Exploring Factors Influencing Farmers’ Continuance Intention to Crop Residue Retention: Evidence from Rural China
Abstract
:1. Introduction
2. Theoretical Framework and Hypotheses
2.1. Theoretical Framework
2.2. Development of Hypothesis
2.2.1. Satisfaction
2.2.2. Perceived Usefulness
2.2.3. Perceived Ease of Use
2.2.4. Confirmation
3. Data and Statistical Analysis
3.1. Study Area
3.2. Data
3.3. Measurement of Key Variables
3.4. Statistical Analysis
4. Results
4.1. Farmers’ Demographic Profile
4.2. Reliability and Validity Test
4.3. Hypothesis Test
4.4. The Results of Mediating Effect Analysis
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Guo, S. How Does Straw Burning Affect Urban Air Quality in China? Am. J. Agric. Econ. 2021, 103, 1122–1140. [Google Scholar] [CrossRef]
- Chen, J.; Gong, Y.; Wang, S.; Guan, B.; Balkovic, J.; Kraxner, F. To burn or retain crop residues on croplands? An integrated analysis of crop residue management in China. Sci. Total Environ. 2019, 662, 141–150. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Li, J.; Bo, Y.; Xie, S. Estimating emissions from crop residue open burning in China based on statistics and MODIS fire products. J. Environ. Sci. 2016, 44, 158–170. [Google Scholar] [CrossRef] [PubMed]
- Zhang, Y.; Shao, M.; Lin, Y.; Luan, S.; Mao, N.; Chen, W.; Wang, M. Emission inventory of carbonaceous pollutants from biomass burning in the Pearl River Delta Region, China. Atmos. Environ. 2013, 76, 189–199. [Google Scholar] [CrossRef]
- Zhang, L.; Liu, Y.; Hao, L. Contributions of open crop straw burning emissions to PM 2.5 concentrations in China. Environ. Res. Lett. 2016, 11, 014014. [Google Scholar] [CrossRef]
- Xia, L.; Wang, S.; Yan, X. Effects of long-term straw incorporation on the net global warming potential and the net economic benefit in a rice-wheat cropping system in China. Agric. Ecosyst. Environ. 2014, 197, 118–127. [Google Scholar] [CrossRef]
- Berhane, M.; Xu, M.; Liang, Z.; Shi, J.; Wei, G.; Tian, X. Effects of long-term straw return on soil organic carbon storage and sequestration rate in North China upland crops: A meta-analysis. Glob. Chang. Biol. 2020, 26, 2686–2701. [Google Scholar] [CrossRef]
- Cheng, K.; Pan, G.; Smith, P.; Luo, T.; Li, L.; Zheng, J.; Zhang, X.; Han, X.; Yan, M. Carbon footprint of China’s crop production—An estimation using agro-statistics data over 1993–2007. Agric. Ecosyst. Environ. 2011, 142, 231–237. [Google Scholar] [CrossRef]
- Sun, D.; Ge, Y.; Zhou, Y. Punishing and rewarding: How do policy measures affect crop straw use by farmers? An empirical analysis of Jiangsu Province of China. Energy Policy 2019, 134, 110882. [Google Scholar] [CrossRef]
- Cai, J.M.; Liu, R.H.; Deng, C.J.; Shen, F. Amount, availability and potential uses for energy of agricultural residues in Mainland China. J. Energy Inst. 2007, 80, 243–246. [Google Scholar] [CrossRef]
- Mann, L.; Tolbert, V.; Cushman, J. Potential environmental effects of corn (Zea mays L.) stover removal with emphasis on soil organic matter and erosion. Agric. Ecosyst. Environ. 2002, 89, 149–166. [Google Scholar] [CrossRef]
- Lou, Y.; Xu, M.; Wang, W.; Sun, X.; Zhao, K. Return rate of straw residue affects soil organic C sequestration by chemical fertilization. Soil Tillage Res. 2011, 113, 70–73. [Google Scholar] [CrossRef]
- Wang, X.; Wu, H.; Dai, K.; Zhang, D.; Feng, Z.; Zhao, Q.; Wu, X.; Jin, K.; Cai, D.; Oenema, O.; et al. Tillage and crop residue effects on rainfed wheat and maize production in northern China. Field Crop. Res. 2012, 132, 106–116. [Google Scholar] [CrossRef]
- Pratt, M.R.; Tyner, W.E.; Muth, D.J.; Kladivko, E.J. Synergies between cover crops and corn stover removal. Agric. Syst. 2014, 130, 67–76. [Google Scholar] [CrossRef]
- Tasca, A.L.; Nessi, S.; Rigamonti, L. Environmental sustainability of agri-food supply chains: An LCA comparison between two alternative forms of production and distribution of endive in northern Italy. J. Clean. Prod. 2017, 140, 725–741. [Google Scholar] [CrossRef]
- Li, M.; Zhang, W.; He, Y.-j.; Wang, G.-l. Research on the effect of straw mulching on the soil moisture by field experiment in the piedmont plain of the Taihang Mountains. J. Groundw. Sci. Eng. 2017, 5, 286–295. [Google Scholar]
- Wang, S.-C.; Zhao, Y.-W.; Wang, J.-Z.; Zhu, P.; Cui, X.; Han, X.-Z.; Xu, M.-G.; Lu, C.-A. The efficiency of long-term straw return to sequester organic carbon in Northeast China’s cropland. J. Integr. Agric. 2018, 17, 436–448. [Google Scholar] [CrossRef]
- Hubei Provincial People’s Congress. Decision on Banning Burning and Comprehensive Utilization of Crop Straw in Open Air. 2016. Available online: http://www.hppc.gov.cn/p/9221.html (accessed on 10 April 2021).
- Gai, H.; Yan, T.; Zhang, J. Perceived value, government regulations and farmers’ behaviors of continued mechanized operation of straw returning to the field: An analysis based on survey data from 1288 farmers in three provinces of Hebei, Anhui, and Hubei. Chin. Rural Econ. 2020, 428, 108–125. (In Chinese) [Google Scholar]
- Mohan, D.; Singh, K.P. Single- and multi-component adsorption of cadmium and zinc using activated carbon derived from bagasse—an agricultural waste. Water Res. 2002, 36, 2304–2318. [Google Scholar] [CrossRef]
- Navia, R.E.; Crowley, D. Closing the loop on organic waste management: Biochar for agricultural land application and climate change mitigation. Waste Manag. Res. 2010, 28, 479–480. [Google Scholar] [CrossRef] [Green Version]
- He, K.; Zhang, J.; Feng, J.; Hu, T.; Zhang, L. The Impact of Social Capital on farmers’ Willingness to Reuse Agricultural Waste for Sustainable Development. Sustain. Dev. 2015, 24, 101–108. [Google Scholar] [CrossRef]
- Jiang, L.; Zhang, J.; Wang, H.H.; Zhang, L.; He, K. The impact of psychological factors on farmers’ intentions to reuse agricultural biomass waste for carbon emission abatement. J. Clean. Prod. 2018, 189, 797–804. [Google Scholar] [CrossRef]
- Zeng, Y.; Tian, Y.; He, K.; Zhang, J. Environmental conscience, external incentives and social norms in rice farmers’ adoption of pro-environmental agricultural practices in rural Hubei province, China. Environ. Technol. 2019, 41, 2518–2532. [Google Scholar] [CrossRef] [PubMed]
- Raza, M.H.; Abid, M.; Yan, T.; Naqvi, S.A.A.; Akhtar, S.; Faisal, M. Understanding farmers’ intentions to adopt sustainable crop residue management practices: A structural equation modeling approach. J. Clean. Prod. 2019, 227, 613–623. [Google Scholar] [CrossRef]
- Lopes, A.A.; Viriyavipart, A.; Tasneem, D. The role of social influence in crop residue management: Evidence from Northern India. Ecol. Econ. 2020, 169, 106563. [Google Scholar] [CrossRef]
- Cao, H.; Zhu, X.; Heijman, W.; Zhao, K. The impact of land transfer and farmers’ knowledge of farmland protection policy on pro-environmental agricultural practices: The case of straw return to fields in Ningxia, China. J. Clean. Prod. 2020, 277, 123701. [Google Scholar] [CrossRef]
- Gao, L.; Zhang, W.; Mei, Y.; Sam, A.G.; Song, Y.; Jin, S. Do farmers adopt fewer conservation practices on rented land? Evidence from straw retention in China. Land Use Policy 2018, 79, 609–621. [Google Scholar] [CrossRef] [Green Version]
- Jiang, W.; Yan, T.; Chen, B. Impact of media channels and social interactions on the adoption of straw return by Chinese farmers. Sci. Total. Environ. 2021, 756, 144078. [Google Scholar] [CrossRef]
- Hou, L.; Chen, X.; Kuhn, L.; Huang, J. The effectiveness of regulations and technologies on sustainable use of crop residue in Northeast China. Energy Econ. 2019, 81, 519–527. [Google Scholar] [CrossRef]
- Knowler, D.; Bradshaw, B. Farmers’ adoption of conservation agriculture: A review and synthesis of recent research. Food Policy 2007, 32, 25–48. [Google Scholar] [CrossRef]
- Hansson, H.; Ferguson, R.; Olofsson, C. Psychological Constructs Underlying Farmers’ Decisions to Diversify or Specialise their Businesses—An Application of Theory of Planned Behaviour. J. Agric. Econ. 2012, 63, 465–482. [Google Scholar] [CrossRef]
- Venkatesh, V.; Morris, M.G.; Ackerman, P.L. A Longitudinal Field Investigation of Gender Differences in Individual Technology Adoption Decision-Making Processes. Organ. Behav. Hum. Decis. Process. 2000, 83, 33–60. [Google Scholar] [CrossRef] [Green Version]
- Bhattacherjee, A. An empirical analysis of the antecedents of electronic commerce service continuance. Decis. Support Syst. 2001, 32, 201–214. [Google Scholar] [CrossRef]
- Bhattacherjee, A. Understanding Information Systems Continuance: An Expectation-Confirmation Model. MIS Q. 2001, 25, 351–370. [Google Scholar] [CrossRef]
- Ifinedo, P. Acceptance and Continuance Intention of Web-based Learning Technologies (WLT) Use among University Students in a Baltic Country. Electron. J. Inf. Syst. Dev. Ctries. 2006, 23, 1–20. [Google Scholar] [CrossRef]
- Thong, J.Y.; Hong, S.-J.; Tam, K.Y. The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. Int. J. Hum. Comput. Stud. 2006, 64, 799–810. [Google Scholar] [CrossRef]
- Hung, M.C.; Hwang, H.G.; Hsieh, T.-C. An exploratory study on the continuance of mobile commerce: An extended expectation-confirmation model of information system use. Int. J. Mob. Commun. 2007, 5, 409–422. [Google Scholar] [CrossRef]
- Yuan, S.; Liu, Y.; Yao, R.; Liu, J. An investigation of users’ continuance intention towards mobile banking in China. Inf. Dev. 2016, 32, 20–34. [Google Scholar] [CrossRef]
- Chong, A.Y.-L. Understanding Mobile Commerce Continuance Intentions: An Empirical Analysis of Chinese Consumers. J. Comput. Inf. Syst. 2013, 53, 22–30. [Google Scholar] [CrossRef]
- Hong, W.; Chan, F.K.Y.; Thong, J.Y.; Chasalow, L.C.; Dhillon, G. A Framework and Guidelines for Context-Specific Theorizing in Information Systems Research. Inf. Syst. Res. 2014, 25, 111–136. [Google Scholar] [CrossRef]
- Lin, C.S.; Wu, S.; Tsai, R.J. Integrating perceived playfulness into expectation-confirmation model for web portal context. Inf. Manag. 2005, 42, 683–693. [Google Scholar] [CrossRef]
- Locke, E.A. The Nature and Causes of Job Satisfaction. In Handbook of Industrial and Organizational Psychology; Rand McNally: Chicago, IL, USA, 1976; pp. 1297–1343. [Google Scholar]
- Zhao, Y.; Bacao, F. What factors determining customer continuingly using food delivery apps during 2019 novel coronavirus pandemic period? Int. J. Hosp. Manag. 2020, 91, 102683. [Google Scholar] [CrossRef] [PubMed]
- Wang, X.; Goh, D.H.-L.; Lim, E.-P. Understanding Continuance Intention toward Crowdsourcing Games: A Longitudinal Investigation. Int. J. Hum. Comput. Interact. 2020, 36, 1168–1177. [Google Scholar] [CrossRef]
- Cheng, S.; Liu, L.; Li, K. Explaining the Factors Influencing the Individuals’ Continuance Intention to Seek Information on Weibo during Rainstorm Disasters. Int. J. Environ. Res. Public Health 2020, 17, 6072. [Google Scholar] [CrossRef]
- Stiglitz, J. Growth with Exhaustible Natural Resources: Efficient and Optimal Growth Paths. Rev. Econ. Stud. 1974, 41, 123. [Google Scholar] [CrossRef] [Green Version]
- Davis, F.D.; Bagozzi, R.P.; Warshaw, P.R. User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Manag. Sci. 1989, 35, 982–1003. [Google Scholar] [CrossRef] [Green Version]
- Hung, M.-C.; Shih-Ting, Y.; Hsieh, T.-C. An examination of the determinants of mobile shopping continuance. Int. J. Electron. Bus. Manag. 2012, 10, 29–37. [Google Scholar]
- Premkumar, G.; Bhattacherjee, A. Explaining information technology usage: A test of competing models. Omega 2008, 36, 64–75. [Google Scholar] [CrossRef]
- Oliver, R.L. A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions. J. Mark. Res. 1980, 17, 460–469. [Google Scholar] [CrossRef]
- Tse, D.K.; Wilton, P.C. Models of Consumer Satisfaction Formation: An Extension. J. Mark. Res. 1988, 25, 204–212. [Google Scholar] [CrossRef]
- Kim, S.S.; Malhotra, N.K. A Longitudinal Model of Continued IS Use: An Integrative View of Four Mechanisms Underlying Postadoption Phenomena. Manag. Sci. 2005, 51, 741–755. [Google Scholar] [CrossRef]
- Compeau, D.R.; Higgins, C.A. Computer Self-Efficacy: Development of a Measure and Initial Test. MIS Q. 1995, 19, 189–211. [Google Scholar] [CrossRef] [Green Version]
- Venkatesh, V.; Thong, J.Y.L.; Chan, F.K.Y.; Hu, P.J.-H.; Brown, S.A. Extending the two-stage information systems continuance model: Incorporating UTAUT predictors and the role of context. Inf. Syst. J. 2011, 21, 527–555. [Google Scholar] [CrossRef]
- Hsu, C.-L.; Lu, H.-P.; Hsu, H.-H. Adoption of the mobile Internet: An empirical study of multimedia message service (MMS). Omega 2007, 35, 715–726. [Google Scholar] [CrossRef]
- Kim, H.-W.; Chan, H.C.; Gupta, S. Value-based Adoption of Mobile Internet: An empirical investigation. Decis. Support Syst. 2007, 43, 111–126. [Google Scholar] [CrossRef]
- Bagheri, A.; Bondori, A.; Allahyari, M.S.; Damalas, C.A. Modeling farmers’ intention to use pesticides: An expanded version of the theory of planned behavior. J. Environ. Manag. 2019, 248, 109291. [Google Scholar] [CrossRef]
- Hair, J.F.; Black, W.C.; Babin, B.J.; Anderson, R.E.; Tatham, R.L. Multivariate Data Analysis; Prentice Hall: Hoboken, NJ, USA, 1998; Volume 5. [Google Scholar]
- Hair, J.F., Jr.; Sarstedt, M.; Hopkins, L.; Kuppelwieser, V. Partial least squares structural equation modeling (PLS-SEM). Eur. Bus. Rev. 2014, 26, 106–121. [Google Scholar] [CrossRef]
- Taylor, A.B.; MacKinnon, D.P.; Tein, J.-Y. Tests of the Three-Path Mediated Effect. Organ. Res. Methods 2007, 11, 241–269. [Google Scholar] [CrossRef]
- Hayes, A.F.; Scharkow, M. The Relative Trustworthiness of Inferential Tests of the Indirect Effect in Statistical Mediation Analysis. Psychol. Sci. 2013, 24, 1918–1927. [Google Scholar] [CrossRef]
- Zhang, Q.; Bilsborrow, R.E.; Song, C.; Tao, S.; Huang, Q. Rural household income distribution and inequality in China: Effects of payments for ecosystem services policies and other factors. Ecol. Econ. 2019, 160, 114–127. [Google Scholar] [CrossRef]
- Zhou, X.; Ma, W.; Renwick, A.; Li, G. Off-farm work decisions of farm couples and land transfer choices in rural China. Appl. Econ. 2020, 52, 6229–6247. [Google Scholar] [CrossRef]
- Ray, A.; Dhir, A.; Bala, P.K.; Kaur, P. Why do people use food delivery apps (FDA)? A uses and gratification theory perspective. J. Retail. Consum. Serv. 2019, 51, 221–230. [Google Scholar] [CrossRef]
- Xu, X.; Wang, S.; Yu, Y. Consumer’s intention to purchase green furniture: Do health consciousness and environmental awareness matter? Sci. Total Environ. 2020, 704, 135275. [Google Scholar] [CrossRef] [PubMed]
Latent Variables | Item | Statement | Mean | Standard Deviation |
---|---|---|---|---|
Perceived usefulness | Ua1 | Overall, residue retention is conducive to the control and amelioration of air pollution. | 4.177 | 0.850 |
Ua2 | Residue retention is beneficial to increase the farmland’s yield of the family. | 3.699 | 0.856 | |
Ua3 | Residue retention is conducive to promoting the development and progress of society. | 4.044 | 0.751 | |
Ua4 | Residue retention is beneficial to improve the living environment. | 4.146 | 0.734 | |
Perceived ease of use | Ea1 | The skilled use of residue retention is easy for me. | 3.435 | 1.147 |
Ea2 | Now I fully remember the operation process of residue retention. | 3.714 | 1.073 | |
Ea3 | Accomplishing a specific goal by residue retention is easy for me. | 3.812 | 1.102 | |
Ea4 | Residue retention is effortless for me. | 3.275 | 1.154 | |
Confirmation | Ca1 | After using residue retention, I found that the productive input was better than expected. | 3.576 | 0.743 |
Ca2 | The improvement of cultivated land with residue retention is more considerable than expected. | 3.120 | 0.980 | |
Ca3 | After residue retention, agricultural income is better than expected. | 3.727 | 0.759 | |
Ca4 | My expectations for residue retention have been realized. | 3.648 | 0.759 | |
Satisfaction | Sa1 | The service of residue retention satisfied me. | 3.470 | 0.913 |
Sa2 | The work of the residue retention provider satisfied me. | 3.255 | 1.061 | |
Sa3 | Generally speaking, I am satisfied with the service level and the effect of residue retention. | 3.363 | 1.028 | |
Continuance intention | Ya1 | I intend to continue to adopt residue retention. | 3.690 | 1.268 |
Ya2 | I intend to continue to adopt residue retention instead of related alternative technologies and services. | 3.961 | 1.125 | |
Ya3 | I will continue to residue retention for the rest of my life. | 4.277 | 0.910 |
Index | Item | Sample Size | Proportion (%) | Index | Item | Sample Size | Proportion (%) |
---|---|---|---|---|---|---|---|
Gender | Male | 474 | 87.45 | Farming experience | ≤10 year | 41 | 7.56 |
Female | 68 | 12.55 | 11~20 year | 69 | 12.73 | ||
Age | ≤30 | 8 | 1.48 | 21~30 year | 145 | 26.75 | |
31~40 | 35 | 6.46 | ≥31 year | 287 | 52.95 | ||
41~50 | 153 | 28.23 | The proportion of annual household, agricultural income | ≤25% | 210 | 38.74 | |
51~60 | 188 | 34.69 | 25%~50% | 130 | 23.99 | ||
≥61 | 158 | 29.15 | 50%~75% | 71 | 13.10 | ||
Educational level | Elementary/below | 124 | 22.88 | ≥75% | 131 | 24.17 | |
Junior high | 299 | 55.17 | Planting scale | ≤0.33 ha | 166 | 30.63 | |
High/ vocational | 104 | 19.19 | 0.33~0.67 ha | 135 | 24.91 | ||
College/above | 15 | 2.77 | 0.67~1.00 ha | 97 | 17.90 | ||
Part-time job | Yes | 183 | 33.76 | 1.00~1.33 ha | 77 | 14.21 | |
No | 359 | 66.24 | ≥1.33 ha | 67 | 12.36 |
Construct | Item | Factor Loadings | Cronbach’s α | KMO | CR | AVE |
---|---|---|---|---|---|---|
Perceived usefulness (PU) | Ua1 | 0.620 | 0.802 | 0.774 | 0.819 | 0.534 |
Ua2 | 0.655 | |||||
Ua3 | 0.804 | |||||
Ua4 | 0.822 | |||||
Perceived ease of use (PEOU) | Ea1 | 0.798 | 0.815 | 0.771 | 0.817 | 0.529 |
Ea2 | 0.713 | |||||
Ea3 | 0.675 | |||||
Ea4 | 0.718 | |||||
Confirmation (CON) | Ca1 | 0.698 | 0.805 | 0.773 | 0.827 | 0.552 |
Ca2 | 0.555 | |||||
Ca3 | 0.795 | |||||
Ca4 | 0.883 | |||||
Satisfaction (SAT) | Sa1 | 0.572 | 0.724 | 0.653 | 0.730 | 0.478 |
Sa2 | 0.759 | |||||
Sa3 | 0.728 | |||||
Continuance intention (CI) | Ya1 | 0.678 | 0.666 | 0.652 | 0.670 | 0.405 |
Ya2 | 0.667 | |||||
Ya3 | 0.558 |
Inspection Index | Recommend Value | Index Result | Test Results | |
---|---|---|---|---|
Absolute fitness index | CMIN/DF | [2,5] | 2.379 | Fit |
RMSEA | <0.100 | 0.050 | Fit | |
GFI | >0.800 | 0.942 | Fit | |
AGFI | >0.800 | 0.922 | Fit | |
Value-added fitness index | NFI | >0.800 | 0.914 | Fit |
RFI | >0.800 | 0.897 | Fit | |
IFI | >0.800 | 0.948 | Fit | |
TLI | >0.800 | 0.937 | Fit | |
CFI | >0.800 | 0.948 | Fit | |
Parsimonious fitness index | PGFI | >0.500 | 0.700 | Fit |
PNFI | >0.500 | 0.759 | Fit | |
PCFI | >0.500 | 0.787 | Fit |
Hypothesis | Path | Coef. | S.E. | C.R. | p | Std. Coef. |
---|---|---|---|---|---|---|
H1 | SAT→CI | 0.210 | 0.067 | 3.122 | 0.002 ** | 0.216 |
H2 | PU→CI | 0.119 | 0.055 | 2.166 | 0.030 * | 0.141 |
H3 | PU→SAT | 0.402 | 0.062 | 6.518 | *** | 0.464 |
H4 | PEOU→CI | 0.344 | 0.046 | 7.403 | *** | 0.505 |
H5 | PEOU→SAT | 0.033 | 0.036 | 0.911 | 0.362 | 0.047 |
H6 | CON→PU | 0.552 | 0.061 | 9.111 | *** | 0.475 |
H7 | CON→PEOU | 0.292 | 0.074 | 3.925 | *** | 0.204 |
H8 | CON→SAT | 0.074 | 0.060 | 1.234 | 0.217 | 0.073 |
Path | Direct Effect | Bias-Corrected Confidence Intervals | Indirect Effect | Bias-Corrected Confidence Intervals | Total Effect | ||
---|---|---|---|---|---|---|---|
Lower Bounds | Upper Bounds | Lower Bounds | Upper Bounds | ||||
CON→PU | 0.475 ** | 0.436 | 0.681 | -- | -- | -- | 0.475 |
CON→PEOU | 0.204 ** | 0.160 | 0.442 | -- | -- | -- | 0.204 |
CON→SAT | 0.073 | -0.05 | 0.209 | 0.230 ** | 0.153 | 0.346 | 0.303 |
CON→CI | -- | -- | -- | 0.236 ** | 0.151 | 0.334 | 0.236 |
PU→SAT | 0.464 ** | 0.272 | 0.560 | -- | -- | -- | 0.464 |
PEOU→SAT | 0.047 | -0.035 | 0.108 | -- | -- | -- | 0.047 |
PU→CI | 0.141 ** | 0.005 | 0.259 | 0.100 ** | 0.034 | 0.160 | 0.242 |
PEOU→CI | 0.505 ** | 0.251 | 0.455 | 0.010 | -0.007 | 0.027 | 0.515 |
SAT→CI | 0.216 ** | 0.086 | 0.364 | -- | -- | -- | 0.216 |
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Gai, H.; Yan, T.; Zhang, A.; Batchelor, W.D.; Tian, Y. Exploring Factors Influencing Farmers’ Continuance Intention to Crop Residue Retention: Evidence from Rural China. Int. J. Environ. Res. Public Health 2021, 18, 7412. https://doi.org/10.3390/ijerph18147412
Gai H, Yan T, Zhang A, Batchelor WD, Tian Y. Exploring Factors Influencing Farmers’ Continuance Intention to Crop Residue Retention: Evidence from Rural China. International Journal of Environmental Research and Public Health. 2021; 18(14):7412. https://doi.org/10.3390/ijerph18147412
Chicago/Turabian StyleGai, Hao, Tingwu Yan, Anran Zhang, William David Batchelor, and Yun Tian. 2021. "Exploring Factors Influencing Farmers’ Continuance Intention to Crop Residue Retention: Evidence from Rural China" International Journal of Environmental Research and Public Health 18, no. 14: 7412. https://doi.org/10.3390/ijerph18147412