Promoting the Adoption of Agricultural Green Production Technologies for Sustainable Farming: A MultiAttribute Decision Analysis
Abstract
:1. Introduction
 (1)
 This study enriches the literature on farmers’ adoption decisions by analyzing a comprehensive framework of behavioral factors in promoting agricultural green production.
 (2)
 Considering the uncertainties and ambiguity in the decisionmaking process of adopting AGPTs, this study employs PLTSs to model farmers’ knowledge and thoughts.
 (3)
 This study develops a novel MADA approach by extending the PSI method into the PLTSs environment to help policymakers understand farmers’ adoption decisions on AGPTs.
 (4)
 Based on the proposed PLPSI method, we analyze the relative importance of the behavioral factors promoting the adoption of AGPTs among rice growers in Ghana.
2. Theoretical Background and Literature Review
2.1. Farmer DecisionMaking and Behavioral Factors
2.2. Related Works on AGPTs
2.3. MADA and Green Technology Adoption
2.4. Probabilistic Linguistic Term Set
 (i)
 If $E({L}_{1}(p))>E({L}_{2}(p))$, then ${L}_{1}(p)$ is more significant than ${L}_{2}(p)$, denoted by ${L}_{1}(p)>{L}_{2}(p)$;
 (ii)
 If $E({L}_{1}(p))=E({L}_{2}(p))$, then
 (a)
 if $\sigma ({L}_{1}(p))<\sigma ({L}_{1}(p))$, then ${L}_{1}(p)>{L}_{2}(p)$;
 (b)
 if $\sigma ({L}_{1}(p))=\sigma ({L}_{1}(p))$, then ${L}_{1}(p)$ is indifferent to ${L}_{2}(p)$, indicated as ${L}_{1}(p)~{L}_{2}(p)$.
3. Methodology
3.1. Problem Description
3.2. Probabilistic Linguistic Preference Selection Index
 Step 3: Calculate the preference variation value regarding each AGPT as follows:
 Step 4: Obtain the deviation in the preference variation value for every AGPT as follows:
 Step 5: Determine the total preference value for each AGPT as follows:
 Step 6: Ascertain the preference selection index ${\Pi}_{i}$ of each factor ${f}_{i}$ based on the probabilistic linguistic weighted average (PLWA) operator [33] as follows:
 Step 7: Calculate the score $s({\Pi}_{i})$ according to Definition 2.
 Step 8: Rank the factors in descending order of $s({\Pi}_{i})$. The larger $s({\Pi}_{i})$, the higher the extent to which the factor ${f}_{i}$ influences the adoption of the AGPTs.
4. Decision Analysis
4.1. Research Approach
4.2. Results
 Step 1: Based on the questionnaire responses, we employed the PLTS described in Section 2.3 and constructed the initial probabilistic linguistic decision matrix of Table 5.
 Step 2: Following the second step, we computed the mean of each AGPT evaluation value using the decision matrix of Table 3 and Equation (4). The result is given as follows:
 Step 3: Per Equation (5), the preference variation value regarding each AGPT was calculated, and the result is given as follows.
 Step 4: Furthermore, we obtained the deviation in the preference variation value for every AGPT based on Equation (6), and we present the outcome in column 3 of Table 4.
 Step 5: The total preference value for each AGPT was determined according to Equation (7), and we show it in column 4 of Table 4.
 Step 6: Then, we computed the preference selection index ${\Pi}_{i}(i=1,2,3,4,5,6,7,8,9)$. based on Equation (8), and the outcome is given in Table 6.
 Step 7: Finally, we deduced the preference selection index scores since they were probabilistic linguistic elements using Definition 2. The nine behavioral factors’ overall scores are listed in column 5 of Table 7. To ascertain the dimensions’ scores, we averaged the individual factors’ scores within a dimension, and we present the outcome in column 2 of Table 7.
 Step 8: The ranking positions of the behavioral factors are presented as follows:
4.3. Discussion
4.4. Implications
4.5. Comparative and Sensitivity Analysis
4.5.1. Comparative Study
4.5.2. Sensitivity Analysis
5. Conclusions
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Constructs  Factors  References 

Dispositional  Personality  [66,67] 
Resistance to change  [57]  
Risk tolerance  [68,69]  
Moral and environmental concerns  [58,59,60]  
Farming objective  [68,70]  
Social  Descriptive norm  [41,62] 
Injunctive norm  [63,68]  
Signaling motives  [62,63]  
Cognitive  Knowledge  [65] 
Perceived control  [63,71]  
Perceived costs and benefits  [58,64,72]  
Perceived risk  [65,72] 
Author  Country  Study Objective  Method/ Approach  Study Results 

[76]  Malaysia  To measure the intention level of farmers to adopt GFT based on the psychological aspects based on the theory of planned behavior (TPB)  Structural Equation Modeling  The results have shown that direct and indirect attitude and indirect subjective norm, as well as direct–indirect perceived behavioral control, have a positive and significant influence on the intention of adopting. 
[77]  China  To examine the logical relationship of farmers’ willingness to adopt green fertilization technology  Structural Equation Modeling  The results showed that adoption motivation, adoption opportunity, technical operation ability, and antirisk ability had significant positive direct effects on adoption willingness, which were 0.610, 0.381, 0.491, and 0.297, respectively. Trust had an indirect effect, which was 0.191. 
[43]  China  To perform social network analysis to identify critical stakeholders and barriers in agriculture green technology diffusion  Network Analysis  The results show that agricultural research institutes, universities, agribusiness, agencies of township promotion, the government, and farmers’ relatives are key stakeholders and that the limited market demand for green technology and the high cost of its diffusion are two main barriers. 
[78]  China  To evaluate the contribution of time preferences to farmers’ technology adoption behavior  Econometric Modeling  The results show that time preferences significantly reduce technology adoption; in particular, farmers who are more presentbiased have a lower proportion of technology adoption. 
[74]  China  To study the factors influencing tea farmers’ adoption of PGCT for tea plant pest control  Structural Equation Modeling  The results indicated that subjective norms and perceived behavioral control have a positive and significant effect on intention, while attitude has no significant effect on the intention 
[79]  China  To analyze the key factors affecting the development of green agriculture  Econometric Modeling  The results show that a farmer’s age, land type, compensation for land transfer, technical service organization, related training, and economic and technological subsidies had significant effects on their green agricultural production willingness. 
[73]  China  To analyze the formation mechanism of the peer effects  Econometric Modeling  The authors found peer effects in the adoption behavior of green control techniques among farmers, which only existed in core and intermediate farmers and not in marginal farmers 
Author  Country  Application Area  Study Objective  Method/ Approach 

[81]  China  Emerging industries  To evaluate green technology innovation on ecological economic efficiency of strategic emerging industries  Entropyweighted TOPSIS 
[82]  China  Emerging industries  To assess the barriers of green technology adoption for enterprises  Fuzzy AHP 
[83]  Pakistan  Energy  To assess the barriers to the implementation of cleaner energy technologies  Modify Delphi and fuzzy AHP 
[84]  United Kingdom (UK)  Building construction  To select retrofit for nondomestic building by using green technology  AHP 
[25]  Ghana  Energy  To identify and rank the barriers to renewable energy development  MULTIMOORAEDAS 
[85]  Turkey  Energy  To evaluate solar projects  AHP and fuzzy VIKOR 
[86]  Egypt  Transportation  To select an electric bus for green transportation  AHP and TOPSIS 
[7]  Pakistan  General  To develop an integrated green technology framework to fill a gap in the literature by prioritizing green technologies’ most critical attributes  Fuzzy AHP 
[87]  Ghana  Humanresource management  To investigate critical barriers to green human resource management (GHRM) implementation  PSI 
[8]  Pakistan  General  To develop an integrated strategic framework based on strengths, weaknesses, opportunities, and threats (SWOT) for effective green technology planning  Gray AHP and gray TOPSIS 
[88]  Ghana  Energy  To assess renewable energy barriers and prioritize their adoption and development strategies  CRITIC and fuzzy TOPSIS 
$${g}_{1}$$

$${g}_{2}$$

$$\cdots $$

$${g}_{n}$$
 
$${f}_{1}$$

$${L}_{11}({p}_{11})$$

$${L}_{12}({p}_{12})$$

$$\cdots $$

$${L}_{1n}({p}_{1n})$$

$${f}_{2}$$

$${L}_{21}({p}_{21})$$

$${L}_{22}({p}_{22})$$

$$\cdots $$

$${L}_{2n}({p}_{2n})$$

$$\vdots $$

$$\vdots $$

$$\vdots $$

$$\cdots $$

$$\vdots $$

$${f}_{m}$$

$${L}_{m1}({p}_{m1})$$

$${L}_{m2}({p}_{m2})$$

$$\cdots $$

$${L}_{mn}({p}_{mn})$$

Factors  ${\mathit{g}}_{1}$  ${\mathit{g}}_{2}$  ${\mathit{g}}_{3}$  ${\mathit{g}}_{4}$ 

${f}_{1}$  $\left\{\begin{array}{l}{s}_{1}(0.110),{s}_{2}(0.110),\\ {s}_{3}(0.140),{s}_{4}(0.110),\\ {s}_{5}(0.540)\end{array}\right\}$  $\left\{\begin{array}{l}{s}_{1}(0.025),{s}_{2}(0.115),\\ {s}_{3}(0.160),{s}_{4}(0.100),\\ {s}_{5}(0.600)\end{array}\right\}$  $\left\{\begin{array}{l}{s}_{1}(0.030),{s}_{2}(0.120),\\ {s}_{3}(0.160),{s}_{4}(0.110),\\ {s}_{5}(0.580)\end{array}\right\}$  $\left\{\begin{array}{l}{s}_{1}(0.190),{s}_{2}(0.050),\\ {s}_{3}(0.150),{s}_{4}(0.110),\\ {s}_{5}(0.520)\end{array}\right\}$ 
${f}_{2}$  $\left\{\begin{array}{l}{s}_{1}(0.020),{s}_{2}(0.115),\\ {s}_{3}(0.160),{s}_{4}(0.120),\\ {s}_{5}(0.590)\end{array}\right\}$  $\left\{\begin{array}{l}{s}_{1}(0.060),{s}_{2}(0.115),\\ {s}_{3}(0.170),{s}_{4}(0.100),\\ {s}_{5}(0.560)\end{array}\right\}$  $\left\{\begin{array}{l}{s}_{1}(0.080),{s}_{2}(0.120),\\ {s}_{3}(0.130),{s}_{4}(0.090),\\ {s}_{5}(0.600)\end{array}\right\}$  $\left\{\begin{array}{l}{s}_{1}(0.040),{s}_{2}(0.125),\\ {s}_{3}(0.115),{s}_{4}(0.285),\\ {s}_{5}(0.435)\end{array}\right\}$ 
${f}_{3}$  $\left\{\begin{array}{l}{s}_{1}(0.025),{s}_{2}(0.120),\\ {s}_{3}(0.155),{s}_{4}(0.235),\\ {s}_{5}(0.465)\end{array}\right\}$  $\left\{\begin{array}{l}{s}_{1}(0.025),{s}_{2}(0.110),\\ {s}_{3}(0.155),{s}_{4}(0.235),\\ {s}_{5}(0.475)\end{array}\right\}$  $\left\{\begin{array}{l}{s}_{1}(0.095),{s}_{2}(0.125),\\ {s}_{3}(0.160),{s}_{4}(0.230),\\ {s}_{5}(0.390)\end{array}\right\}$  $\left\{\begin{array}{l}{s}_{1}(0.125),{s}_{2}(0.030),\\ {s}_{3}(0.280),{s}_{4}(0.115),\\ {s}_{5}(0.450)\end{array}\right\}$ 
${f}_{4}$  $\left\{\begin{array}{l}{s}_{1}(0.020),{s}_{2}(0.120),\\ {s}_{3}(0.155),{s}_{4}(0.115),\\ {s}_{5}(0.590)\end{array}\right\}$  $\left\{\begin{array}{l}{s}_{1}(0.035),{s}_{2}(0.110),\\ {s}_{3}(0.160),{s}_{4}(0.095),\\ {s}_{5}(0.600)\end{array}\right\}$  $\left\{\begin{array}{l}{s}_{1}(0.025),{s}_{2}(0.120),\\ {s}_{3}(0.155),{s}_{4}(0.105),\\ {s}_{5}(0.595)\end{array}\right\}$  $\left\{\begin{array}{l}{s}_{1}(0.040),{s}_{2}(0.165),\\ {s}_{3}(0.220),{s}_{4}(0.145),\\ {s}_{5}(0.430)\end{array}\right\}$ 
${f}_{5}$  $\left\{\begin{array}{l}{s}_{1}(0.040),{s}_{2}(0.120),\\ {s}_{3}(0.160),{s}_{4}(0.120),\\ {s}_{5}(0.570)\end{array}\right\}$  $\left\{\begin{array}{l}{s}_{1}(0.020),{s}_{2}(0.100),\\ {s}_{3}(0.170),{s}_{4}(0.100),\\ {s}_{5}(0.630)\end{array}\right\}$  $\left\{\begin{array}{l}{s}_{1}(0.060),{s}_{2}(0.115),\\ {s}_{3}(0.150),{s}_{4}(0.090),\\ {s}_{5}(0.590)\end{array}\right\}$  $\left\{\begin{array}{l}{s}_{1}(0.150),{s}_{2}(0.055),\\ {s}_{3}(0.110),{s}_{4}(0.250),\\ {s}_{5}(0.435)\end{array}\right\}$ 
${f}_{6}$  $\left\{\begin{array}{l}{s}_{1}(0.030),{s}_{2}(0.115),\\ {s}_{3}(0.155),{s}_{4}(0.120),\\ {s}_{5}(0.580)\end{array}\right\}$  $\left\{\begin{array}{l}{s}_{1}(0.100),{s}_{2}(0.120),\\ {s}_{3}(0.170),{s}_{4}(0.100),\\ {s}_{5}(0.560)\end{array}\right\}$  $\left\{\begin{array}{l}{s}_{1}(0.080),{s}_{2}(0.110),\\ {s}_{3}(0.120),{s}_{4}(0.100),\\ {s}_{5}(0.610)\end{array}\right\}$  $\left\{\begin{array}{l}{s}_{1}(0.060),{s}_{2}(0.140),\\ {s}_{3}(0.110),{s}_{4}(0.265),\\ {s}_{5}(0.425)\end{array}\right\}$ 
${f}_{7}$  $\left\{\begin{array}{l}{s}_{1}(0.300),{s}_{2}(0.120),\\ {s}_{3}(0.160),{s}_{4}(0.120),\\ {s}_{5}(0.590)\end{array}\right\}$  $\left\{\begin{array}{l}{s}_{1}(0.040),{s}_{2}(0.110),\\ {s}_{3}(0.150),{s}_{4}(0.100),\\ {s}_{5}(0.600)\end{array}\right\}$  $\left\{\begin{array}{l}{s}_{1}(0.110),{s}_{2}(0.125),\\ {s}_{3}(0.155),{s}_{4}(0.105),\\ {s}_{5}(0.505)\end{array}\right\}$  $\left\{\begin{array}{l}{s}_{1}(0.040),{s}_{2}(0.130),\\ {s}_{3}(0.120),{s}_{4}(0.240),\\ {s}_{5}(0.480)\end{array}\right\}$ 
${f}_{8}$  $\left\{\begin{array}{l}{s}_{1}(0.020),{s}_{2}(0.090),\\ {s}_{3}(0.150),{s}_{4}(0.120),\\ {s}_{5}(0.640)\end{array}\right\}$  $\left\{\begin{array}{l}{s}_{1}(0.030),{s}_{2}(0.110),\\ {s}_{3}(0.120),{s}_{4}(0.100),\\ {s}_{5}(0.650)\end{array}\right\}$  $\left\{\begin{array}{l}{s}_{1}(0.025),{s}_{2}(0.120),\\ {s}_{3}(0.155),{s}_{4}(0.100),\\ {s}_{5}(0.610)\end{array}\right\}$  $\left\{\begin{array}{l}{s}_{1}(0.145),{s}_{2}(0.055),\\ {s}_{3}(0.105),{s}_{4}(0.275),\\ {s}_{5}(0.420)\end{array}\right\}$ 
${f}_{9}$  $\left\{\begin{array}{l}{s}_{1}(0.025),{s}_{2}(0.115),\\ {s}_{3}(0.155),{s}_{4}(0.115),\\ {s}_{5}(0.590)\end{array}\right\}$  $\left\{\begin{array}{l}{s}_{1}(0.075),{s}_{2}(0.115),\\ {s}_{3}(0.155),{s}_{4}(0.105),\\ {s}_{5}(0.550)\end{array}\right\}$  $\left\{\begin{array}{l}{s}_{1}(0.055),{s}_{2}(0.125),\\ {s}_{3}(0.160),{s}_{4}(0.110),\\ {s}_{5}(0.550)\end{array}\right\}$  $\left\{\begin{array}{l}{s}_{1}(0.130),{s}_{2}(0.040),\\ {s}_{3}(0.235),{s}_{4}(0.155),\\ {s}_{5}(0.440)\end{array}\right\}$ 
Mean  $\left\{\begin{array}{l}{s}_{0.066},{s}_{0.228},{s}_{0.463},\\ {s}_{0.522},{s}_{2.864}\end{array}\right\}$  $\left\{\begin{array}{l}{s}_{0.046},{s}_{0.223},{s}_{0.470},\\ {s}_{0.460},{s}_{2.903}\end{array}\right\}$  $\left\{\begin{array}{l}{s}_{0.062},{s}_{0.240},{s}_{0.448},\\ {s}_{0.462},{s}_{2.794}\end{array}\right\}$  $\left\{\begin{array}{l}{s}_{0.102},{s}_{0.176},{s}_{0.482},\\ {s}_{0.818},{s}_{2.242}\end{array}\right\}$ 
Factors  PSI $\left({\Pi}_{\mathit{i}}\right)$ 

${f}_{1}$  $\left\{\begin{array}{l}{s}_{0.088},{s}_{0.199},{s}_{0.457},\\ {s}_{0.430},{s}_{2.801}\end{array}\right\}$ 
${f}_{2}$  $\left\{\begin{array}{l}{s}_{0.050},{s}_{0.237},{s}_{0.434},\\ {s}_{0.589},{s}_{2.737}\end{array}\right\}$ 
${f}_{3}$  $\left\{\begin{array}{l}{s}_{0.066},{s}_{0.194},{s}_{0.559},\\ {s}_{0.820},{s}_{2.230}\end{array}\right\}$ 
${f}_{4}$  $\left\{\begin{array}{l}{s}_{0.030},{s}_{0.256},{s}_{0.516},\\ {s}_{0.459},{s}_{2.776}\end{array}\right\}$ 
${f}_{5}$  $\left\{\begin{array}{l}{s}_{0.066},{s}_{0.196},{s}_{0.444},\\ {s}_{0.556},{s}_{2.789}\end{array}\right\}$ 
${f}_{6}$  $\left\{\begin{array}{l}{s}_{0.067},{s}_{0.242},{s}_{0.419},\\ {s}_{0.580},{s}_{2.724}\end{array}\right\}$ 
${f}_{7}$  $\left\{\begin{array}{l}{s}_{0.125},{s}_{0.242},{s}_{0.440},\\ {s}_{0.560},{s}_{2.728}\end{array}\right\}$ 
${f}_{8}$  $\left\{\begin{array}{l}{s}_{0.054},{s}_{0.188},{s}_{0.398},\\ {s}_{0.589},{s}_{2.912}\end{array}\right\}$ 
${f}_{9}$  $\left\{\begin{array}{l}{s}_{0.070},{s}_{0.199},{s}_{0.526},\\ {s}_{0.483},{s}_{2.670}\end{array}\right\}$ 
Dimensions  Average Score  Dimension Ranking  Factors  $\mathbf{Overall}\mathbf{Scores}\mathit{s}\left({\Pi}_{\mathit{i}}\right)$  Global Ranking 

Dispositional context  ${S}_{0.793}$  $3rd$  ${f}_{1}$  ${S}_{0.795}$  $7th$ 
${f}_{2}$  ${S}_{0.809}$  $4th$  
${f}_{3}$  ${S}_{0.774}$  $9th$  
Social context  ${S}_{0.809}$  $2nd$  ${f}_{4}$  ${S}_{0.807}$  $5th$ 
${f}_{5}$  ${S}_{0.810}$  $3rd$  
Cognitive context  ${S}_{0.811}$  $1st$  ${f}_{6}$  ${S}_{0.806}$  $6th$ 
${f}_{7}$  ${S}_{0.819}$  $2nd$  
${f}_{8}$  ${S}_{0.828}$  $1st$  
${f}_{9}$  ${S}_{0.790}$  $8th$ 
No.  Methods  Rank 

1  PLTOPSIS  $\begin{array}{l}{f}_{8}\succ {f}_{7}\succ {f}_{2}\succ {f}_{4}\succ {f}_{5}\succ \\ {f}_{6}\succ {f}_{1}\succ {f}_{9}\succ {f}_{3}\end{array}$ 
2  PLEDAS  $\begin{array}{l}{f}_{7}\succ {f}_{8}\succ {f}_{5}\succ {f}_{2}\succ {f}_{6}\succ \\ {f}_{4}\succ {f}_{1}\succ {f}_{9}\approx {f}_{3}\end{array}$ 
3  PLWASPAS  $\begin{array}{l}{f}_{8}\succ {f}_{7}\succ {f}_{5}\succ {f}_{2}\succ {f}_{4}\succ \\ {f}_{6}\succ {f}_{1}\succ {f}_{9}\succ {f}_{3}\end{array}$ 
4  Our proposed method  $\begin{array}{l}{f}_{8}\succ {f}_{7}\succ {f}_{5}\succ {f}_{2}\succ {f}_{4}\succ \\ {f}_{6}\succ {f}_{1}\succ {f}_{9}\succ {f}_{3}\end{array}$ 
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AsieduAyeh, L.O.; Zheng, X.; Agbodah, K.; Dogbe, B.S.; Darko, A.P. Promoting the Adoption of Agricultural Green Production Technologies for Sustainable Farming: A MultiAttribute Decision Analysis. Sustainability 2022, 14, 9977. https://doi.org/10.3390/su14169977
AsieduAyeh LO, Zheng X, Agbodah K, Dogbe BS, Darko AP. Promoting the Adoption of Agricultural Green Production Technologies for Sustainable Farming: A MultiAttribute Decision Analysis. Sustainability. 2022; 14(16):9977. https://doi.org/10.3390/su14169977
Chicago/Turabian StyleAsieduAyeh, Love Offeibea, Xungang Zheng, Kobina Agbodah, Bright Senyo Dogbe, and Adjei Peter Darko. 2022. "Promoting the Adoption of Agricultural Green Production Technologies for Sustainable Farming: A MultiAttribute Decision Analysis" Sustainability 14, no. 16: 9977. https://doi.org/10.3390/su14169977