy Reports, 8, 480-489.
  • Zhou, Y., Li, Y., & Xu, C. (2020). Land consolidation and rural revitalization in China: Mechanisms and paths. Land Use Policy, 91, 104379.
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