Institutional Supply, Digital Governance and Homestead Exit in Megacity
——An empirical study based on Shanghai
- DOI
- 10.2991/978-94-6463-102-9_96How to use a DOI?
- Keywords
- urban planning; homestead exit; self-determination theory; smart governance; information technology
- Abstract
[Research methods] The binary logistic regression model was used for quantitative research, and the self-determination theory was introduced to analyze the influencing factors of the willingness of farmers to quit their homesteads in the megacity. [Research results] (1) Self-support has become a key factor influencing the willingness to withdraw from homesteads in the megacity. (2) Competency support factors are secondary influencing factors. The age of farmers, the per capita income of households, and the proportion of agricultural income all have a negative effect on the willingness to quit homesteads in the megacity. (3) To a lesser extent, the attribution support factor negatively affects the willingness of farmers to quit their homesteads. The modern information technology should be further used to strengthen the intelligent governance and digital governance of homesteads, and strengthen farmers' trust in government decision-making, so as to promote the process of homestead withdrawal.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Min Zhu AU - Yuhui Dong PY - 2022 DA - 2022/12/29 TI - Institutional Supply, Digital Governance and Homestead Exit in Megacity BT - Proceedings of the 2022 2nd International Conference on Business Administration and Data Science (BADS 2022) PB - Atlantis Press SP - 934 EP - 941 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-102-9_96 DO - 10.2991/978-94-6463-102-9_96 ID - Zhu2022 ER -