Assessing Influence Mechanism of Green Utilization of Agricultural Wastes in Five Provinces of China through Farmers’ Motivation-Cognition-Behavior
Abstract
:1. Introduction
2. Theoretical Analysis and Mechanism
2.1. The “Motivation-Cognition-Behavior” Theoretical Framework for the Green Utilization of Agricultural Waste
2.2. Analysis of the Broken Window Effect on the Extrinsic Motivation of Farmers’ Green Utilization Behavior
2.3. An Analysis Framework Based on the “Motivation-Cognition-Behavior” Model
- (1)
- Extrinsic motivation in the motivational dimension. Considering the double broken window effect, two extrinsic motivational factors are defined: positive broken window and negative broken window variables. The positive broken window effect is characterized by farmers’ perception of the external environment in which agricultural waste are used, specifically: the extrinsic motivation formed by farmers when other people obtain considerable utilization benefits; whether farmers’ incineration activities will dwindle under a good environmental governance and supervision system; and when actively participating in green utilization, whether farmers will have an impact on the outside world, that is, the spread of positive broken window effects. Negative broken window effect is characterized by farmers’ perception of the external environment of agricultural waste incineration, specifically, to what degree will farmers be influenced by bad behaviors and spread their bad behaviors when the governance rules are broken, or under bad environmental governance.Therefore, the sub hypothesis is as follows:H1a.Positive broken window has a positive effect on subjective norms;H1b.Negative broken window has a negative effect on subjective norms.
- (2)
- Intrinsic motivation in the motivational dimension. The threat analysis in the protection motivation theory means that farmers consider comprehensively the adverse effects and benefits of the negative disposal of agricultural waste, which is essentially a quantitative manifestation of farmers’ intrinsic motivation [35]. As a result, the threat assessment of protection motivation and intrinsic motivation is the same. The measurements of threat assessment: threat assessment = (seriousness + susceptibility)-return [33], in which the seriousness means the consequences (causing damage) resulted from farmers’ negative disposal of agricultural waste, the susceptibility equals to the possibility of vicious consequences caused by negative disposal of agricultural waste, and the return is the benefit from the negative disposal of agricultural waste. Therefore, three intrinsic motivation factors are defined in the measurements of intrinsic motivation: seriousness, susceptibility, and return.Therefore, the sub hypothesis is as follows:H2a.Seriousness has a positive effect on behavior attitude;H2b.Susceptibility has a positive effect on behavior attitude;H2c.Return has a negative effect on behavior attitude.
- (3)
- Response analysis in the motivational dimension. According to the measurements of response analysis in the protection motivation theory: response analysis = (self-efficacy + response efficacy)-response cost [33]. Self-efficacy is farmers’ judgment on the conditions of green utilization of agricultural waste, and they can clarify their actual ability and participation status; the response efficiency is characterized by the benefits given to farmers by the green utilization of agricultural waste; the response cost is the farmers’ understanding of the cost of green utilization of agricultural waste. Therefore, three response motivational factors are defined in the measurements of response analysis: self-efficacy, response efficacy, and reflection cost.Therefore, the sub hypothesis:H3a.Self-efficacy has a positive effect on perceived behavior control;H3b.Response efficacy has a positive effect on perceived behavior control;H3c.Response cost has a negative effect on perceived behavior control.
- (4)
- Cognitive dimension. According to the theory of planned behavior, three cognitive sub-dimensional variables are defined in this dimension: subjective norms, behavioral attitudes, and perceived behavioral control. The subjective norms mean how deeply farmers are affected by the external environment when participating in decision-making. Behavioral attitude stands for the farmers’ own efforts to participate in activities. Moreover, perceived behavioral control denotes how fully farmers control their capabilities.Therefore, the sub hypothesis:H4a.Subjective norms have a positive effect on utilization intention;H4b.Behavior attitude has a positive effect on utilization intention;H4c.Perceived behavior control has a positive effect on utilization intention.
- (5)
- Behavioral dimension. According to the theory of planned behavior, two behavioral sub-dimensional variables are defined in this dimension: utilization intention and utilization behavior, which indicate farmers’ willingness to participate in straw utilization and their actual participation, respectively. See Figure 1 for the above analysis:
3. Data Source and Descriptive Analysis
3.1. Data Source
3.1.1. Overview of the Study Area with Respect to Agricultural Activities
3.1.2. Data Collection and Statistics on Respondents
3.2. Descriptive Analysis
4. Examination of Influencing Factors of Green Utilization of Agricultural Waste
4.1. Reliability and Validity Test
4.2. Result Analysis
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Nie, P.; Sousa-Poza, A.; Xue, J.H. Fuel for Life: Domestic Cooking Fuels and Women’s Health in Rural China. Inter. J. Environ. Res. Pub. Heal. 2016, 13, 810. [Google Scholar] [CrossRef] [PubMed]
- Marousek, J. Study on Agriculture Decision-Makers Behavior on Sustainable Energy Utilization. J. Agric. Environ. Ethic. 2013, 26, 679–689. [Google Scholar] [CrossRef]
- Stana, J.; Medek, A. Waste from agricultural production of energetic crops and its utilization. Krmiva 2010, 52, 233–235. [Google Scholar]
- Syamsu, A.; Karim, H. The Policy Strategy of Rice Straw Utilization of as Feed for Ruminants. AJARD 2013, 3, 615–621. [Google Scholar]
- Sparling, A.S.; Martin, D.W.; Posey, L.B. An Evaluation of the Proposed Worker Protection Standard with Respect to Pesticide Exposure and Parkinson’s disease. Inter. J. Env. Res. Pub. Heal. 2017, 14, 640. [Google Scholar] [CrossRef] [Green Version]
- Yadav, G.S.; Datta, R.; Pathan, S.I. Effects of Conservation Tillage and Nutrient Management Practices on Soil Fertility and Productivity of Rice (Oryza sativa L.)-Rice System in the North-Eastern Region of India. Sustainability 2017, 9, 1816. [Google Scholar] [CrossRef] [Green Version]
- Champagne, P. Bioethanol from agricultural waste residues. Environ. Prog. 2008, 27, 51–57. [Google Scholar] [CrossRef]
- Arthur, R.; Baidoo, M.F. Harnessing methane generated from livestock manure in Ghana, Nigeria, Mali, and Burkina Faso. Biomass Bioenerg. 2011, 35, 4648–4656. [Google Scholar] [CrossRef]
- Bhuvaneshwari, S.; Hiroshan, H.; Meegoda, J.N. Crop Residue Burning in India: Policy Challenges and Potential Solutions. Inter. J. Env. Res. Pub. Heal. 2019, 16, 832. [Google Scholar] [CrossRef] [Green Version]
- He, K.; Zhang, J.; Feng, J. The Impact of Social Capital on farmers’ Willingness to Reuse Agricultural Waste for Sustainable Development. Sustain. Dev. 2016, 24, 101–108. [Google Scholar] [CrossRef]
- Gao, L.H.; Li, Y. The rationalization analysis for regional environmental administration regulation under the perspective of proportion principle: Taking examples of the prohibition of burning crop straw. China Popul. Resour. Environ. 2017, 27, 79–87. [Google Scholar]
- Yan, T.W.; He, K.; Cui, M.M.; Zhang, J.B. Welfare Response Analysis of Farmers to Resource Utilization of Crop Straw: A Case Study of Hubei Province. J. Agro Tech. Econ. 2016, 4, 28–40. [Google Scholar]
- Ren, J.Q.; Yu, P.X.; Xu, X.H. Straw Utilization in China-Status and Recommendations. Sustainability 2019, 11, 1762. [Google Scholar] [CrossRef] [Green Version]
- Tong, H.Z.; Liu, W. Influence Mechanism of Policy Mix on Farmers’ Adoption Behavior of Protective Cultivation Technology. Soft Sci. 2018, 32, 18–23. [Google Scholar]
- Jiang, L.L.; Zhang, J.B.; 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]
- Zhang, T.Z.; Yan, T.W.; Zhang, J.B. How the Rural Cadre-farmer Relationship Affects the Farmers’Agricultural Waste Utilization: Based on the Survey Data of 1372 Farmers in Four Provinces. J. Nanjing Agric. Univ. (Soc. Sci. Ed.) 2020, 20, 150–160. [Google Scholar]
- Chen, J.; Cheng, M.; Zeng, Z. Thinking on the promotion and application path of innovative agricultural PPP model——Based on the Enlightenment of agricultural waste resource utilization in Qianjiang City. Rural Econ. 2019, 4, 116–121. [Google Scholar]
- Zhang, T.C.; Yan, T.W.; He, K.; Zhang, J.B. Contrary to farmers’ willingness of straw utilization to the behavior: Based on the MOA model. J. Arid Land Resour. Environ. 2019, 33, 30–35. [Google Scholar]
- Wang, J.H.; Deng, Y.Y.; Ma, Y.T. Relationships between Safe Pesticide Practice and Perceived Benefits and Subjective Norm, and the Moderation Role of Information Acquisition: Evidence from 971 Farmers in China. Inter. J. Env. Res. Pub. Heal. 2017, 14, 962. [Google Scholar] [CrossRef] [Green Version]
- Song, J.N.; Pu, Y.; Yang, W. Highlighting Regional Energy-Economic-Environmental Benefits of Agricultural Bioresources Utilization: An Integrated Model from Life Cycle Perspective. Sustainability 2019, 11, 3743. [Google Scholar] [CrossRef] [Green Version]
- Ajzen, I.; Madden, T.J. Prediction of goal-directed behavior: Attitudes, intentions, and perceived behavioral control. J. Exp. Soc. Psychol. 1986, 22, 453–474. [Google Scholar] [CrossRef]
- Conner, M.; Armitage, C.J. Extending the Theory of Planned Behavior: A Review and Avenues for Further Research. J. Appl. Soc. Psychol. 1998, 28, 1429–1464. [Google Scholar] [CrossRef]
- Xiong, S.Y.; Zhou, K. Study on the Influencing Mechanism of Farmers ‘Participation in the Utilization of Straw Resources. Rural Econ. 2019, 4, 110–115. [Google Scholar]
- Gan, C.L.; Tang, Y.H.; Chen, L.; Chen, Y.R.; Ren, L. Effects of the farmers’ cognition on the farmland transfer based on theory of planned behavior framework. China Popul. Resour. Environ. 2018, 28, 152–159. [Google Scholar]
- Christina, M.; Jennifer, F. Does Identity Incompatibility Lead to Disidentification? Internal Motivation to Be a Group Member Acts as Buffer for Sojourners from Independent Cultures, Whereas Extrinsic Motivation Acts as Buffer for Sojourners from Interdependent Cultures. Front. Psychol. 2017, 8, 1–10. [Google Scholar]
- Gbededo, M.A.; Liyanage, K. Identification and Alignment of the Social Aspects of Sustainable Manufacturing with the Theory of Motivation. Sustainability 2018, 10, 852. [Google Scholar] [CrossRef] [Green Version]
- Covington, M.V. Goal Theory, Motivation, and School Achievement: An Integrative Review. Annu. Rev. Psychol. 2000, 51, 171–200. [Google Scholar] [CrossRef] [Green Version]
- Ramirez-Andreotta, M.D.; Tapper, A.; Clough, D. Understanding the Intrinsic and Extrinsic Motivations Associated with Community Gardening to Improve Environmental Public Health Prevention and Intervention. Inter. J. Environ. Res. Pub. Heal. 2019, 16, 494. [Google Scholar] [CrossRef] [Green Version]
- Janmaimool, P. Application of Protection Motivation Theory to Investigate Sustainable Waste Management Behaviors. Sustainability 2017, 9, 1079. [Google Scholar] [CrossRef] [Green Version]
- Colakoglu, O.M.; Omur, A. Motivational measure of the instruction compared: Instruction based on the ARCS Motivation Theory V.S. traditional instruction in blended courses. Turkish Online J. Distance Educ. 2010, 11, 73–89. [Google Scholar]
- Jeno, L.M.; Raaheim, A.; Kristensen, S.M. The Relative Effect of Team-Based Learning on Motivation and Learning: A Self-Determination Theory Perspective. CBE Life Sci. Educ. 2017, 16, 1–12. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Cai, X.; Geng, X.L. Self-Protective Implicit Voice Belief and Employee Silence: A Research in the Context of China. Sci. Sci. Manag. S. T. 2016, 37, 153–163. [Google Scholar]
- Rippetoe, P.A.; Rogers, R.W. Effects of components of protection-motivation theory on adaptive and maladaptive coping with a health threat. J. Pers. Soc. Psychol. 1987, 52, 596–604. [Google Scholar] [CrossRef]
- Wilson, J.Q. The Economy of Patronage. J. Polit. Econ. 1961, 69, 369–380. [Google Scholar] [CrossRef]
- Bialobrzeska, O.; Elliot, A.J.; Wildschut, T. Nostalgia counteracts the negative relation between threat appraisals and intrinsic motivation in an educational context. Learn. Individ. Differ. 2019, 69, 219–224. [Google Scholar] [CrossRef] [Green Version]
- Zhang, J.; Zhang, Y.; Wang, R.; Chen, H.; Zhang, M.; Zhang, J. Predication of the sources of particulate polycyclic aromatic hydrocarbons in China with distinctive characteristics based on multivariate analysis. J. Clean. Prod. 2018, 185, 841–851. [Google Scholar] [CrossRef]
- Liu, M.P.; Nan, L.; Li, X.Q.; Zhao, L.J. Impact of environmental literacy on farmers’farmland ecological protection behavior. J. Arid Land Resour. Environ. 2019, 33, 53–59. [Google Scholar]
- Li, L.; Zhao, Q.; Zhang, J. Bottom-up emission inventories of multiple air pollutants from open straw burning: A case study of Jiangsu province, Eastern China. Atmos. Pollut. Res. 2018, 10, 501–507. [Google Scholar] [CrossRef]
- Li, H.; Min, X.; Dai, M.W.; Dong, X.J. The Biomass Potential and GHG (Greenhouse Gas) Emissions Mitigation of Straw-Based Biomass Power Plant: A Case Study in Anhui Province, China. Processes 2019, 7, 608. [Google Scholar] [CrossRef] [Green Version]
- Barrett, P. Structural equation modeling: Adjudging model fit. Pers. Indiv. Differ. 2007, 42, 815–824. [Google Scholar] [CrossRef]
- Floyd, D.L.; Steven, P.D.; Rogers, R.W. A Meta-Analysis of Research on Protection Motivation Theory. J. Appl. Soc. Psychol. 2000, 30, 407–429. [Google Scholar] [CrossRef]
- Bagozzi, R.P.; Yi, Y. On the evaluation of structural equation models. J. Acad. Market. Sci. 1988, 16, 74–94. [Google Scholar] [CrossRef]
- Liu, H.B.; Zhou, Y.P. Urbanization, Land Use Behavior and Land Quality in Rural China: An Analysis Based on Pressure-Response-Impact Framework and SEM Approach. Inter. J. Env. Res. Pub. Heal. 2018, 15, 2621. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Podsakoff, P.M. Self-Reports in Organizational Research: Problems and Prospects. J. Manag. 1986, 12, 531–544. [Google Scholar] [CrossRef]
Latent Variables | Measurable Variables | Measurement Content | Average | Standard Deviation | |
---|---|---|---|---|---|
Positive Broken Window Effect | A1 | Positive Examples | If someone participates in the green disposal of straw and gains a lot, I will envy or also want to participate | 3.58 | 0.936 |
A2 | No Incineration Supervision | If no one incinerates or discards the straw, I will also refuse to do it or dare not incinerate | 3.82 | 0.873 | |
A3 | Benefits-Oriented Publicity | If I gain benefits in the green utilization of straw, I will also publicize the benefits and persuade others to do so | 3.41 | 0.891 | |
A4 | Active Publicity | If I have explicitly refused to incinerate straw, then I will also supervise others or lead them to resist straw incineration | 3.11 | 0.957 | |
Negative Broken Window Effect | B1 | Supervision Loopholes | If others incinerate the straw without punishment, I may also do it | 2.76 | 0.849 |
B2 | Vacant System | I may incinerate straw if there is no corresponding punishment or supervision mechanism | 3.04 | 0.948 | |
B3 | Publicizing for Incineration | If I incinerate straw without being punished, I might also persuade others to do so | 2.46 | 0.92 | |
Seriousness | C1 | Seriousness of Environment | It is believed that straw incineration will affect the environment, economy and village image | 3.52 | 0.871 |
C2 | Seriousness of Health | It is believed that straw incineration will affect one’s health | 3.75 | 0.761 | |
C3 | Seriousness of Punishment | It is believed that incinerating straw will be punished | 3.89 | 0.758 | |
Susceptibility | D1 | Image Loss | Possibility of straw incineration affecting environment, economy and village image | 3.37 | 0.802 |
D2 | Loss of Health | Possibility of straw incineration affecting health | 3.58 | 0.745 | |
D3 | Money Loss | Possibility of punishment | 3.81 | 0.758 | |
Return | E1 | Money Return | Incinerating straw costs less money | 2.85 | 1.005 |
E2 | Time Return | Incinerating straw costs less time | 3.05 | 0.951 | |
E3 | Energy Return | Incinerating straw costs less energy | 3.16 | 0.94 | |
Self-Efficacy | F1 | Capability Efficacy | Having the ability to conduct green utilization of straw | 3.59 | 0.86 |
F2 | Money Efficacy | Having money to conduct green utilization of straw | 3.52 | 0.841 | |
F3 | Time Efficacy | Having time to conduct green utilization of straw | 3.36 | 0.919 | |
Response Efficacy | G1 | Environment Response | Green disposal of straw can optimize the environment and promote economic development | 3.47 | 0.878 |
G2 | Agriculture Response | Green disposal of straw can promote sustainable development of agriculture | 3.57 | 0.822 | |
G3 | Self-Response | Green disposal of straw can protect one’s health and prevent soil pollution | 3.6 | 0.834 | |
Response Cost | H1 | Money Cost | Green disposal of straw costs more money | 2.92 | 0.879 |
H2 | Time Cost | Green disposal of straw costs more time | 3.07 | 0.855 | |
H3 | Energy Cost | Green disposal of straw costs more energy | 3.17 | 0.864 | |
Subjective Norms | I1 | Leader Support | Support and recognition from village leaders | 3.76 | 0.783 |
I2 | Family Support | Support and recognition from family | 3.45 | 0.925 | |
I3 | Neighbor Support | Support and recognition from neighbors | 3.45 | 0.944 | |
I4 | Society Support | Green disposal of straw meets social trends and national requirements | 3.74 | 0.826 | |
Behavioral Attitude | J1 | Economically Beneficial | It is believed that green disposal of straw can increase household income | 3.74 | 0.795 |
J2 | Environmentally Beneficial | It is believed that green disposal of straw can promote sustainable ecological development and the transformation of green agriculture | 3.38 | 0.86 | |
J3 | Beneficial to Resource | It is believed that the green disposal of straw can solve the problem of idle straw and make full use of production resources | 3.68 | 0.864 | |
Perceived Behavioral Control | K1 | Financial Ability | I have the financial ability to invest the time and energy | 2.94 | 0.943 |
K2 | Leaning Ability | I have the ability of independent learning | 3.01 | 1.001 | |
K3 | Cognition of Policy | I am familiar with the policies and channels | 3.49 | 0.863 | |
K4 | Independence | I can independently decide how to dispose of the straw in a green way | 3.67 | 0.809 | |
Utilization Intention | L1 | Utilization Intention | Whether you are willing to participate in green disposal of straw or not | 3.04 | 0.82 |
L2 | Utilization Trend | Whether you are willing to learn related knowledge and policies or hold a positive attitude toward green utilization | 3.51 | 0.786 | |
Utilization Behavior | M1 | Behavior State | Whether the green disposal has started | 3.39 | 0.752 |
M2 | Channel State | Whether a channel of green disposal has been opened or the corresponding equipment has been purchased | 2.98 | 0.985 | |
M3 | Technical State | Whether you have learned or mastered relevant techniques, policies and so on | 3.33 | 0.91 |
Effect Pathway | Standardized Regression Coefficient | Standard Error | T-Value | Significance Level |
---|---|---|---|---|
S.E. | C.R. (Critical ratio) | p | ||
Subjective norms<---positive broken window effect | 0.314 | 0.048 | 4.845 | *** |
Subjective Norms<---negative broken window effect | −0.047 | 0.041 | −3.766 | *** |
Behavioral Attitude<---seriousness | 0.004 | 0.046 | 4.018 | *** |
Behavioral Attitude<---susceptibility | −0.004 | 0.05 | −3.874 | *** |
Behavioral Attitude<---return | −0.314 | 0.04 | −5.629 | *** |
Perceived Behavioral Control<---self-efficacy | 0.048 | 0.079 | 1.715 | ** |
Perceived Behavioral Control<---response efficacy | 0.129 | 0.087 | 4.294 | *** |
Perceived Behavioral Control<---response cost | −0.053 | 0.063 | −1.545 | ** |
Utilization Intention<---subjective norms | 0.164 | 0.053 | 4.075 | *** |
Utilization Intention<---behavioral attitude | −0.038 | 0.048 | −1.895 | ** |
Utilization Intention<---perceived behavioral control | 0.595 | 0.063 | 11.523 | *** |
Utilization Behavior<---utilization intention | 0.583 | 0.046 | 11.621 | *** |
A1<---positive broken window effect | 0.686 | 0.046 | 14.623 | *** |
A2<---positive broken window effect | 0.617 | 0.057 | 14.707 | *** |
A3<---positive broken window effect | 0.845 | 0.061 | 19.168 | *** |
A4<---positive broken window effect | 0.842 | 0.066 | 19.127 | *** |
B1<---negative broken window effect | 0.843 | 0.063 | 21.972 | *** |
B2<---negative broken window effect | 0.768 | 0.047 | 21.567 | *** |
B3<---negative broken window effect | 0.829 | 0.046 | 23.119 | *** |
C1<---seriousness | 0.67 | 0.072 | 12.273 | *** |
C2<---seriousness | 0.807 | 0.085 | 12.369 | *** |
C3<---seriousness | 0.613 | 0.063 | 12.616 | *** |
D1<---susceptibility | 0.716 | 0.071 | 12.539 | *** |
D2<---susceptibility | 0.702 | 0.073 | 12.408 | *** |
D3<---susceptibility | 0.641 | 0.069 | 12.271 | *** |
E1<---return | 0.739 | 0.068 | 14.992 | *** |
E2<---return | 0.77 | 0.064 | 15.35 | *** |
E3<---return | 0.691 | 0.058 | 14.951 | *** |
F1<---self-efficacy | 0.669 | 0.076 | 13.323 | *** |
F2<---self-efficacy | 0.672 | 0.073 | 13.503 | *** |
F3<---self-efficacy | 0.761 | 0.087 | 14.011 | *** |
G1<---response efficacy | 0.673 | 0.069 | 13,794 | *** |
G2<---response efficacy | 0.703 | 0.071 | 13.824 | *** |
G3<---response efficacy | 0.705 | 0.072 | 13.837 | *** |
H1<---response cost | 0.731 | 0.064 | 16.015 | *** |
H2<---response cost | 0.775 | 0.063 | 16.478 | *** |
H3<---response cost | 0.736 | 0.061 | 16.183 | *** |
I1<---subjective norms | 0.608 | 0.111 | 12.648 | *** |
I2<---subjective norms | 0.759 | 0.109 | 13.583 | *** |
I3<---subjective norms | 0.733 | 0.108 | 13.457 | *** |
I4<---subjective norms | 0.565 | 0.085 | 11.495 | *** |
J1<---behavioral attitude | 0.67 | 0.091 | 11.303 | *** |
J2<---behavioral attitude | 0.62 | 0.089 | 11.279 | *** |
J3<---behavioral attitude | 0.701 | 0.1 | 11.345 | *** |
K1<---perceived behavioral control | 0.645 | 0.078 | 13.927 | *** |
K2<---perceived behavioral control | 0.677 | 0.079 | 14.033 | *** |
K3<---perceived behavioral control | 0.678 | 0.069 | 14.036 | *** |
K4<---perceived behavioral control | 0.645 | 0.063 | 13.556 | *** |
L1<---utilization intention | 0.789 | 0.07 | 17.845 | *** |
L2<---utilization intention | 0.736 | 0.05 | 17.82 | *** |
M1<---utilization behavior | 0.743 | 0.082 | 16.739 | *** |
M2<---utilization behavior | 0.718 | 0.078 | 16.137 | *** |
M3<---utilization behavior | 0.688 | 0.072 | 15.632 | *** |
Hypothesis | Assumption Content | Result |
---|---|---|
H1 | External motivation has a positive effect on farmers’ subjective norms | |
H1a | Positive broken window has a positive effect on subjective norms | Accept |
H1b | Negative broken window has a negative effect on subjective norms | Accept |
H2 | Intrinsic motivation (threat assessment) has a positive effect on the behavior attitude of farmers | |
H2a | Seriousness has a positive effect on behavior attitude | Accept |
H2b | Susceptibility has a positive effect on behavior attitude | Refuse |
H2c | Return has a negative effect on behavior attitude | Accept |
H3 | Response analysis has a positive effect on the perceived behavior control of farmers | |
H3a | Self-efficacy has a positive effect on perceived behavior control | Accept |
H3b | Response efficacy has a positive effect on perceived behavior control | Accept |
H3c | Response cost has a negative effect on perceived behavior control | Accept |
H4 | Cognition has a positive effect on behavior | |
H4a | Subjective norms have a positive effect on utilization intention | Accept |
H4b | Behavior attitude has a positive effect on utilization intention | Refuse |
H4c | Perceived behavior control has a positive effect on utilization intention | Accept |
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Yu, L.; Liu, H.; Diabate, A.; Qian, Y.; Sibiri, H.; Yan, B. Assessing Influence Mechanism of Green Utilization of Agricultural Wastes in Five Provinces of China through Farmers’ Motivation-Cognition-Behavior. Int. J. Environ. Res. Public Health 2020, 17, 3381. https://doi.org/10.3390/ijerph17103381
Yu L, Liu H, Diabate A, Qian Y, Sibiri H, Yan B. Assessing Influence Mechanism of Green Utilization of Agricultural Wastes in Five Provinces of China through Farmers’ Motivation-Cognition-Behavior. International Journal of Environmental Research and Public Health. 2020; 17(10):3381. https://doi.org/10.3390/ijerph17103381
Chicago/Turabian StyleYu, Liying, Hongda Liu, Ardjouman Diabate, Yuyao Qian, Hagan Sibiri, and Bing Yan. 2020. "Assessing Influence Mechanism of Green Utilization of Agricultural Wastes in Five Provinces of China through Farmers’ Motivation-Cognition-Behavior" International Journal of Environmental Research and Public Health 17, no. 10: 3381. https://doi.org/10.3390/ijerph17103381