This section presents the demography of agripreneurs, emporographics of agri-ventures, agripreneurs’ satisfaction, and the effect of demographic and emporographics on agripreneurs’ satisfaction.
Demographics and Emporographics of Agripreneurs
Table 2: Demographic Characteristics of Agripreneurs
Category
|
|
Frequency
(N = 784)
|
%
|
Age (in years)
|
Upto 35
|
350
|
44.6
|
36-55
|
158
|
20.1
|
Above 55
|
277
|
35.3
|
Gender
|
Male
|
574
|
73.2
|
Female
|
210
|
26.8
|
Education level
|
Primary School
|
150
|
19.1
|
Secondary School
|
130
|
16.6
|
Higher Secondary
|
307
|
39.1
|
College
|
198
|
25.2
|
Marital status
|
Married
|
688
|
87.8
|
Unmarried
|
96
|
12.2
|
Type of family
|
Nuclear
|
170
|
21.7
|
Joint family
|
614
|
78.3
|
Farming experience (in years)
|
Up to 10
|
80
|
10.2
|
11-15
|
351
|
44.8
|
16-20
|
250
|
31.9
|
Above 20
|
103
|
13.1
|
The demography of the agripreneurs as exhibited in Table 2 reveals that out of 784 agripreneurs, 44.6 per cent were in the age group of below 35 years. It indicates that young farmers as agriculture entrepreneurs constitute the major chunk of the study population while the middle-aged and old-aged farmers groups constitute the remaining study population in the study area. Another interesting inference that can be made from this descriptive analysis is that the majority (73.2 per cent) of the agripreneurs are male. This denotes the entry of a noticeable number of women agripreneurs in the study area. 39.1 per cent and 25.2 per cent of the agripreneurs hold higher secondary and college-level education, respectively. Most of the agripreneurs (87.8 per cent) are married and belonged to the joint family system. 44.8 per cent of the respondents have 11-15 years of experience in agripreneurship.
Table 3: Emporographics
Category
|
|
Frequency
(N = 784)
|
%
|
Farm age
(in years)
|
Upto 5
|
237
|
30.2
|
6-10
|
390
|
49.7
|
Above 10
|
158
|
20.1
|
Farm size
(in hectare)
|
Below 1
|
449
|
57.3
|
2-4
|
267
|
34.0
|
Above 4
|
68
|
8.7
|
Annual income (in INR)
|
Below 50,000
|
127
|
16.2
|
50,001-1,00,000
|
371
|
47.3
|
1,00,001-1,50,000
|
191
|
24.4
|
Above 1,50,000
|
95
|
12.1
|
Land ownership
|
Own
|
636
|
81.1
|
Lease
|
148
|
18.9
|
Sources of funds
|
Relatives/ Friends
|
252
|
32.1
|
Banks
|
183
|
23.3
|
Moneylenders
|
350
|
44.6
|
Intercropping
|
Cultivated
|
452
|
57.7
|
Not cultivated
|
332
|
42.3
|
The emporographics as presented in Table 3 divulges that 49.7 per cent of the farmers run 6-10 years old enterprise. The majority, 57.3 per cent of the respondents cultivated crops in less than 1 hectare. 47.3 per cent of the agripreneurs earned
50,001-1,00,000 per annum from cultivation. The majority of the agripreneurs cultivated on their own land (81.1 per cent). Most of the agripreneurs (44.6 per cent) have availed of loans for cultivation purposes, while 32.1 per cent of farmers have borrowed money without interest from relatives and friends. 57.7 per cent of the agripreneurs have cultivated multiple crops in their farmland along with the main crop.
Results of Exploratory Factor Analysis for Satisfaction of Agripreneurs
The result of EFA presented in Table 4. As the value of KMO stood at 0.644, the application of factor analysis was highly appropriate for the variables included in this study (Hair et al., 2010). Bartlett's χ2 value was 23721.048, and it was significant (p=<0.05) at a 5 per cent level. It is noted that a high level of inter-relationship was found among the scale variables. So, these variables were adequate for the PCA. The factor analysis by PCA with varimax rotation identified seven Eigenvalues, which were greater than 1. These seven extracted factors explained 78.77 per cent of the total variation depicting the presence of seven factors that have predominantly influence the satisfaction of agripreneurs.
Table 4: Results of Exploratory Factor Analysis
Constructs / Factors
|
Factor Loadings
|
Eigen Value
|
% of
Variance
Explained
|
Factor - 1: Materials Availability
|
|
4.698
|
16.202
|
MA-1
|
.824
|
|
|
MA-2
|
.821
|
|
|
MA-3
|
.745
|
|
|
MA-4
|
.740
|
|
|
MA-5
|
.714
|
|
|
MA-6
|
.696
|
|
|
MA-7
|
.651
|
|
|
Factor - 2: Government Support
|
|
4.027
|
13.885
|
GS-1
|
.822
|
|
|
GS-2
|
.787
|
|
|
GS-3
|
.783
|
|
|
GS-4
|
.694
|
|
|
GS-5
|
.598
|
|
|
Factor - 3: Farm Growth
|
|
3.283
|
11.319
|
FG-1
|
.850
|
|
|
FG-2
|
.791
|
|
|
FG-3
|
.715
|
|
|
FG-4
|
.559
|
|
|
Factor - 4: Farm Income
|
|
3.214
|
11.084
|
FI-1
|
.829
|
|
|
FI-2
|
.717
|
|
|
FI-3
|
.709
|
|
|
Factor - 5: Market Performance
|
|
3.131
|
10.797
|
MP-1
|
.875
|
|
|
MP-2
|
.700
|
|
|
MP-3
|
.693
|
|
|
MP-4
|
.677
|
|
|
Factor - 6: Cultivation and Production
|
|
2.538
|
8.752
|
CP-1
|
.803
|
|
|
CP-2
|
.653
|
|
|
CP-3
|
.646
|
|
|
Factor - 7: Perceived Farm Image
|
|
1.954
|
6.739
|
PFI-1
|
.773
|
|
|
PFI-2
|
.593
|
|
|
PFI-3
|
.589
|
|
|
The first set of variable loading has seven variables with 16.20 per cent of the variance. This component was suitably named as ‘Materials Availability’. Thus, these variables have highly influenced the satisfaction of agripreneurs. The second component consists of five variables with 11.31 per cent of the variance. These variables were aptly named as ‘Government Support’. The third set of variable loading has four variables with 11.31 per cent of the variance. This component was appropriately named as ‘Farm Growth’. The fourth variable loading consists of three variables with 11.08 per cent of the variance. Hence, this factor was named as ‘Farm Income’. The fifth variable loading includes four variables with 10.79 per cent of the variance. Hence, this factor was called as ‘Market Performance’. The sixth variable loading comprises three variables with 8.75 per cent of the variance. Hence, this factor was noted as ‘Cultivation and Production’. The seventh variable loading comprises three variables with 6.73 per cent of the variance. Hence, this factor was suitably named as ‘Perceived Farm Image’. In a nutshell, the material availability, government support, farm growth, farm income, market performance, cultivation and production and perceived farm image were highly influenced in the agripreneurs’ satisfaction (Figure 2).
Results of Confirmatory Factor Analysis (CFA) and Discriminant Validity
Table 5 exhibits the CFA and measurement properties. The composite reliability (C.R.) value for each factor ranged between 0.758-0.875, clearly surpasses the minimum threshold of 0.70 (Hair et al., 2010) and obviously, indicates good internal consistency among all the constructs. The goodness of fit indices for CFA yielded an acceptable level of fit (GFI=0.906; CFI=0.912; TLI=0.901; ꭓ2/df =3.16). The value of the RMSEA was 0.049, which denotes that the model fit was good as RMSEA value less than 0.08 is the gold standard for a strong fit of the model (Browne and Cudeck 1993). This goodness of fit index for the seven-factor model shows the confirmation of construct distinctiveness for materials availability, government support, farm growth, farm income, market performance, cultivation and production and perceived farm image.
Moreover, we checked for discriminant validity by comparing the variance-extracted estimates of the measures with the squared correlation between constructs, as described by Fornell and Larcker (1981) and Netemeyer et al. (1990). The average variance-extracted (AVE) for all variables in this investigation was more than the suggested value of 0.50 (Malhotra and Dash, 2011). Since the value of variance extracted was more than the squared correlation, the measures have discriminant validity. In this study, the estimates of variance extracted for cultivation and production and perceived farm image were 0.563 and 0.546, respectively, and both variables were more than the squared correlation between them. Likewise, the squared correlation between government support and farm growth was more than the variance extracted. Based on these statistics along with the CFA results, the study establishes discriminant validity between these seven variables.
Table 5: Results of Confirmatory Factor Analysis
Constructs
|
C.R.
|
Standardized Loadings
(λyi)
|
Reliability
(λ2yi)
|
Variance
(Var(εi))
|
AVE
Σ(λ2yi)/[(λ2yi)
+/(Var(εi))]
|
Materials Availability
|
.875
|
|
|
|
0.503
|
MA-1
|
|
0.806
|
.650
|
.350
|
|
MA-2
|
|
0.772
|
.596
|
.404
|
|
MA-3
|
|
0.673
|
.453
|
.547
|
|
MA-4
|
|
0.735
|
.540
|
.460
|
|
MA-5
|
|
0.742
|
.551
|
.449
|
|
MA-6
|
|
0.636
|
.404
|
.596
|
|
MA-7
|
|
0.569
|
.324
|
.676
|
|
Government Support
|
.869
|
|
|
|
0.574
|
GS-1
|
|
0.901
|
.812
|
.188
|
|
GS-2
|
|
0.638
|
.407
|
.593
|
|
GS-3
|
|
0.806
|
.650
|
.350
|
|
GS-4
|
|
0.704
|
.496
|
.504
|
|
GS-5
|
|
0.713
|
.508
|
.492
|
|
Farm Growth
|
.836
|
|
|
|
0.568
|
FG-1
|
|
0.626
|
.392
|
.608
|
|
FG-2
|
|
0.869
|
.755
|
.245
|
|
FG-3
|
|
0.888
|
.789
|
.211
|
|
FG-4
|
|
0.580
|
.336
|
.664
|
|
Farm Income
|
.758
|
|
|
|
0.516
|
FI-1
|
|
0.613
|
.376
|
.624
|
|
FI-2
|
|
0.679
|
.461
|
.539
|
|
FI-3
|
|
0.843
|
.711
|
.289
|
|
Market Performance
|
.806
|
|
|
|
0.512
|
MP-1
|
|
0.728
|
.530
|
.470
|
|
MP-2
|
|
0.778
|
.605
|
.395
|
|
MP-3
|
|
0.731
|
.534
|
.466
|
|
MP-4
|
|
0.614
|
.377
|
.623
|
|
Cultivation and Production
|
.790
|
|
|
|
0.563
|
CP-1
|
|
0.858
|
.736
|
.264
|
|
CP-2
|
|
0.787
|
.619
|
.381
|
|
CP-3
|
|
0.578
|
.334
|
.666
|
|
Perceived Farm Image
|
.782
|
|
|
|
0.546
|
PFI-1
|
|
0.640
|
.410
|
.590
|
|
PFI-2
|
|
0.754
|
.569
|
.431
|
|
PFI-3
|
|
0.813
|
.661
|
.339
|
|
Table 6: Descriptives and Correlation
|
x̅
|
σ
|
1
|
2
|
3
|
4
|
5
|
6
|
7
|
1. Materials Availability
|
3.690
|
0.719
|
1
|
|
|
|
|
|
|
2. Government Support
|
3.585
|
0.960
|
.025
|
1
|
|
|
|
|
|
3. Farm Growth
|
4.014
|
0.702
|
.047
|
.594**
|
1
|
|
|
|
|
4. Farm Income
|
3.844
|
0.724
|
.078*
|
.389**
|
.379**
|
1
|
|
|
|
5. Market Performance
|
4.056
|
0.551
|
.257**
|
.429**
|
.314**
|
.283**
|
1
|
|
|
6. Cultivation and Production
|
3.319
|
0.781
|
.473**
|
.273**
|
.015
|
.060
|
.108**
|
1
|
|
7. Perceived Farm Image
|
3.916
|
0.545
|
.385**
|
.286**
|
.167**
|
.300**
|
.043
|
.649**
|
1
|
Table 6 reports mean (x̅), standard deviation (σ) and correlation. The mean values indicate that agripreneurs were highly satisfied with the market performance followed by farm growth, perceived farm image, farm income, materials availability, government support and cultivation and production. The study observed a strong positive correlation (0.594) between farm growth and government support, a moderate positive correlation (0.473) between materials availability and cultivation and production and a weak positive correlation (0.385) between materials availability and perceived farm image. Furthermore, government support was significantly related to farm growth, farm income, market performance, cultivation and production and perceived farm image.
Effect of Demographic and Emporographics on Agripreneurs’ Satisfaction
Multiple regression was employed to find out the effect of independent variables namely, demographic factors and emporographics on the dependent variable namely, satisfaction of agripreneurs.
Table 7: Regression Model Summary
Variables
|
Constructs
|
β
|
‘t’
|
P.
|
Model Summary
|
R
|
R2
|
Adj.R2
|
F
|
P
|
Demographics
|
Constant
|
18.365
|
27.829
|
.000
|
.687
|
.442
|
.427
|
18.692
|
.000
|
Age
|
-.328
|
-1.702
|
.000
|
Gender
|
.017
|
.275
|
.123
|
Education level
|
.160
|
1.163
|
.001
|
Marital status
|
.041
|
.634
|
.153
|
Type of family
|
.025
|
.415
|
.421
|
Farming experience
|
.371
|
4.447
|
.000
|
Emporographics
|
Constant
|
19.147
|
29.487
|
.000
|
.704
|
.496
|
.462
|
24.071
|
.000
|
Farm age
|
.532
|
1.053
|
.000
|
Farm size
|
.419
|
2.285
|
.003
|
Annual income
|
.241
|
2.360
|
.000
|
Land ownership
|
-.128
|
-.756
|
.002
|
Sources of funds
|
-.122
|
-.523
|
.004
|
Intercropping
|
.438
|
1.576
|
.000
|
The effect of demographics and emporographics on the agripreneurs’ satisfaction as presented in Table 7 reveals that the satisfaction of agripreneurs was substantially influenced by the demographic characteristics included in the model since Adj.R2 value stood at 0.427. It denotes that 42.7per cent of the difference in the agripreneurs’ satisfaction was influenced by the set of demographic variables, which confirms that the proposed model was a strong predictor. There was a strong association between agripreneurs’ satisfaction and demographic characteristics since the ‘R’ value was 0.687. The F statistic (18.692) was significant at a 5 per cent level (p≤.05) points out that the overall model fit was significant.
The regression coefficient shows that the demographic characteristics of agripreneurs such as age (β=-.328; t=.-1.702, p ≤ 0.05), education level (β=.160; t=1.163, p ≤ 0.05) and farming experience (β=.371; t=4.447; p ≤ 0.05) substantially influence the agripreneurs’ satisfaction. It could be ascertained that that age has a substantial negative influence on agripreneurs’ satisfaction. Further, gender, marital status and type of family did not significantly (p > 0.05) influence the agripreneurs’ satisfaction. Therefore, the study hypotheses H1a, H1c, and H1f are proven correct, whereas the study failed to prove the remaining hypothesis such as H1b, H1d, and H1e.
The satisfaction of agripreneurs was substantially influenced by the emporographics included in the model since Adj.R2 value stood at 0.462. It implied that 46.2 per cent of the difference in the agripreneurs’ satisfaction was determined by the emporographic variables, which hint at the strong predictable nature of the proposed model. There was a strong association between agripreneurs’ satisfaction and emporographics since the ‘R’ value was 0.704. The F statistic (24.071) was significant at a 5per cent level (p≤.05). So, the overall model fit was significant.
The emporographics factors such as farm age (β=.532; t=1.053, p ≤ 0.05), farm size (β=.419; t=2.285, p ≤ 0.05), annual income (β=.241; t=2.360, p ≤ 0.05), land ownership (β=-.128; t=-.756, p ≤ 0.05), sources of funds (β=.122; t=-.523, p ≤ 0.05) and intercropping (β=.438; t=1.576, p ≤ 0.05) were significantly related to the agripreneurs’ satisfaction. This study observes that land ownership had a negative effect on the agripreneurs’ satisfaction. Therefore, the hypotheses H2a, H2b, H2c, H2d, H2e, and H2f are proved correct (Figure 3).