Journal Screenshot

International Journal of Academic Research in Economics and Management Sciences

Open Access Journal

ISSN: 2226-3624

The Road Towards Sustainability: Transforming Consumption Patterns with Artificial Intelligence

Cornelia-Rodica Jude

http://dx.doi.org/10.6007/IJAREMS/v13-i2/21805

Open access

This study aims to contribute to a better understanding of the role of artificial intelligence (AI) in transforming modern consumption patterns and supporting international efforts to transition towards sustainable consumption. To meet this objective, a systematic literature review (SLR) was conducted, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for literature search and selection. Bibliographic searches were conducted in two well-regarded databases: Elsevier Scopus (Scopus) and the Web of Science (WoS), with the final search completed on May 1, 2024. Identified studies were assessed for eligibility and eleven peer-reviewed English articles published in respected journals were included in the final review. The author synthesized the included articles and used qualitative methods to present the current knowledge vis-à-vis AI’s applications for sustainable consumption. Grey literature was consulted to avoid source selection bias. This SLR led to a conceptual understanding of how AI contributes to meeting Sustainable Development Goal 12 (SDG12) of the 2030 United Nations (UN) Agenda for Sustainable Development, particularly in evaluating, ensuring, and promoting sustainable consumption behaviors. The study also discusses the main challenges of adopting AI for advancing sustainable consumption initiatives. This theoretical understanding has important implications for informing sustainable consumption initiatives. The study also acknowledges its limitations, including the risk of bias and the questions left unanswered within the existing body of research.

Abbasi, M., & Hanandeh, A. E. (2016). Forecasting municipal solid waste generation using artificial intelligence modeling approaches. Waste Management, 56, 13–22. https://doi.org/10.1016/j.wasman.2016.05.018
Acuti, D., Pizzetti, M., & Dolnicar, S. (2022). When sustainability backfires: A review on the unintended negative side?effects of product and service sustainability on consumer behavior. Psychology & Marketing, 39(10), 1933–1945.
https://doi.org/10.1002/mar.21709
Agrawal, M., Dutta, S., Kelly, R., & Millán, I. (2021). COVID-19: An inflection point for Industry 4.0. McKinsey & Company. Retrieved May 5, 2024, from https://www.mckinsey.com/capabilities/operations/our-insights/covid-19-an-inflection-point-for-industry-40
Akenji, L., Bengtsson, M., Briggs, E., Chiu, A., Daconto, G., Fadeeva, Z., Fotiou, S., Gandhi, R., Mathews, C., Metternicht, G., Mohanty, B., Salem, J., Sang-Arun, J., Srisakulchairak, T., Schandl, H., & Tabucanon, M. (2015). Sustainable Consumption and Production. A Handbook for Policymakers. Global edition. United Nations Environment Programme (UNEP). Retrieved May 1, 2024, from:
https://wedocs.unep.org/bitstream/handle/20.500.11822/9660/-Sustainable_Consumption_and_Production_a_Handbook_for_PolicymakersSustainable_Consumptio.pdf
Akram, S. V., Malik, P. K., Singh, R., Gehlot, A., Juyal, A., Ghafoor, K. Z., & Shrestha, S. (2022). Implementation of Digitalized Technologies for Fashion Industry 4.0: Opportunities and Challenges. Scientific Programming, 2022, 1–17. https://doi.org/10.1155/2022/7523246
Amani, M. A., & Sarkodie, S. A. (2022). Mitigating the spread of contamination in meat supply chain management using deep learning. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-08993-5
Bartmann, M. (2022). The Ethics of AI-Powered Climate Nudging—How Much AI Should We Use to Save the Planet? Sustainability, 14(9), 5153. https://doi.org/10.3390/su14095153
Bleher, H., & Braun, M. (2022). Diffused responsibility: attributions of responsibility in the use of AI-driven clinical decision support systems. AI And Ethics, 2(4), 747–761. https://doi.org/10.1007/s43681-022-00135-x
Borsatto, J. M. L. S., Marcolin, C. B., Abdalla, E. C., & Amaral, F. D. (2024). Aligning community outreach initiatives with SDGs in a higher education institution with artificial intelligence. Cleaner and Responsible Consumption, 12, 100160. https://doi.org/10.1016/j.clrc.2023.100160
Boström, M. (2020). The social life of mass and excess consumption. Environmental Sociology, 6(3), 268–278. https://doi.org/10.1080/23251042.2020.1755001
Burger, B., Kanbach, D. K., Kraus, S., Breier, M., & Corvello, V. (2023). On the use of AI-based tools like ChatGPT to support management research. European Journal of Innovation Management, 26(7), 233–241. https://doi.org/10.1108/ejim-02-2023-0156
Cao, P., & Liu, S. (2023). The Impact of Artificial Intelligence Technology Stimuli on Sustainable Consumption Behavior: Evidence from Ant Forest Users in China. Behavioral Sciences, 13(7), 604. https://doi.org/10.3390/bs13070604
Carrera-Rivera, A., Ochoa, W., Larrinaga, F., & Lasa, G. (2022). How to conduct a systematic literature review: A quick guide for computer science research. MethodsX, 9, 101895. https://doi.org/10.1016/j.mex.2022.101895
Chen, J. J., & Lin, J. C. (2024). Artificial intelligence as a double-edged sword: Wielding the POWER principles to maximize its positive effects and minimize its negative effects. Contemporary Issues in Early Childhood, 25(1), 146-153.
https://doi.org/10.1177/14639491231169813
Choy, K. L., Ho, G. T. S., Lee, C. K. H., Lam, H. Y., Cheng, S. W. Y., Siu, P. K. Y., Pang, G. K. H., Tang, V., Lee, J. C. H., & Tsang, Y. P. (2016). A recursive operations strategy model for managing sustainable chemical product development and production. International Journal of Production Economics, 181, 262–272.
https://doi.org/10.1016/j.ijpe.2016.07.011
Crouse, M. (2024). GPT-4 Cheat Sheet: What Is GPT-4, and What Is it Capable Of? TechRepublic. Retrieved June 9, 2024 from
https://www.techrepublic.com/article/gpt-4-cheat-sheet/
Curry, R. (2023). Recent data shows AI job losses are rising, but the numbers don’t tell the full story. CNBC. Retrieved June 9, 2024, from https://www.cnbc.com/2023/12/16/ai-job-losses-are-rising-but-the-numbers-dont-tell-the-full-story.html
Dawkins, E., André, K., Axelsson, K., Benoist, L., Swartling, Å. G., & Persson, Å. (2019). Advancing sustainable consumption at the local government level: A literature review. Journal of Cleaner Production, 231, 1450–1462.
https://doi.org/10.1016/j.jclepro.2019.05.176
de Oliveira U. R., Gomes T. S. M., de Oliveira G. G., de Abreu J. C. A., Oliveira M. A., da Silva César A., & Aprigliano Fernandes V. (2022) Systematic Literature Review on Sustainable Consumption from the Perspective of Companies, People and Public Policies. Sustainability, 14(21), 13771. https://doi.org/10.3390/su142113771
Di, K., Chen, W., Shi, Q., Cai, Q., & Liu, S. (2024). Analyzing the impact of coupled domestic demand dynamics of green and low-carbon consumption in the market based on SEM-ANN. Journal of Retailing and Consumer Services, 79, 103856. https://doi.org/10.1016/j.jretconser.2024.103856
Diaz, R. A. C., Ghita, M., Copot, D., Birs, I. R., Muresan, C., & Ionescu, C. (2020). Context-Aware Control Systems: An Engineering Applications Perspective. IEEE Access, 8, 215550–215569. https://doi.org/10.1109/access.2020.3041357
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., … Wright, R. (2023). Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
Fang, B., Yu, J., Chen, Z., Osman, A. I., Farghali, M., Ihara, I., Hamza, E. H., Rooney, D. W., & Yap, P. (2023). Artificial intelligence for waste management in smart cities: a review. Environmental Chemistry Letters, 21(4), 1959–1989. https://doi.org/10.1007/s10311-023-01604-3
Ferrara, E. (2023). Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies. Sci, 6(1), 3. https://doi.org/10.3390/sci6010003
Fischer, M., Foord, D., Frecè, J., Hillebrand, K., Kissling-Näf, I., Meili, R., Peskova, M., Risi, D., Schmidpeter, R., & Stucki, T. (2023). In: Sustainable Business. SpringerBriefs in Business (105–116). Springer, Cham. https://doi.org/10.1007/978-3-031-25397-3_7
Fossa, F. (2024). Artificial intelligence and human autonomy: the case of driving automation. AI & Society. https://doi.org/10.1007/s00146-024-01955-7
Geissmar, J., Niemand, T., & Kraus, S. (2023). Surprisingly unsustainable: How and when hindsight biases shape consumer evaluations of unsustainable and sustainable products. Business Strategy and the Environment, 32(8), 5969–5991. https://doi.org/10.1002/bse.3468
Georgeson, L., Maslin, M., & Poessinouw, M. (2017). The global green economy: a review of concepts, definitions, measurement methodologies and their interactions. Geo: Geography and Environment, 4(1). https://doi.org/10.1002/geo2.36
Ghobakhloo, M. (2020). Industry 4.0, digitization, and opportunities for sustainability. Journal of Cleaner Production, 252, 119869. https://doi.org/10.1016/j.jclepro.2019.119869
Gonçalves, A. R., Pinto, D. C., Shuqair, S., Dalmoro, M., & Mattila, A. S. (2024). Artificial intelligence vs. autonomous decision-making in streaming platforms: A mixed-method approach. International Journal of Information Management, 76, 102748. https://doi.org/10.1016/j.ijinfomgt.2023.102748
Goodman, K., Zandi, D., Reis, A., & Vayena, E. (2020). Balancing risks and benefits of artificial intelligence in the health sector. Bulletin of the World Health Organization, 98(4). 230-230A. https://doi.org/10.2471/blt.20.253823
Goodwin, N., Harris, J. -M., Nelson, J. -A., Rajkarnikar, P. -J., Roach, B., & Torras, M. (2019). Consumption and the consumer society. In Principles of Economics in Context (2nd ed., pp. 188-215) Routledge eBooks. https://doi.org/10.4324/9780429438752-10
Gruetzemacher, R., & Whittlestone, J. (2022). The transformative potential of artificial intelligence. Futures, 135, 102884. https://doi.org/10.1016/j.futures.2021.102884
Guo, H., Huang, L., & Liang, D. (2022). Further promotion of sustainable development goals using science, technology, and innovation. The Innovation 3(6), 100325. https://doi.org/10.1016/j.xinn.2022.100325
Gupta, S., Langhans, S. D., Domisch, S., Fuso-Nerini, F., Felländer, A., Battaglini, M., Tegmark, M., & Vinuesa, R. (2021). Assessing whether artificial intelligence is an enabler or an inhibitor of sustainability at indicator level. Transportation Engineering, 4, 100064. https://doi.org/10.1016/j.treng.2021.100064
Haynes, P., & Alemna, D. (2022). A Systematic Literature Review of the Impact of Complexity Theory on Applied Economics. Economies, 10(8), 192. https://doi.org/10.3390/economies10080192
Heikkilä, M. (2023). AI’s carbon footprint is bigger than you think. MIT Technology Review. Retrieved June 9, 2024 from: https://www.technologyreview.com/2023/12/05/1084417/ais-carbon-footprint-is-bigger-than-you-think/
Hermann, E. (2022). Psychological targeting: nudge or boost to foster mindful and sustainable consumption? AI & Society, 38(2), 961–962. https://doi.org/10.1007/s00146-022-01403-4
Jackson, T. (2014). Sustainable consumption. In Atkinson, G., Dietz, S., Neumayer, E. & Agarwala, M. (Eds.) Handbook of Sustainable Development (2nd ed., pp. 279-290). Edward Elgar Publishing.
Jin, B. E., & Shin, D. C. (2020). Changing the game to compete: Innovations in the fashion retail industry from the disruptive business model. Business Horizons, 63(3), 301–311. https://doi.org/10.1016/j.bushor.2020.01.004
Jones, M. D., Hutcheson, S., & Camba, J. D. (2021). Past, present, and future barriers to digital transformation in manufacturing: A review. Journal of Manufacturing Systems, 60, 936–948. https://doi.org/10.1016/j.jmsy.2021.03.006
Jonkut?, G., & Staniškis, J. -K. (2019). The Role of Different Stakeholders in Implementing Sustainable Consumption and Production in Lithuania. Environmental Engineering and Management Journal, 18(3), 617–632. https://doi.org/10.30638/eemj.2019.057
Kothari, A., Demaria, F., & Acosta, A. (2014). Buen Vivir, Degrowth and Ecological Swaraj: Alternatives to sustainable development and the Green Economy. Development, 57(3–4), 362–375. https://doi.org/10.1057/dev.2015.24
Kubiszewski, I., Costanza, R., Franco, C., Lawn, P., Talberth, J., Jackson, T., & Aylmer, C. (2013). Beyond GDP: Measuring and achieving global genuine progress. Ecological Economics, 93, 57–68. https://doi.org/10.1016/j.ecolecon.2013.04.019
Lehner, M., Richter, J. L., & Mont, O. (2024). Digitalization: A Potential Tool for Sustainable Consumption? In: Bäckström, K., Egan-Wyer, C., Samsioe, E. (Eds) The Future of Consumption. Palgrave Macmillan, Cham. (p. 189–204). https://doi.org/10.1007/978-3-031-33246-3_12
Leismann, K., Schmitt, M., Rohn, H., & Baedeker, C. (2013). Collaborative Consumption: Towards a Resource-Saving Consumption Culture. Resources, 2(3), 184–203. https://doi.org/10.3390/resources2030184
Li, T., Das, S., Lee, H., Wang, D., Yao, B., & Zhang, Z. (2024). Human-Centered Privacy Research in the Age of Large Language Models. In Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3613905.3643983
Lim, W. M., & Rasul, T. (2022). Customer engagement and social media: Revisiting the past to inform the future. Journal of Business Research, 148, 325–342. https://doi.org/10.1016/j.jbusres.2022.04.068
Linardatos, P., Papastefanopoulos, V., & Kotsiantis, S. (2021). Explainable AI: A Review of Machine Learning Interpretability Methods. Entropy, 23(1), 18. https://doi.org/10.3390/e23010018
Litvinenko, V. S. (2019). Digital Economy as a Factor in the Technological Development of the Mineral Sector. Natural Resources Research, 29(3), 1521–1541. https://doi.org/10.1007/s11053-019-09568-4
Marcelin, J. R., Siraj, D. S., Victor, R., Kotadia, S., & Maldonado, Y. A. (2019). The Impact of Unconscious Bias in Healthcare: How to Recognize and Mitigate It. The Journal of Infectious Diseases, 220(220 Suppl 2), S62–S73. https://doi.org/10.1093/infdis/jiz214
McGuinness, P. (2023). GPT-4 Details Revealed. GPT-4: 1.8T parameter mixture-of-experts model trained on 13T tokens and optimized for inference. AI Changes Everything. Retrieved June 8, 2024 from: https://patmcguinness.substack.com/p/gpt-4-details-revealed
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A Survey on Bias and Fairness in Machine Learning. ACM Computing Surveys, 54(6), 1–35. https://doi.org/10.1145/3457607
Moser-Plautz, B., & Schmidthuber, L. (2023). Digital government transformation as an organizational response to the COVID-19 pandemic. Government Information Quarterly, 40(3), 101815. https://doi.org/10.1016/j.giq.2023.101815
Muraca, B. (2012). Towards a fair degrowth-society: Justice and the right to a ‘good life’ beyond growth. Futures, 44(6), 535–545.
https://doi.org/10.1016/j.futures.2012.03.014
Murta, F. T. (2023). Artificial Intelligence for Sustainability: What is the Role of AI in Advancing Targets for Sustainability. TEMA Project. Retrieved May 30, 2024 from: https://tema-project.eu/articles/artificial-intelligence-sustainability-what-role-ai-advancing-targets-sustainability
Naik, N., Hameed, B. M. Z., Shetty, D. K., Swain, D., Shah, M., Paul, R., Aggarwal, K., Ibrahim, S., Patil, V., Smriti, K., Shetty, S., Rai, B. P., Chlosta, P., & Somani, B. K. (2022). Legal and Ethical Consideration in Artificial Intelligence in Healthcare: Who Takes Responsibility? Frontiers in Surgery, 9. https://doi.org/10.3389/fsurg.2022.862322
Nicola, M., Alsafi, Z., Sohrabi, C., Kerwan, A., Al-Jabir, A., Iosifidis, C., Agha, M., & Agha, R. (2020). The socio-economic implications of the coronavirus pandemic (COVID-19): A review. International Journal of Surgery, 78, 185–193. https://doi.org/10.1016/j.ijsu.2020.04.018
O’Sullivan, P., & Kraisornsuthasinee, S. (2019). YOU EARN as YOU LIVE as YOU VALUE. Sustainability Accounting, Management and Policy Journal, 11(2), 429–450. https://doi.org/10.1108/sampj-12-2018-0362
Ofstad, S., Westly, L., & Bratelli, T. (1994). Norway, miljøverndepartementet, symposium on sustainable consumption. In Symposium: Sustainable Consumption (pp. 19-20).
Page, M. J., Moher, D., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., . . . McKenzie, J. E. (2021). PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. BMJ, n160. https://doi.org/10.1136/bmj.n160
Pannucci, C. J., & Wilkins, E. G. (2010). Identifying and avoiding bias in research. Plastic and reconstructive surgery, 126(2), 619–625.
https://doi.org/10.1097/PRS.0b013e3181de24bc
Pascucci, F., Savelli, E., & Gistri, G. (2023). How digital technologies reshape marketing: evidence from a qualitative investigation. Italian Journal of Marketing, 2023, 27-58 https://doi.org/10.1007/s43039-023-00063-6
Price, W. N., & Cohen, I. G. (2019). Privacy in the age of medical big data. Nature Medicine, 25(1), 37–43. https://doi.org/10.1038/s41591-018-0272-7
Radanliev, P., Santos, O., Brandon-Jones, A. & Joinson A. (2024) Ethics and responsible AI deployment. Frontiers in Artificial Intelligence, 7. https://doi.org/10.3389/frai.2024.1377011
Rana, J., & Paul, J. (2017). Consumer behavior and purchase intention for organic food: A review and research agenda. Journal of Retailing and Consumer Services, 38, 157–165. https://doi.org/10.1016/j.jretconser.2017.06.004
Ravanera, C., & Kaplan, S. (2021). An Equity Lens on Artificial Intelligence. In Institute for Gender and the Economy. Institute for Gender and the Economy, Rotman School of Management, University of Toronto. Retrieved May 13, 2024, from https://www.gendereconomy.org/wp-content/uploads/2021/09/An-Equity-Lens-on-Artificial-Intelligence-Public-Version-English-1.pdf
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why Should I Trust You?”: Explaining the Predictions of Any Classifier. In KDD ’16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (22nd ed.). ACM Digital Library. https://doi.org/10.1145/2939672.2939778
Robbins, S., & Van Wynsberghe, A. (2022). Our New Artificial Intelligence Infrastructure: Becoming Locked into an Unsustainable Future. Sustainability, 14(8), 4829. https://doi.org/10.3390/su14084829
Robertsone, G., & Lapi?a, I. (2023). Digital transformation as a catalyst for sustainability and open innovation. Journal of Open Innovation, 9(1), 100017. https://doi.org/10.1016/j.joitmc.2023.100017
Rodriguez-Barrios, E. U., Melendez-Armenta, R. A., Garcia-Aburto, S. G., Lavoignet-Ruiz, M., Sandoval-Herazo, L. C., Molina-Navarro, A., & Morales-Rosales, L. A. (2021a). Bayesian Approach to Analyze Reading Comprehension: A Case Study in Elementary School Children in Mexico. Sustainability, 13(8), 4285. https://doi.org/10.3390/su13084285
Ruggerio, C. A. (2021). Sustainability and sustainable development: A review of principles and definitions. Science of the Total Environment, 786, 147481. https://doi.org/10.1016/j.scitotenv.2021.147481
Saeed, S., Altamimi, S. A., Alkayyal, N. A., Alshehri, E., & Alabbad, D. A. (2023). Digital Transformation and Cybersecurity Challenges for Businesses Resilience: Issues and Recommendations. Sensors, 23(15). https://doi.org/10.3390/s23156666
Saghai Y. (2013). Salvaging the concept of nudge. Journal of Medical Ethics, 39(8), 487–493. https://doi.org/10.1136/medethics-2012-100727
Sánchez-García, E., Martínez-Falcó, J., Marco-Lajara, B., & Manresa-Marhuenda, E. (2024). Revolutionizing the circular economy through new technologies: A new era of sustainable progress. Environmental Technology & Innovation, 33, 103509. https://doi.org/10.1016/j.eti.2023.103509
Sankaran, K. (2020). Carbon Emission and Plastic Pollution: How Circular Economy, Blockchain, and Artificial Intelligence Support Energy Transition? Journal of Innovation Management, 7(4), 7–13. https://doi.org/10.24840/2183-0606_007.004_0002
Sarmento, E. M., & Loureiro, S. M. C. (2021). Exploring the Role of Norms and Habit in Explaining Pro-Environmental Behavior Intentions in Situations of Use Robots and AI Agents as Providers in Tourism Sector. Sustainability, 13(24), 13928. https://doi.org/10.3390/su132413928
Schmidt, A. T., & Engelen, B. (2020). The ethics of nudging: An overview. Philosophy Compass, 15(4). https://doi.org/10.1111/phc3.12658
Sekulova, F., Kallis, G., Rodríguez-Labajos, B., & Schneider, F. (2013). Degrowth: from theory to practice. Journal of Cleaner Production, 38, 1–6. https://doi.org/10.1016/j.jclepro.2012.06.022
Shin, E., Kim, S., & Koh, A. (2022). Satisfaction Through Clothing Utilization and Environmental Sustainability Based on Fashion AI Curation Service. KSII Transactions on Internet and Information Systems, 16(9), 2867-2881, https://doi.org/10.3837/tiis.2022.09.002.
Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70, 263–286. https://doi.org/10.1016/j.jbusres.2016.08.001
Soprano, M., Roitero, K., La Barbera, D., Ceolin, D., Spina, D., Demartini, G., & Mizzaro, S. (2024). Cognitive Biases in Fact-Checking and Their Countermeasures: A review. Information Processing & Management, 61(3), 103672. https://doi.org/10.1016/j.ipm.2024.103672
Spair, R. (2024). The Benefits of AI in Project Management: A Comprehensive Guide. Medium. Retrieved June 1, 2024, from: https://medium.com/@rickspair/the-benefits-of-ai-in-project-management-a-comprehensive-guide-472f5bb5686c
Sravanthi, J., Sobti, R., Semwal, A., Shravan, M., Al-Hilali, A. A., & Bader Alazzam, M. (2023). AI-Assisted Resource Allocation in Project Management. In 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 70-74, https://doi.org/10.1109/ICACITE57410.2023.10182760.
Ter Huurne, M., Ronteltap, A., Corten, R., & Buskens, V. (2017). Antecedents of trust in the sharing economy: A systematic review. Journal of Consumer Behaviour, 16(6), 485–498. https://doi.org/10.1002/cb.1667
Thaler, R. H., & Sunstein, C. R. (2012). Nudge: The Final Edition. Penguin UK.
Thøgersen, J., & Ölander, F. (2002). Human values and the emergence of a sustainable consumption pattern: A panel study. Journal of Economic Psychology, 23(5), 605–630. https://doi.org/10.1016/s0167-4870(02)00120-4
Ting, D. S. W., Carin, L., Dzau, V., & Wong, T. Y. (2020). Digital technology and COVID-19. Nature Medicine, 26(4), 459–461. https://doi.org/10.1038/s41591-020-0824-5
Tyagi, S., & Sarma, K. (2020). Qualitative assessment, geochemical characterization and corrosion-scaling potential of groundwater resources in Ghaziabad district of Uttar Pradesh, India. Groundwater for Sustainable Development, 10, 100370. https://doi.org/10.1016/j.gsd.2020.100370
UN. Secretary-General & World Commission on Environment and Development. (1987). Report of the World Commission on Environment and Development: Our Common Future. In United Nations Digital Library. United Nations. Retrieved May 1, 2024, from https://digitallibrary.un.org/record/139811?ln=en&v=pdf
Verma, S., Sharma, R., Deb, S., & Maitra, D. (2021). Artificial intelligence in marketing: Systematic review and future research direction. International Journal of Information Management Data Insights, 1(1), 100002.
https://doi.org/10.1016/j.jjimei.2020.100002
Vermeir, I., & Verbeke, W. (2006). Sustainable Food Consumption: Exploring the Consumer “Attitude – Behavioral Intention” Gap. Journal of Agricultural and Environmental Ethics, 19(2), 169–194. https://doi.org/10.1007/s10806-005-5485-3
Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., Felländer, A., Langhans, S. D., Tegmark, M., & Nerini, F. F. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature Communications, 11(1). https://doi.org/10.1038/s41467-019-14108-y
White, K., Habib, R., & Hardisty, D. J. (2019). How to SHIFT Consumer Behaviors to be More Sustainable: A Literature Review and Guiding Framework. Journal of Marketing, 83(3), 22–49. https://doi.org/10.1177/0022242919825649
Wilkens, U. (2020). Artificial intelligence in the workplace – A double-edged sword. International Journal of Information and Learning Technology, 37(5), 253–265. https://doi.org/10.1108/ijilt-02-2020-0022
Wohlin, C., Runeson, P., Höst, M., Ohlsson, M. C., Regnell, B., & Wesslén, A. (2012). Systematic Literature Reviews. In: Experimentation in Software Engineering (p. 45–54). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29044-2_4
Zech, H. (2021). Liability for AI: public policy considerations. ERA-Forum, 22(1), 147–158. https://doi.org/10.1007/s12027-020-00648-0
Zhang, J., Luna-Reyes, L. F., Jarman, H., & Tayi, G. K. (2014). Information systems to support sustainable consumption and sustainable supply. Information Technology and Management, 16(1), 1–4. https://doi.org/10.1007/s10799-014-0206-0
Zhang, K., & Tao, J. (2022). Threat to Nature Connectedness: How Does It Influence Consumers’ Preferences for Automated Products? Sustainability, 14(1), 485. https://doi.org/10.3390/su14010485
Zukin, S., & Maguire, J. -S. (2004). Consumers and Consumption. Annual Review of Sociology, 30(1), 173–197. https://doi.org/10.1146/annurev.soc.30.012703.110553

(Jude, 2024)
Jude, C.-R. (2024). The Road Towards Sustainability: Transforming Consumption Patterns with Artificial Intelligence. International Journal of Academic Research in Economics and Management and Sciences, 13(2), 466–486.