Positive sentiments
High MPS and top ranks:
Statements with high MPS (Positive sentiments)
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Statement 2: “KCC is a potential tool for providing information to farmers.”
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Statement 4: “KCC provides first-hand information about queries.”
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Statement 9: “Kisan Call Centre helps in building up self-reliance of farmers.”
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Statement 10: “The services of Kisan Call Centre are very useful for illiterate farmers.”
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Statement 7: “I strongly feel that the advices given by Kisan Call Centre are applicable to my farm also.”
Interpretation
• These statements received high MPS, indicating that farmers view KCC positively in terms of its ability to provide information, assist with decision-making and support farmers, including illiterate farmers.
• The fact that farmers find the advice applicable to their farms also suggests a strong sense of relevance in the services provided.
• Additionally, the perception that KCC helps in building self-reliance reflects that farmers see it as a tool that empowers them to make informed decisions, increasing their autonomy and confidence in their agricultural activities.
Areas of concern
Low MPS and lower ranks (Negative perceptions)
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Statements with Low MPS (Negative Sentiments)
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Statement 14: “KCC can’t meet the location-specific needs of farmers.”
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Statement 15: “I would not advise my friends to contact`Kisan Call Centre for seeking information.”
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Statement 19: “Calling at Kisan Call Centre is a wastageof time.”
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Statement 18: “Kisan Call Centre services are only for progressive and big farmers.”
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Statement 17: “KCC don’t change farmers’ own decision- making.”
Interpretation
• These statements received low MPS values, which reflect negative perceptions of KCC. Farmers may feel that the service is not tailored to their specific needs,especially for those in remote or geographically unique areas (Statement 14).
• The advice is seen as ineffective or not worth the time for some farmers (Statements 15 and 19), leading to a general disinterest in recommending the service.
• Additionally, the perception that KCC is more beneficial for progressive or large-scale farmers (Statement 18) could indicate a feeling of exclusion among smaller, traditional,or resource-limited farmers.
• Non-impact on decision-making (Statement 17) suggests that for some farmers, KCC may not significantly influence their choices, which could be due to a lack of personalization or inapplicability to certain farming contexts.
Neutral or mixed sentiments
Statements with moderate MPS
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Statement 3: “Kisan Call Centre system is aninnovative source to get agricultural information.”
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Statement 6: “Expert advice makes the farmers’ enterprise/ activities productive.”
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Statement 8: “Women can also use the service of KCC effortlessly.”
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Statement 13: “Existing infrastructure of KCC is not enough to meet the needs of the farming community.”
Interpretation
• These statements show neutral or mixed responses,where moderate MPS suggests that farmers have an ambivalent or balanced view on certain aspects of KCC.
• Innovation and expert advice were rated positively, but perhaps not as strongly as other statements (Statement 3 and Statement 6), indicating that while farmers see KCC as a valuable tool, they may still have reservations about its practical implementation or real-world effective-ness in all cases.
• Women’s access to KCC services (Statement 8) received a moderate MPS, suggesting that while some farmers see KCC as accessible to women, there could be challenges in terms of cultural or logistical barriers.
• The infrastructure (Statement 13) of KCC was seen as insufficient by some farmers, indicating that the system may need upgrades to better support the diverse needs of the farming community.
Suggestions for improvement
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Location-specific support
Given the concern over location- specific needs (Statement 14), KCC could consider introducing more region-specific services or offering tailored advice based on local farming conditions, climates and crop types.
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Better personalization
The fact that some farmers feel KCC doesn’t change their decision-making (Statement 17) implies that personalized advice could enhance its impact. KCC could improve by offering more customized consultations based on each farmer’s individual situation.
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Addressing perceived inefficiency
Low MPS for statements about wasting time (Statements 15 and 19) suggests that improving the efficiency of communication and making the services faster and more user-friendly could increase satisfaction.
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Inclusivity for all farmers
KCC may need to work on its image as being for only progressive or large-scale farmers (Statement 18). Ensuring that the service is accessible and beneficial to small- scale and marginal farmers is crucial for its success in the farming community as a whole.
Conclusion
• The overall attitude of farmers toward the Kisan Call Centre is mixed, with positive perceptions regarding its ability to provide information, build self-reliance and help illiterate farmers. However, concerns about its lack of personalization, location-specific solutions and perceived inefficiency highlight areas that need improvement. According to the data presented in Table 1, 50.00 percent of respondents had a moderate attitude towards the use of Kisan Call Centre, followed by 20.67 per cent who had an unfavorable attitude, 15.33 per cent who had a favorable attitude, 12.67 per cent who had a strongly favorable attitude and only 1.33 per cent who had a strongly unfavorable attitude. Furthermore, was revealed that the majority of respondents (50.00%) had a somewhat favorable attitude towards the utilisation of Kisan Call Centre.
The findings are similar to the findings reported by
Sharma et al. (2012);
Verma et al. (2012);
Arora and Rathore (2013).
Relationship between the profile of the respondents with their attitude towards the use of kisan call center
The data relevant to the association between the profile of the respondents and their attitude towards the usage of Kisan Call Centre are reported in Table 2 .
Table 3 presents the correlation coefficients (rrr) between several independent variables and a dependent variable. The values of the correlation coefficient range from -1 to +1, indicating the strength and direction of the relationship between the variables. A positive value suggests a positive relationship, while a negative value suggests an inverse relationship. The significance of these correlations is indicated by the asterisks (*, **), with the following interpretations:
•
(0.01 significance level) – denoted by **
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(0.05 significance level) – denoted by *
Detailed interpretation
*Age (r = 0.0179)**
• The correlation coefficient for age is very low (0.0179), indicating a very weak positive correlation with thedependent variable. The asterisk suggests that this relationship is statistically significant at the 0.05 level, but the weak strength of the correlation implies that age has a negligible effect on the dependent variable.
Education (r = 0.0434)**
• Education shows a similarly weak positive correlation (0.0434) with the dependent variable. It is statistically significant at the 0.05 level. This indicates that higher levels of education may slightly increase the dependent variable, although the effect is minimal.
*Family size (r = 0.0400)**
• Family size has a small positive correlation (0.0400), indicating a weak relationship with the dependent variable. Like age and education, this correlation is statistically significant at the 0.05 level, but its practical significance remains limited.
Landholding (r = -0.0729)
• The negative correlation coefficient (-0.0729) for landholding suggests a very weak inverse relationship with the dependent variable. However, there is no statistical significance (no asterisk), implying that this relationship is not strongenough to be considered meaningful.
Occupation (r = -0.0367):
• Occupation also shows a weak negative correlation (-0.0367), suggesting a slight inverse relationship with the dependent variable. The absence of statistical significance indicates that occupation does not have a meaningful impact on the dependent variable.
Annual income (r = -0.2807)**
• A significantly stronger negative correlation is observed for annual income (-0.2807), which is statistically significant at the 0.01 level. This suggests that as annual income increases, the dependent variable tends to decrease. The relationship is moderate in strength and highly significant.
Innovativeness (r = -0.0637)
• Innovativeness shows a weak negative correlation (-0.0637) with the dependent variable. This relationship is not statistically significant, implying that innovativeness does not have a significant impact on the dependent variable.
Extension contact (r = -0.0157)
• Extension contact has a very weak negative correlation (-0.0157), indicating an almost negligible relationship with the dependent variable. This correlation is not significant, suggesting that extension contact does not significantly affect the dependent variable.
Social participation (r = -0.0569)
• Social participation also demonstrates a weak negative correlation (-0.0569) with the dependent variable, with no statistical significance. This indicates that social participation has a very minor and statistically insignificant influence on the dependent variable.
*Source of information (r = 0.1230)**
• The source of information has a weak positive correlation (0.1230), indicating a slight relationship with the dependent variable. The asterisk suggests that this correlation is statistically significant at the 0.05 level, though it remains weak in practical terms.
*Scientific orientation (r = 0.0272)**
• Scientific orientation shows a very weak positive correlation (0.0272) with the dependent variable. Despite the low strength of the correlation, it is statistically significant at the 0.05 level.
Risk orientation (r = -0.0181)
• Risk orientation has an extremely weak negative correlation (-0.0181) with the dependent variable, which is not statistically significant. This suggests that risk orientation has an almost negligible effect on the dependent variable.
Summary
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Significant correlations
Annual income (r=-0.2807** r =-0.2807**r=”0.2807**), Age (r=0.0179*r=0.0179*r=0.0179*) Education(r=0.0434*r = 0.0434*r=0.0434*), Family size (r=0.0400*r =0.0400* r=0.0400*), Source of information (r=0.1230*r =0.1230*r =0.1230*) and Scientific orientation (r=0.0272*r =0.0272*r=0.0272*) all show statistically significant correlations with the dependent variable.
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Negative correlations
Annual income stands out with a moderate negative correlation, while other variables like landholding, occupation, innovativeness, extension contact, social participation and risk orientation show weak negative or negligible relationships.
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Weak relationships
Most of the correlations are weak and even those that are statistically significant (except for annual income) do not suggest strong practical relevance.The results are somewhat comparable to those published by
Sharnagat (2008);
Goswami (2012);
Lal (2012) and
Sharma et al. (2012).
