Publication
Abstract
This paper analyzes a non-experimental declarative framework for interpreting changes in the U.S. automotive market, including chip shortages, accelerated car adoption, and the continued dominance of SUVs and trucks. It proposes an in-depth analysis of a four-layered demand hierarchy, focusing on the following layers: basic needs, functional/performance needs, experience/service needs, and identity/value needs. Furthermore, through a complementary emotional-functional ontology, it covers factors related to safety/ADAS, powertrain, and charging, and infotainment/human-machine interaction. It outlines measurement blueprints (co-occurrence enhancement, conditional share, journey slicing) and management tools (importance matrix, demand hierarchy scorecard), and verifies that electric vehicle anxiety is more strongly influenced by charging reliability than by rated range; trust depends on service transparency and OTA stability. Finally, it prioritizes related services and user experience, while establishing a clear path for future empirical verification.
Keywords
Customer Needs Hierarchy , Sentiment–Function Mapping , U.S. Automotive Market , Voice of Customer (VoC) .
Metadata
Disciplines: Information Science
Subjects: Information Retrieval
Cite This Article
APA Style
Zhou, Z. (2026). Hierarchical needs in u.s. automotive customer feedback and the sentiment–function nexus. Journal of Industrial Engineering and Applied Science, 4(1), 27-33. https://doi.org/10.70393/6a69656173.333637
Acknowledgments
The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.
FUNDING
INSTITUTIONAL REVIEW BOARD STATEMENT
DATA AVAILABILITY STATEMENT
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
INFORMED CONSENT STATEMENT
CONFLICT OF INTEREST
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
AUTHOR CONTRIBUTIONS
References
1.
Biney, J., Jones, E. C. J., & Jones, E. C. (2024). Understanding the EV Semiconductor Chip Sustainable Supply Chain Chip Shortage. International Supply Chain Technology Journal, 10(2).
2.
Fedrigo, E. (2024). The growing role of In-System Programming activity in the electric vehicle industry and its key players in the Chinese semiconductor market, with English-Chinese terminographic repertoire.
3.
Khaleel, M., Nassar, Y., El-Khozondar, H. J., Elmnifi, M., Rajab, Z., Yaghoubi, E., & Yaghoubi, E. (2024). Electric vehicles in China, Europe, and the United States: Current trend and market comparison. Int. J. Electr. Eng. and sustain., 1-20.
4.
Kenrick, D. T., Griskevicius, V., Neuberg, S. L., & Schaller, M. (2010).Renovating the pyramid of needs: Contemporary extensions built upon ancient foundations. Perspectives on Psychological Science, 5(3), 292–314.
5.
Archak, N., Ghose, A., & Ipeirotis, P. G. (2011).Deriving the pricing power of product features by mining consumer reviews. Management Science, 57(8), 1485–1509.
6.
Yang, J., Wu, Y., Yuan, Y., Xue, H., Bourouis, S., Abdel-Salam, M., ... & Por, L. Y. (2025). Llm-ae-mp: Web attack detection using a large language model with autoencoder and multilayer perceptron. Expert Systems with Applications, 274, 126982.
7.
Kožnjak, B. (2017). Kuhn meets Maslow: The psychology behind scientific revolutions. Journal for General Philosophy of Science, 48(2), 257-287.
8.
Guy, E. S. (2020). Case Study of the Reinforced Instruction for Student Excellence (RISE) Teaching Faculty’s Understanding of Theoretical and Practical Applications of Maslow’s Theory of Human Motivation in a Corequisite Developmental Classroom. North Carolina State University.
9.
Yang, W., Zhang, B., & Wang, J. (2025, May). Research on AI economic cycle prediction method based on big data. In Proceedings of the 2025 International Conference on Digital Economy and Intelligent Computing (pp. 13-17).
10.
Su, Z., Yang, D., Wang, C., Xiao, Z., & Cai, S. (2025). Structural assessment of family and educational influences on student health behaviours: Insights from a public health perspective. Plos one, 20(9), e0333086.
11.
Yang, Z., Sun, A., Zhao, Y., Yang, Y., Li, D., & Zhou, C. (2025, August). Rlhf fine-tuning of llms for alignment with implicit user feedback in conversational recommenders. In 2025 4th International Conference on Artificial Intelligence, Internet of Things and Cloud Computing Technology (AIoTC) (pp. 587-591). IEEE.
12.
Han, X., & Dou, X. (2025). User recommendation method integrating hierarchical graph attention network with multimodal knowledge graph. Frontiers in Neurorobotics, 19, 1587973.
13.
Xu, I. (2025). Computer vision-enabled inventory management system: A cloud-native solution for retail cost reduction.
14.
Hopkins, M., & Lazonick, W. (2024). Tesla as a Global Competitor: Strategic Control in the EV Transition. Institute for New Economic Thinking Working Paper Series, (225).
15.
Huitt, W. (2007). Maslow's hierarchy of needs. Educational psychology interactive, 23.
16.
Poston, B. (2009). Maslow’s hierarchy of needs. The surgical technologist, 41(8), 347-353.
17.
Bennett, M., & Shangreaux, C. (2005). Appyling Maslow's Hierarchy Theory. First Peoples Child & Family Review, 2(1), 89-116.
18.
Kolar, E., & Lindström, L. (2018). Future business model for OEMs in the automotive industry business model adaptation based on the role an OEM takes in a future business network.
19.
Brunson, K., & Barnes, S. (2020). Automotive Dealership Management Fundamentals.
20.
Zhang, L., & Meng, Q. (2025). User Portrait-Driven Smart Home Device Deployment Optimization and Spatial Interaction Design.
21.
Tan, C., Gao, F., Song, C., Xu, M., Li, Y., & Ma, H. (2024). Proposed Damage Detection and Isolation from Limited Experimental Data Based on a Deep Transfer Learning and an Ensemble Learning Classifier.
22.
Zhao, H., Chen, Y., Dang, B., & Jian, X. (2024, December). Research on Steel Production Scheduling Optimization Based on Deep Learning. In 2024 4th International Symposium on Artificial Intelligence and Intelligent Manufacturing (AIIM) (pp. 813-816). IEEE.
23.
Yang, W., Lin, Y., Xue, H., & Wang, J. (2025, April). Research on stock market sentiment analysis and prediction method based on convolutional neural network. In Proceedings of the 2025 International Conference on Machine Learning and Neural Networks (pp. 91-96).
24.
Yuan, Y., & Xue, H. (2025, January). Multimodal Information Integration and Retrieval Framework Based on Graph Neural Networks. In Proceedings of the 2025 4th International Conference on Big Data, Information and Computer Network(pp. 135-139).
25.
Li, Z., Ji, Q., Ling, X., & Others. (2025). A comprehensive review of multi-agent reinforcement learning in video games. Authorea Preprints.
26.
Mittal, V., & Kamakura, W. A. (2001).Satisfaction, repurchase intent, and repurchase behavior: Investigating the moderating effect of customer characteristics. Journal of Marketing Research, 38(1), 131–142.
27.
Chitturi, R., Raghunathan, R., & Mahajan, V. (2008).Delight by design: The role of hedonic versus utilitarian benefits. Journal of Marketing, 72(3), 48–63.
28.
Zhang, Z., Wang, J., Li, Z., Wang, Y., & Zheng, J. (2025). AnnCoder: A multi-agent-based code generation and optimization model. Symmetry, 17(7), 1087.
29.
Lu, J., Zhao, H., Zhai, H., Yang, X., & Han, S. (2025). DeepSPG: Exploring deep semantic prior guidance for low-light image enhancement with multimodal learning. In Proceedings of the 2025 International Conference on Multimedia Retrieval (pp. 935–943).
30.
Deng, X. (2025). Cooperative optimization strategies for data collection and machine learning in large-scale distributed systems. In Proceedings of the 2025 4th International Symposium on Computer Applications and Information Technology (ISCAIT) (pp. 2151–2154). IEEE.
31.
Yang, H., Lin, X., Chen, Z., & Others. (2025). Research on model parallelism and data parallelism optimization methods in large language model-based recommendation systems. arXiv preprint arXiv:2506.17551.
PUBLISHER'S NOTE
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Copyright © 2025 The Author(s). Published by Southern United Academy of Sciences.
This work is licensed under a Creative
Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
