Understanding cognitive and socio-psychological factors determining farmers’ intentions to use improved grassland: Implications of land use policy for sustainable pasture production
TL;DR: In this article, the authors used the theory of planned behavior to determine farmers' cognitive and socio-psychological behavior to use improved grassland and found that a favorable attitude was found to use improving grassland, which is emerged from beliefs.
About: This article is published in Land Use Policy.The article was published on 2021-03-01. It has received 127 citations till now.
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TL;DR: In this article , an extension of the Theory of Planned Behaviour (TPB) was used to evaluate farmers' intentions to install a photovoltaic (PV) water pump in rural Pakistan and the farmers willingness to pay extra for green electricity.
156 citations
01 Jan 2002
TL;DR: In this paper, the authors used an ideal typology of Greek farms to identify different types of farms as regards their mode of survival, and linked each survival strategy to different motivations for and constraints against the adoption of alternative farm enterprises.
Abstract: Farm household survival strategies are acknowledged to determine the adoption of alternative farm enterprises as part of the farm household’s production and reproduction pattern and are, thus, used to identify the potential adopters of such enterprises. The present work utilises an ideal typology of Greek farms in order to identify different types of farms as regards their mode of survival. Each survival strategy is linked to different motives for and constraints against the adoption of alternative farm enterprises. Results show that three types of farm households may be identified, namely subsistence, survivalist and productivist farm households. The potential adopters of alternative farm enterprises may be traced among farm households that pursue a survivalist mode of production. It is argued that the diversity of farm structures observed within this type of farm households cannot be regarded as the decisive factor as far as their mode of survival is concerned. Rather, it is considered to form a context of different motivations for and constraints against the adoption of alternative farming activities. r 2002 Elsevier Science Ltd. All rights reserved.
98 citations
TL;DR: In this article , the authors used the Levenberg-Marquardt algorithm to find the best topology of the ANN model at a hidden layer consisting of 10 neurons, including the lowest mean absolute percentage error (14.42) and the highest R2 (0.79).
95 citations
TL;DR: Li et al. as discussed by the authors used a dynamic panel data model to empirically determine the CO2 emission reduction effects of different green finance instruments under different environmental regulatory intensities, and found that green finance tools had significant negative effects on the intensity of CO2 emissions.
Abstract: Green finance and environmental regulation can reduce CO2 emissions and promote the sustainability of economic development. Based on panel data of 126 resource-based prefecture-level cities in China from 2005 to 2017, the current study used a dynamic panel data model to empirically determine the CO2 emission reduction effects of different green finance instruments under different environmental regulatory intensities. The results showed that green finance tools had significant negative effects on the intensity of CO2 emissions, and green finance can adapt to environmental regulations of different intensities, which cooperated to promote carbon emission reduction. Moreover, in comparison, the debt-based green finance instrument had a stronger effect than the equity-based green finance instrument, and they did not show a coupling relationship. An administrative adjustment in green finance and environmental regulation is required to reduce environmental emissions and to improve sustainable development.
40 citations
TL;DR: In this article , the authors evaluated the amount of energy input-output of cotton productions and their environmental interventions and found that the major energy consumed by three culprits, i.e., chemical fertilizer, diesel fuel, and irrigation water, are the most probable cause of poor energy use efficiency.
Abstract: The concept of agricultural and environmental sustainability refers to minimizing the degradation of natural resources while increasing crop productions; assessment of inflow and outflow energy resources is helpful in highlighting the resilience of the system and maintaining its productivity. In this regard, the current study evaluated the amount of energy input–output of cotton productions and their environmental interventions. Data are randomly collected from 400 cotton farmers through face-to-face interview. Results suggested that the major energy is consumed by three culprits, i.e., chemical fertilizer, diesel fuel, and irrigation water (11,532.60, 11,121.54, and 4,531.97 MJ ha −1 , respectively). Total greenhouse gas (GHG) emission is 1,106.12 kg CO 2eq ha −1 with the main share coming from diesel fuel, machinery, and irrigation water. Stimulating data of energies, e.g., energy use efficiency (1.53), specific energy (7.69 MJ kg −1 ), energy productivity (0.13 kg MJ −1 ), and net energy gained (16,409.77 MJ ha −1 ). Further analysis using data envelopment analysis (DEA) showed that low technical efficiency, i.e., 69.02%, is the most probable cause of poor energy use efficiency. The impermanent trend in growth of energy efficiency has been witnessed with plausible potential of energy savings from 4,048.012 to 16,194.77 MJ ha −1 and a reduction of 148.96–595.96 kg CO 2eq ha −1 in GHG emission. Cobb–Douglas production function is further applied to discover the associations of energy input to output, which inferred that chemical fertilizer, diesel fuel, machinery, and biocides have significant effect on cotton yield. The marginal physical productivity (MPP) values obliged that the additional use in energy (1 MJ) from fuel (diesel), biocides, and machinery can enhance cotton yield at the rate of 0.35, 1.52, and 0.45 kg ha −1 , respectively. Energy saving best links with energy sharing data, i.e., 55.66% (direct), 44.34% (indirect), 21.05% (renewable), and 78.95% (nonrenewable), further unveiled the high usage of nonrenewable energy resources (fossil fuels) that ultimately contributes to high emissions of GHGs. We hope that these findings could help in the management of energy budget that we believe will reduce the high emissions of GHGs.
27 citations
References
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TL;DR: Ajzen, 1985, 1987, this article reviewed the theory of planned behavior and some unresolved issues and concluded that the theory is well supported by empirical evidence and that intention to perform behaviors of different kinds can be predicted with high accuracy from attitudes toward the behavior, subjective norms, and perceived behavioral control; and these intentions, together with perceptions of behavioral control, account for considerable variance in actual behavior.
65,095 citations
Book•
21 Jul 2011
TL;DR: Structural Equation Models: The Basics using the EQS Program and testing for Construct Validity: The Multitrait-Multimethod Model and Change Over Time: The Latent Growth Curve Model.
Abstract: Psychology is a science that advances by leaps and bounds The impulse of new mathematical models along with the incorporation of computers to research has drawn a new reality with many methodological progresses that only a few people could imagine not too long ago Such progress has no doubt revolutionized the panorama of research in the behavioral sciences Structural Equation Models are a clear example of this Under this label are usually included a series of state-of-the-art multivariate statistical procedures that allow the researcher to test theoryguided hypotheses with clearly confi rmatory ends as well as to establish causal relations among variables Confi rmatory factor analysis, the study of measurement invariance, or the multitraitmultimethod models are some of the procedures that stem from this methodology In this sense, it would be diffi cult to fi nd a scientifi c journal that publishes empirical works in psychology that does not address some of these issues, so their current transcendence is undeniable The manual written by the Full Professor of the University of Ottawa, Barbara M Byrne, is a link in a series of books that address this topic Throughout her long academic trajectory, Professor Byrne developed interesting and popular work focused on bringing the researcher and the professional layman—and not so layman—closer to the diverse statistical programs available on the market for data analysis from the perspective of structural equation models (ie, LISREL, AMOS, EQS) (Byrne, 1998, 2001, 2006) Bearing this in mind, the main goal of this work is to introduce the reader to the basic concepts of this methodology, in a simple and entertaining way, avoiding mathematical technicisms and statistical jargon For this purpose, we used the statistical program Mplus 60 (Muthen & Muthen, 2007-2010), an extremely suggestive software that incorporates interesting applications The authoress provides a practical guide that leads the reader through illustrative examples of how to proceed step by step with the Mplus, from the initial specifi cations of the model to the interpretation of the output fi les On the one hand, we underline that the data used proceed from prior investigations and can be consulted in the Internet, offering the reader the possibility of practicing with them (http://wwwpsypresscom/sem-with-mplus/ datasets/); on the other hand, updating the information with novel and apt bibliographic references allows the reader to study in more depth the diverse topics that are presented in the manual, if he or she so desires The book consists of four sections, with a total of 12 chapters The fi rst section, Chapters 1 and 2, addresses introductory terms related to structural equation models and working with the Mplus program at a user-level The second unit focuses on data analysis with a single group In Chapter 3, the factor validity of the self-concept is tested by means of confi rmatory factor analysis In Chapter 4, the authoress performs a fi rst-order confi rmatory factor analysis, in which she examines the validity of the scores of the Maslach Burnout Inventory (MBI) in a sample of teachers In Chapter 5, the internal structure of the scores on the Beck Depression Inventory-II is analyzed by means of second-order confi rmatory factor analysis in a sample of Chinese adolescents In the next chapter, the complete model of structural equations is tested, and the authoress examines the causal relation established between diverse variables (ie, work climate, self-esteem, social support) and Burnout The third section of the manual is, in my opinion, the most interesting, not only because of the expansion of the study of measurement invariance in recent years but also because of the expansion it may possibly have in the future In this section, Professor Byrne goes into multigroup comparisons Specifi cally, in Chapter 7, she examines the factor equivalence of the MBI in two samples of teachers by means of the analysis of covariance structures In this chapter, she introduces relevant concepts, such as types of invariance (confi gural, metric, and strict) or the invariance of partial measurement In Chapter 8, she also analyzes measurement invariance, using for this purpose the analysis of mean and covariance structures This analysis, in comparison to the analysis of covariance structures, allows contrasting the latent means of two or more groups With this goal, she verifi es whether there is measurement invariance between the scores on the Self-description Questionnaire-I in Nigerian and Australian adolescents In Chapter 9, she proposes a complete model of structural equations in which she tests the causal structure through the procedure of cross validation Lastly, in the fourth section, she reveals three very interesting topics, that are also up-to-date and that, to some degree, go beyond the initial goal of the book, such as the multitrait-multimethod models, latent growth curves, and multilevel models Summing up, the work “Structural Equation Modeling with Mplus: Basic concepts, applications, and programming” is of enormous interest and utility for all professionals of psychology and related sciences who, without having exhaustive knowledge of the details of structural equation models, wish to test their hypothetical models by means of the Mplus program No doubt, this is a reference manual, a must-read that is accessible and that has a high degree of methodological rigor We hope that the readers
16,616 citations
Book•
01 Nov 2000TL;DR: In this article, the EQS program is used to test the factorial verifiability of a theoretical construct and its invariance to a Causal Structure using the First-Order CFA model.
Abstract: Contents: Part I: Introduction. Structural Equation Models: The Basics. Using the EQS Program. Part II: Single-Group Analyses. Application 1: Testing for the Factorial Validity of a Theoretical Construct (First-Order CFA Model). Application 2: Testing for the Factorial Validity of Scores From a Measuring Instrument (First-Order CFA Model). Application 3: Testing for the Factorial Validity of Scores from a Measuring Instrument (Second-Order CFA Model). Application 4: Testing for the Validity of a Causal Structure. Part III: Multiple-Group Analyses. Application 5: Testing for the Factorial Invariance of a Measuring Instrument. Application 6: Testing for the Invariance of a Causal Structure. Application 7: Testing for Latent Mean Differences (First-Order CFA Model). Application 8: Testing for Latent Mean Differences (Second-Order CFA Model). Part IV: Other Important Topics. Application 9: Testing for Construct Validity: The Multitrait-Multimethod Model. Application 10: Testing for Change Over Time: The Latent Growth Curve Model. Application 11: Testing for Within- and Between-Level Variance: The Multilevel Model.
13,439 citations
01 Jan 2004
TL;DR: The theory of Planned Behaviour is one of the models most frequently used in the literature to explore pro-environmental behaviour including recycling, travel mode choice, energy consumption, water conservation, food choice, and ethical investment.
Abstract: The theory of Planned Behaviour is one of the models most frequently used in the literature to explore pro-environmental behaviour including recycling, travel mode choice, energy consumption, water conservation, food choice, and ethical investment (Stern, 2000; Staats, 2003). Armitage and Conner (2001) identified its application in 154 different contexts. The Theory of Planned Behaviour (Ajzen, 1988) assumes that the best prediction of behaviour is given by asking people if they are intending to behave in a certain way. Here we note that the intention will not express itself in behaviour if it is physically impossible to perform the behaviour or if unexpected barriers stand in the way. Assuming intention can explain behaviour, how can intention be explained?. According to Azjen, three determinants explain behavioural intention: 1. The attitude (opinions of oneself about the behaviour); 2. The subjective norm (opinions of others about the behaviour); 3. The perceived behavioural control (self-efficacy towards the behaviour).
6,061 citations
TL;DR: The historical development of a from other indexes of internal consistency (split-half reliability and Kuder-Richardson 20) and four myths associated with a are discussed, including that it is a fixed property of the scale and that higher values are always preferred over lower ones.
Abstract: Cronbach's a is the most widely used index of the reliability of a scale. However, its use and interpretation can be subject to a number of errors. This article discusses the historical development of a from other indexes of internal consistency (split-half reliability and Kuder-Richardson 20) and discusses four myths associated with a: (a) that it is a fixed property of the scale, (b) that it measures only the internal consistency of the scale, (c) that higher values are always preferred over lower ones, and (d) that it is restricted to the range of 0 to 1. It provides some recommendations for acceptable values of a in different situations.
2,757 citations