A Feasibility Discussion: Is ML Suitable for Predicting Sustainable Patterns in Consumer Product Preferences?
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Saaty, Thomas L. & Vargas, Luis G. & Dellmann, Klaus, 2003. "The allocation of intangible resources: the analytic hierarchy process and linear programming," Socio-Economic Planning Sciences, Elsevier, vol. 37(3), pages 169-184, September.
- Kazemzadeh, Mohammad-Rasool & Amjadian, Ali & Amraee, Turaj, 2020. "A hybrid data mining driven algorithm for long term electric peak load and energy demand forecasting," Energy, Elsevier, vol. 204(C).
- Voyant, Cyril & Notton, Gilles & Kalogirou, Soteris & Nivet, Marie-Laure & Paoli, Christophe & Motte, Fabrice & Fouilloy, Alexis, 2017. "Machine learning methods for solar radiation forecasting: A review," Renewable Energy, Elsevier, vol. 105(C), pages 569-582.
- Žaneta Piligrimienė & Andželika Žukauskaitė & Hubert Korzilius & Jūratė Banytė & Aistė Dovalienė, 2020. "Internal and External Determinants of Consumer Engagement in Sustainable Consumption," Sustainability, MDPI, vol. 12(4), pages 1-20, February.
- Prentice, Catherine & Chen, Jue & Wang, Xuequn, 2019. "The influence of product and personal attributes on organic food marketing," Journal of Retailing and Consumer Services, Elsevier, vol. 46(C), pages 70-78.
- Thakur, Rakhi, 2016. "Understanding Customer Engagement and Loyalty: A Case of Mobile Devices for Shopping," Journal of Retailing and Consumer Services, Elsevier, vol. 32(C), pages 151-163.
- Odey Alshboul & Ali Shehadeh & Ghassan Almasabha & Ali Saeed Almuflih, 2022. "Extreme Gradient Boosting-Based Machine Learning Approach for Green Building Cost Prediction," Sustainability, MDPI, vol. 14(11), pages 1-20, May.
- Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
- Chengyan Yue & Yufeng Lai & Jingjing Wang & Paul Mitchell, 2020. "Consumer Preferences for Sustainable Product Attributes and Farm Program Features," Sustainability, MDPI, vol. 12(18), pages 1-19, September.
- Monier-Dilhan, Sylvette & Bergès, Fabian, 2016. "Consumers' Motivations Driving Organic Demand: Between Self-interest and Sustainability," Agricultural and Resource Economics Review, Cambridge University Press, vol. 45(3), pages 522-538, December.
- Sonja Maria Geiger & Daniel Fischer & Ulf Schrader, 2018. "Measuring What Matters in Sustainable Consumption: An Integrative Framework for the Selection of Relevant Behaviors," Sustainable Development, John Wiley & Sons, Ltd., vol. 26(1), pages 18-33, January.
- Alizamir, Meysam & Kim, Sungwon & Kisi, Ozgur & Zounemat-Kermani, Mohammad, 2020. "A comparative study of several machine learning based non-linear regression methods in estimating solar radiation: Case studies of the USA and Turkey regions," Energy, Elsevier, vol. 197(C).
- Julia Wojciechowska-Solis & Anetta Barska, 2021. "Exploring the Preferences of Consumers’ Organic Products in Aspects of Sustainable Consumption: The Case of the Polish Consumer," Agriculture, MDPI, vol. 11(2), pages 1-17, February.
- McFadden, Jonathan R. & Huffman, Wallace E., 2017. "Willingness-to-pay for natural, organic, and conventional foods: The effects of information and meaningful labels," Food Policy, Elsevier, vol. 68(C), pages 214-232.
- Fatima Lambarraa-Lehnhardt & Rico Ihle & Hajar Elyoubi, 2021. "How Successful Is Origin Labeling in a Developing Country Context? Moroccan Consumers’ Preferences toward Local Products," Sustainability, MDPI, vol. 13(15), pages 1-17, July.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
- Jūratė Banytė & Laura Šalčiuvienė & Aistė Dovalienė & Žaneta Piligrimienė & Włodzimierz Sroka, 2020. "Sustainable Consumption Behavior at Home and in the Workplace: Avenues for Innovative Solutions," Sustainability, MDPI, vol. 12(16), pages 1-24, August.
- Carmen Bălan, 2020. "How Does Retail Engage Consumers in Sustainable Consumption? A Systematic Literature Review," Sustainability, MDPI, vol. 13(1), pages 1-25, December.
- András István Kun & Marietta Kiss, 2021. "On the Mechanics of the Organic Label Effect: How Does Organic Labeling Change Consumer Evaluation of Food Products?," Sustainability, MDPI, vol. 13(3), pages 1-25, January.
- Žaneta Piligrimienė & Andželika Žukauskaitė & Hubert Korzilius & Jūratė Banytė & Aistė Dovalienė, 2020. "Internal and External Determinants of Consumer Engagement in Sustainable Consumption," Sustainability, MDPI, vol. 12(4), pages 1-20, February.
- Guijo-Rubio, D. & Durán-Rosal, A.M. & Gutiérrez, P.A. & Gómez-Orellana, A.M. & Casanova-Mateo, C. & Sanz-Justo, J. & Salcedo-Sanz, S. & Hervás-Martínez, C., 2020. "Evolutionary artificial neural networks for accurate solar radiation prediction," Energy, Elsevier, vol. 210(C).
- Fateme Dinmohammadi & Yuxuan Han & Mahmood Shafiee, 2023. "Predicting Energy Consumption in Residential Buildings Using Advanced Machine Learning Algorithms," Energies, MDPI, vol. 16(9), pages 1-23, April.
- Md Mijanur Rahman & Mohammad Shakeri & Sieh Kiong Tiong & Fatema Khatun & Nowshad Amin & Jagadeesh Pasupuleti & Mohammad Kamrul Hasan, 2021. "Prospective Methodologies in Hybrid Renewable Energy Systems for Energy Prediction Using Artificial Neural Networks," Sustainability, MDPI, vol. 13(4), pages 1-28, February.
- Miriam Benedetti & Francesca Bonfà & Vito Introna & Annalisa Santolamazza & Stefano Ubertini, 2019. "Real Time Energy Performance Control for Industrial Compressed Air Systems: Methodology and Applications," Energies, MDPI, vol. 12(20), pages 1-28, October.
- Jebli, Imane & Belouadha, Fatima-Zahra & Kabbaj, Mohammed Issam & Tilioua, Amine, 2021. "Prediction of solar energy guided by pearson correlation using machine learning," Energy, Elsevier, vol. 224(C).
- Yuting Cui & Raphael Lissillour & Juraj Chebeň & Drahoslav Lančarič & Chunlin Duan, 2022. "The position of financial prudence, social influence, and environmental satisfaction in the sustainable consumption behavioural model: Cross‐market intergenerational investigation during the Covid‐19 ," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 29(4), pages 996-1020, July.
- Bei Wang & Alina M. Udall, 2023. "Sustainable Consumer Behaviors: The Effects of Identity, Environment Value and Marketing Promotion," Sustainability, MDPI, vol. 15(2), pages 1-14, January.
- Aslam, Sheraz & Herodotou, Herodotos & Mohsin, Syed Muhammad & Javaid, Nadeem & Ashraf, Nouman & Aslam, Shahzad, 2021. "A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
- Chen, Xiaoyi & Dong, Zhenbiao & Zhu, Liujuan & Ling, Xiang, 2023. "Mass transfer performance inside Ca-based thermochemical energy storage materials under different operating conditions," Renewable Energy, Elsevier, vol. 205(C), pages 340-348.
- Julia Wojciechowska-Solis & Anetta Barska, 2021. "Exploring the Preferences of Consumers’ Organic Products in Aspects of Sustainable Consumption: The Case of the Polish Consumer," Agriculture, MDPI, vol. 11(2), pages 1-17, February.
- Molinillo, Sebastian & Vidal-Branco, Murilo & Japutra, Arnold, 2020. "Understanding the drivers of organic foods purchasing of millennials: Evidence from Brazil and Spain," Journal of Retailing and Consumer Services, Elsevier, vol. 52(C).
- Aistė Čapienė & Aušra Rūtelionė & Manuela Tvaronavičienė, 2021. "Pro-Environmental and Pro-Social Engagement in Sustainable Consumption: Exploratory Study," Sustainability, MDPI, vol. 13(4), pages 1-20, February.
- Zhang, Guiqing & Tian, Chenlu & Li, Chengdong & Zhang, Jun Jason & Zuo, Wangda, 2020. "Accurate forecasting of building energy consumption via a novel ensembled deep learning method considering the cyclic feature," Energy, Elsevier, vol. 201(C).
- Amir Mosavi & Mohsen Salimi & Sina Faizollahzadeh Ardabili & Timon Rabczuk & Shahaboddin Shamshirband & Annamaria R. Varkonyi-Koczy, 2019. "State of the Art of Machine Learning Models in Energy Systems, a Systematic Review," Energies, MDPI, vol. 12(7), pages 1-42, April.
- Yue Wu & Katalin Takács-György, 2022. "Comparison of Consuming Habits on Organic Food—Is It the Same? Hungary Versus China," Sustainability, MDPI, vol. 14(13), pages 1-19, June.
More about this item
Keywords
ML; KJ; AHP; sustainable; consumer preferences;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:5:p:3983-:d:1076789. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.