Farmers’ Profitability through Online Sales of Organic Vegetables and Fruits during the COVID-19 Pandemic—An Empirical Study

Abstract

The purpose of this research is to empirically identify the association of the IT literacy of farmers and their profitability through online sales of fresh organic fruits and vegetables during the COVID-19 pandemic. The methodology used in the research is quantitative in nature. A closed-end questionnaire has been used as a survey tool to collect data from Indian farmers. The farmers’ IT literacy and attitudes towards organic farming are independent variables, and the frequency of sales of products through online stores and farmers’ profitability are the dependent variables. The researcher has attempted to study how the outbreak of COVID-19 has moderated the relationship between the independent and dependent variables. The sample size is 271 farmers who sell their products through online stores. The findings reveal that the farmers’ attitude towards organic farming and their IT literacy correlate with the profitability of online sales. Further, the outbreak of the COVID-19 pandemic has moderated the relationship between both the dependent and independent variables. This research will help the government in formulating policies for facilitating farmers to sell their produce through online modes. The study could be further extended by collecting data from farmers from different Asian countries and comparing the results with those of the present research findings.

1. Introduction

During the COVID-19 pandemic, many countries faced food shortage issues. The farmers were not able to transport their produce, and retailers failed to acquire produce to sell at physical stores. To reach the consumers, many farmers globally adopted e-commerce and then, yet again, failed to earn profit due to a lack of IT and computer literacy, leading to loss and an unwillingness to pay through insecure transactional ports [1]. Farming and agricultural products are generally considered as perishable goods that require a fast-selling strategy through safer payment modes. Post COVID-19, businesses had to change their operations and strategic management to earn profit [2].

Information and communication technology in e-commerce plays a fundamental and significant role, and farmers, especially rural farmers who lack IT literacy, will eventually struggle to earn through e-commerce since the middleman fills the gap and gains the original profit that should have been the farmers’ [3]. The essentiality and importance of IT literacy among farmers decides their online sales, profit, turnovers, and consumer frequency. Henceforth, by gaining IT literacy and knowledge, the urban-based farmers and rural farm producers could increase their profits through digitization [4]. Post COVID-19, the necessity of IT literacy in agricultural farming has been relevant for organic farmers since they invest huge capital and earn less profits. Thus, the attitudes of the farmers decide their profitability and consumer flow [5].

The Indian farmers during COVID-19 managed to adapt to the transition from traditional marketing to social and digital marketing via company websites, social marketing, advertisements, and more. During COVID-19, small farmers, who always relied on end-to-end sales or one-to-one sales, had to adopt mobile-application-based sales and online sales to earn income and mere profit [6]. Henceforth, the Indian government introduced the e-NAM (Electronic-National Agriculture Market) to digitize the agricultural sector. Along with the Indian government, startups, such as FarmBee, KVSMT, Vithai, eFarming, and so on, helped the Indian farmers in selling their crops online. Among these, the top application recognized as user-friendly by the farmers in Tamil Nadu is “Vithai”. Other than the above-mentioned applications, there are other applications, such as Kisan Suvidha, IFFCO Kisan agriculture, Pusa Krishi, AgriApp, Krishi Gyan, and more, that also assist the farmers with information and farming guidelines through which they can gain insight and knowledge [7].

During and post COVID-19, people in India have changed their consumption pattern [8], where consciousness around healthier diets and organic food, especially farm produce and own produce without pesticides and other harmful chemicals, has increased. Post COVID-19, the consumption of organic foods increased by 1.35–1.70 metric tons from 2019 to 2020, and a 26% increase in organic food consumption was recognized in India at the end of 2021 [9]. Hence, in this research, the focus is particularly on organic foods in the Indian market through online sales, and thus non-organic foods are not examined.

Though COVID-19 has changed the way in which organizations in India operate, it has brought in lot of benefits to e-commerce-based businesses. To date, there is little literature on the impact of COVID-19 on agricultural products, especially through online sales, and researchers are still studying this topic. This current research is the first of its kind to examine the variables of farmers’ IT literacy, attitudes towards organic farming, and the correlation with profitability through online sales. The research aims to identify how the frequency of sales through online stores is affected by these factors.

2. Material and Methods

2.1. Literature Review

The author of [10] studied e-commerce models and how organic agricultural products are sold through online stores with transparency. The study was conducted in China-based online stores that sell vegetables and fruits. The author of ] [11] argued that agro-products-based e-commerce in China needs improvement, since online purchasers rely on feel and touch in traditional purchases (physical shopping), unlike many developed countries such as the US, Japan, and countries in Europe. Purchases and sales in the modern/digitized era adopt e-commerce as their platform to increase profits; here, the user, i.e., the farmer, should possess IT literacy.

Similarly, authors in [12] studied Romania-based farmers’ sales in agro-products (fruits and vegetables) through e-commerce and the correlation between purchasing decisions and the selling platform during the COVID-19 pandemic. They observed that people hesitated to buy perishable goods through online portals and websites selling vegetables and fruits and preferred to purchase directly from the local farm product “Producers”. However, according to [13], COVID-19 influenced the decisions of the purchasers/shoppers to switch from traditional purchasing to online shopping where they were forced to purchase fruits and vegetables based on availability and pricing. Contrarily, those who lacked IT literacy suffered a lot and still preferred local products over online sales, which resulted in lower profits for online sellers and thus reduced the frequency of online shopping, as argued in [14]. Thus, IT literacy impacts users at both ends (seller and purchaser).

Thus, Hypotheses 1 and 2 are formulated as follows:

H1: 

There is a correlation between farmers’ IT literacy and farmers’ profitability through online sales.

H2: 

There is a correlation between farmers’ IT literacy and frequency of sales online stores.

The authors [15] studied Washington’s farming industry and market sales and found that, post COVID-19, physical markets earned lower margins compared to margins of online markets and stores. The study made a comparison of sales between the years 2019 and 2020.The study found that online sales had increased in 2020 when compared with that of 2019, as a result of the COVID-19 pandemic. However, the study was not able to conclude that the farmers’ were profitable because they had to make a lot of additional investment in order to equip themselves in following the COVID-19 protocols like social distancing, face masks, etc., in order to perform sales.

Authors [16] studied China’s agricultural produces and how consumers chose their mode of shopping produces. They gathered data and analyzed it to find out that the government aided the poor farmers who earned lesser than 1 Lakh/year through government ecommerce platforms, but the farmers failed to retain their profit since many farmers lacked IT literacy. It was found that they even failed to manage transactions and transportations. Thus, according to author [17], IT literacy heightens profitability through online sales and online marketing.

Authors [18] studied Vietnam-based consumers’ behavior toward online shopping. They found that the farmers’ attitudes toward consumers impact the purchase decision through online sales. According to the author of [19], many consumers preferred online sales from local producers through affection, familiarity, security, safety regulations, payment issues (transaction failure and delivery issues), and willingness and awareness about product availability and shops’ attitude. Also, local farmers focused on producing agro-products through organic farming practices and developing an understanding of the demand-and-supply of those products to increase their profits.

Studies [20,21,22] insisted that the adoption of an e-commerce platform increased the profit of farmers especially when the local consumers were willing to pay higher prices in order to meet their demands. By meeting the demands of consumers through adequate supply of produce, especially through organic farming, farmers have gained huge profitability in online sales [23] However the impact of IT literacy also was found to affect the profit. Thus, it is clear that IT literacy is essential. Moreover, how the farmers dealt with consumers (attitude) during the pandemic without compromising quality also was found to impact the online sales. Thus, the attitude of farmers is related to profit and online sales frequency.

Thus, Hypotheses 3 and 4 were formulated as:

H3: 

There is a correlation between farmers’ attitude towards organic farming and farmers’ profitability through online sales.

H4: 

There is a correlation between farmers’ attitude towards organic farming and frequency of sales through online stores.

The study [24] examined the organic produce in agricultural farming, and study [25] evaluated the impact caused by COVID-19 on the farmers and their produce. The studies revealed that organic produces were preferred by online purchasers whereas the lack of organic produces in online stores, less IT literacy of farmers, and farmers’ attitude toward organic farming caused consumers to shift from online sales and to shop from local producers, resulting in less profit. Though the factors seem independent, they were in fact related. The study [26] insisted the impact of COVID-19 on farming and agro-products increased profits for farmers especially when the products were organic produce. The farmers with better IT literacy and knowledge of financial management sustained better than traditional farmers and earned more turnover during and post pandemic.

Thus Hypothesis 5 is formulated as follows:

H5: 

The relationship between farmers’ IT literacy and farmers’ profitability through online sales is moderated by the outbreak of the COVID-19 pandemic.

2.2. Conceptual Framework and Hypothesis

The research takes into consideration farmers’ IT literacy and farmers’ attitude toward organic farming as independent variables. The frequency of sales through online stores and the profitability of farmers through online sales are considered as dependent variables. The moderating factor is the variable of the outbreak of COVID-19 (refer Figure 1).

After the outbreak of the COVID-19 pandemic, many organizations and companies transformed their leadership and operational strategies in order to meet the market demand through online sales. Therefore, the outbreak of the COVID-19 pandemic has been added as a moderating factor. The following hypotheses have been formulated taking into consideration the outbreak of the COVID-19 pandemic as a moderator:

3. Methodology

Research generally applies different systematic approaches, methodologies, and tools for collecting data and analyzing data.

This study adopts a positivism-paradigm since the researcher aims to reduce facts and truths from the acquired information. Since the research is toward examining and evaluating the relationship between the participants’ (in this case the farmers) IT literacy, attitude, their profitability through online sales, and their consumers’ purchase frequency through online stores, it falls-under the quantitative strategy. Additionally, the research is said to have adopted a deductive approach. Since the research describes, through systematic data collection, farmers’ attitude towards organic farming, IT literacy level, their profits through online sales, and their consumers frequency of online purchases, the design is ‘descriptive design’.

3.1. Instrumentation

The instrument designing and development include several criteria. In the process of instrument design, the major points upon which to be focused are the usage of scale, adoption of existing tools as per study’s necessity and purpose, validation of items, and variables/factors. In this research the instrument developed is to be rated using a 5-point Likert scale. The tool is divided into six sections, where the first section deals with demographic data (age, gender, education, annual income, and name); the second section includes the “frequency of online sales” (FOS) as single-item through which the consumers’ frequency of purchasing/ordering through online mode would be measured; the third section includes “outbreak of COVID” (OutCov) that is measured through 4 items; section four includes “IT literacy” (ITL) that is measured with 4 items; section five consists of “farmers’ attitude” (AT) with 4 items; and finally the sixth section “profitability through online sales” (PR) has 6 items. Thus, the customized scale includes a total of 19 items.

The items adopted are:

(1)

FOS with 1-item from [27];

(2)

PR with 6-items from [28];

(3)

AT with 4-items from [28]);

(4)

ITL with 4-items from [29];

(5)

OutCov with 4-items.

A 5-point Likert scale is adopted throughout, and the instrument is designed as closed-ended, in order to measure the responses accurately. From author [28] developed a scale of 46 items. However, the current research requires only 10 items that are relevant to the research objectives. Similarly, 1 item has been used for FOS to measure the ‘online purchasing-decisions’. In addition to that, the 6-item scale developed by [27] and 4 items for ITL from the 17-item scale developed by [29] have also been employed in this research. The items for COVID-19-related measures were designed by the researcher for the study’s purposes, in order to measure the moderating effect of the proposed variable on the dependent and independent variables.

The authors [30] studied digitization of farmers (IT and computer literacy) by adopting items from [29] and observed that outcomes were consistent throughout. The study by author [31] examined the consumer behavior (FOS) by adopting items from [27] and noted that the outcomes were reliable. Similarly, the authors [32] examined the farmers’ attitude (AT) and behavior post COVID-19 by adopting items from [28] and also the authors [33] adopted PR items from [28] and gained consistency in outcomes. Thus, the research instrument for the current research has been designed and customized for data collection, based on the above studies.

3.2. Reliability Statistics of the Research Instrument

Prior to analyzing the collected data, the reliability of scale through ‘reliability’ test using Cronbach’s alpha has been carried out, in order to ensure consistency in the outcomes.

Table 1. Reliability scale testing.

S. No	Construct	Item	Alpha
1	Outbreak of COVID-19 (OutCov)	4	0.846
2	IT literacy of farmers (ITL)	4	0.797
3	Farmer’s profitability through online sales (PR)	6	0.922
4	Attitude towards organic farming (AT)	4	0.846
5	Frequency of sales through online (FOS)	1	0.916

shows items in scale developed and its Cronbach’s alpha score:

The value of Cronbach’s alpha for the items/scales in the developed tool is greater than 0.7 inferring that developed instrument is reliable. The values in the Table 1 prove that the items of the developed scale are valid and reliable.

3.3. Data Collection

Primary data collection has been done using the research instrument designed for this study. The participants are limited to Tamil Nadu (South India). The respondents were farmers who were engaged in the activity of cultivation of crops, fruits, vegetables, spices, and other agricultural based products. The respondents were chosen from various districts of Tamilnadu such as Villupuram, Tanjore, Coimbatore, Erode, Dindigul, Salem, and Madurai, since major organic farmers were found to hail from these regions. Though Kerala also produces and cultivates organic products, our study is restricted to Tamil Nadu, and thus other states or districts were not included. The timeline of the study was from January 2021 to April 2021. The organic and non-organic farmers were filtered from the survey, since they were classified based on their produce. Thus, the organic food producers alone were retained for primary data collection through the survey. Relevant secondary resources (research, studies, literatures, articles, e-journals, etc.) were also utilized in the study.

3.4. Target Respondents

The research exclusively focuses on farmers’ IT literacy, attitude towards organic farming, their profit through online sales, and frequency of consumers through online stores. Henceforth, the target respondents were just limited to farmers who produce organic products.

3.5. Sampling Plan and Size

From the desired targets, the sample-unit has been reduced as farmers from ‘Tamil Nadu’ with sampling design as ‘simple-random sampling’ and sample size as n = 271. The sample size is estimated through unknown population estimation using the formula by [34]. The Cochran’s formula utilized to estimate sample is:

$$Sam=\widehat{popn}\pm z\times \sqrt{\frac{popn(1-popn)}{Sam}}$$

(1)

where $\widehat{popn}$ = the proportioned population; Sam = the sample-size; and z = the z-score.

The survey was conducted through an online mode where 300 invitations (via Google form) were sent to the online farmers. Survey questionnaires were sent online, and the respondents filled them through a remote mode. After the elimination of incomplete surveys [22] and irrelevant responses [7], the remaining and complete number of responses were seggregated to find the final sample size. The sample size was thus estimated as n = 271, and the response rate was found to be 90.33%, which indicated an excellent response rate.

Indicators of the research: The indicators used in this research are the outbreak of COVID-19 (OutCov) as a moderating factor, IT literacy of farmers (ITL) and attitude toward organic farming (AT) as input factors, and Farmer’s profitability through online sales (PR) and Frequency of sales through online (FOS) as output factors.

3.6. Analysis Tools and Software

SPSS software has been used to analyze the data collected. ANOVA, Regression analysis, Simple percentage analysis, and Hayes PROCESS Macro have been employed in order to test the hypothesis.

4. Data Analysis

4.1. Descriptive Statistics

From

Table 2. Descriptive statistical data.

	N	Mean	Median	Mode	Std. Deviation	Skewness
	Valid	Missing
AGE	271	0	2.5609	3.0000	3.00	0.97894
GENDER	271	0	1.4908	1.0000	1.00	0.50818
HEDUC	271	0	3.2509	3.0000	3.00	1.32907
OutCov1	271	0	3.9705	4.0000	4.00	0.64338
OutCov2	271	0	3.8524	4.0000	4.00	0.67246
OutCov2	271	0	4.0923	4.0000	4.00	0.62270
OutCov2	271	0	3.9004	4.0000	4.00	0.70530
FOS	271	0	3.1144	3.0000	3.00	0.85949
ITL1	271	0	2.8413	3.0000	2.00	0.93130
ITL2	271	0	3.3911	4.0000	4.00	0.88305
ITL3	271	0	2.6790	3.0000	2.00	0.96431
ITL4	271	0	2.8635	3.0000	2.00	1.01096
PR1	271	0	3.4649	4.0000	4.00	0.82421
PR1	271	0	3.4576	4.0000	4.00	0.85041
PR3	271	0	3.5277	4.0000	4.00	0.83786
PR4	271	0	3.7048	4.0000	4.00	0.72107
PR5	271	0	3.3764	3.0000	4.00	0.86412
PR6	271	0	3.6125	4.0000	4.00	0.78956
AT1	271	0	3.9705	4.0000	4.00	0.64338
AT2	271	0	3.8524	4.0000	4.00	0.67246
AT3	271	0	4.0923	4.0000	4.00	0.62270
AT4	271	0	3.9004	4.0000	4.00	0.70530

HEDUC—Highest level of education; FOS—Frequency of Online Sales; PR—Profitability; ITL—Farmer’s IT literacy; AT—Farmers’ attitude; OutCov—Outcome of COVID-19.

, it can be inferred that the items with a Mode score of 4 represented higher central tendency. The standard deviation values of the items HEDUC and ITL were found to be nearer to ‘1’ showing that those values were widely spread unlike the other items FOS, PR, ITL, AT, and OutCov.

4.2. Demographic Statistics

The demographic data collected in this research include age, gender, educational level, and annual income (refer

Table 3. Demographic statistical data.

	Description	Frequency	Percentage
Age (In years)	18–20	45	16.6
	20–30	80	29.5
	30–40	95	35.1
	40 and above	51	18.8
	Total	271	100
Gender	Male	193	71.2
	Female	78	28.8
	Total	271	100
Highest level of education	No schooling/until primary school	43	15.9
	Until Secondary school	70	25.8
	Until higher secondary/diploma	56	20.7
	Undergraduate	64	23.6
	Postgraduate	28	10.3
	Doctorate	10	3.7
	Total	271	100
Annual Turnover (INR)	less than 2 L	27	10.0
	2 L TO 5 L	88	32.5
	5 L TO 10 L	103	38.0
	MORE THAN 10 L	53	19.6
	Total	271	100

).

Age & Gender: A majority of respondents who participated in the research were found to be in the age-group of 30–40 years (35.1%) followed by 20–30 years (29.5%). Male respondents were found to be a majority (71.2%) when compared with that of the female respondents (28.8%).

Educational qualification: It was necessary to identify the educational level of the respondents since it was important to know how familiar they were with computers, in order to meet the objectives of this research. It was found that the highest level of education recorded was secondary schooling (25.8%) followed by undergraduates (23.6%) and diploma holders (20.7%). Contrarily, there were 15.9% respondents who had never attended schooling and 10.3% of respondents who had obtained postgraduate, and just 3.7% were found to have obtained a doctoral degree in agriculture.

Income: The highest turnover (annual) obtained by farmers was estimated to be around 5–10 Lakhs/year (38%) followed by 2–5 Lakhs/year (32.5%). It was recorded that only 19.6% of the farmers earned 10+ Lakhs/year, and there were also 10% farmers who earned less than 2 Lakhs/year.

From these statistics it could be inferred that the people with higher knowledge are lesser in numbers when organic farming is considered, and the same could be assumed toward IT literacy since they are focused toward possessing knowledge in relevant subjects (farming and agriculture) rather than possessing knowledge toward increasing their sales/profitability through online or e-commerce platforms. If the organic farmers and agro producers could increase their IT literacy and knowledge on e-commerce, it can indirectly benefit them, by boosting their profits and turnover especially in the digital era and during situations such as that of the pandemic, especially when there is a huge demand for agro-products [35].

4.3. Hypothesis Testing

4.3.1. IT Literacy versus Farmers’ Profitability

H1: 

There is a correlation between farmers’ IT literacy and farmers’ profitability through online sales.

Model Summary b
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate	Change Statistics
					R Square Change	F Change	df1	df2	Sig. F Change
1	0.631 a	0.610	0.506	0.65404	0.610	33.172	1	269	0.000
a. Predictors: (Constant), ITL
b. Dependent Variable: PR

Through the table of the model summary of the R-value, the Adjusted R² value and R² are obtained. The R-Value for developed model is 0.631; the R² value is 0.610; and the Adjusted R² value is 0.506. It shows that the independent variable concentrates and explains 61.0% of dependent variables’ variability. It is evident that the model’s variables are a good fit for research with a higher R-value than R² value.

ANOVA a
Model		Sum of Squares	Df	Mean Square	F	Sig.
1	Regression	14.190	1	14.190	33.172	0.000 b
	Residual	115.071	269	0.428
	Total	129.261	270
a. Dependent Variable: PR
b. Predictors: (Constant), ITL

From the above ANOVA table, the dependent variable is predicted from significant independent variable to be 33.172, i.e., F(269, 1), and the sig value 0.000 is <0.0005 proving that the regression model is significantly a good fit.

Coefficients a
Model		Un-Standardized Coefficients		Standardized Coefficients	t	Sig.
		B	Std. Error	Beta
1	(Constant)	2.621	0.162		16.216	0.000
	ITL	0.307	0.053	0.331	5.760	0.000
a. Dependent Variable: PR

The formulated hypothesis H1 “There is a correlation between farmers’ IT literacy and farmers’ profitability through online sales” is accepted, and the regression equation from the model’s coefficient estimation, where the farmers’ IT literacy is predicted with profitability through online stores, is estimated as:

ITL = 2.621 + (0.307 * PR)

(2)

4.3.2. IT Literacy versus Frequency through Online Sales

H2: 

There is a correlation between farmers’ IT literacy and frequency of sales online stores.

Model Summary b
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate	Change Statistics
					R Square Change	F Change	df1	df2	Sig. F Change
1	0.632 a	0.601	0.603	0.86065	0.601	0.270	1	269	0.000
a. Predictors: (Constant), MOP
b. Dependent Variable: FPO

The model summary table gives the R-value, Adjusted R² value, and R² value. The R-Value for the developed model is 0.632; the R² value is 0.601; and the Adjusted R² value is 0.603, which states that the independent variable concentrates and explains 60.1% of the dependent variables’ variability. Thus, it can be inferred that the model’s variables are a good fit for research with a higher R-value than R² value.

ANOVA a
Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	0.200	1	0.200	0.270	0.000 b
	Residual	199.254	269	0.741
	Total	199.454	270
a. Dependent Variable: FPO
b. Predictors: (Constant), MOP

From the above ANOVA table, the dependent variable is predicted from significant independent variable to be 0.270, i.e., F(269, 1), and the sig value 0.000 is <0.0005 proving that the regression model is significantly a good fit.

Coefficients a
Model		Un-Standardized Coefficients		Standardized Coefficients	t	Sig.
		B	Std. Error	Beta
1	(Constant)	3.007	0.213		14.136	0.000
	MOP	0.036	0.070	0.032	0.520	0.000
a. Dependent Variable: FPO

The formulated hypothesis H2 “There is a correlation between farmers’ IT literacy and frequency of sales online stores” is accepted, and the regression equation from the model’s coefficient estimation, where the farmers’ IT literacy is predicted with frequency of sales through online stores, is estimated as:

ITL = 3.007 + (0.036 * FOS)

(3)

4.3.3. Farmers’ Attitude versus Profitability

H3: 

There is a correlation between farmers’ attitude towards organic farming and farmers’ profitability through online sales.

Model Summary b
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate	Change Statistics
					R Square Change	F Change	df1	df2	Sig. F Change
1	0.683 a	0.607	0.603	0.69080	0.607	1.875	1	269	0.000
a. Predictors: (Constant), AT
b. Dependent Variable: PR

Through the model summary table, the R-value, Adjusted R² value, and R² values are obtained. The R-Value for developed model is 0.683; the R² value is 0.607; and the Adjusted R² value is 0.603, which states that the independent variable concentrates and explains 60.7% of the dependent variables’ variability. It is understood that the model’s variables are a good fit for research.

ANOVA a
Model		Sum of Squares	Df	Mean Square	F	Sig.
1	Regression	0.895	1	0.895	1.875	0.000 b
	Residual	128.366	269	0.477
	Total	129.261	270
a. Dependent Variable: PR
b. Predictors: (Constant), AT

From the above ANOVA table, the dependent variable is predicted from significant independent variable to be 1.875, i.e., F(269, 1), and the sig value 0.000 is <0.0005 proving that the regression model is rather a good fit.

Coefficients a
Model		Un-Standardized Coefficients		Standardized Coefficients	T	Sig.
		B	Std. Error	Beta
1	(Constant)	3.108	0.307		10.141	0.000
	AT	0.105	0.077	0.083	1.369	0.000
a. Dependent Variable: PR

The formulated hypothesis H3 “There is a correlation between farmers’ attitude towards organic farming and farmers’ profitability through online sales” is accepted, and the regression equation from the model’s coefficient estimation, where the farmers’ attitudes towards organic farming are predicted with profitability through online sales value, is estimated as:

AT = 3.108 + (0.105 * PR)

(4)

4.3.4. Farmer’s Attitude versus Frequency of Online Purchase

H4: 

There is a correlation between farmers’ attitude towards organic farming and frequency of sales through online stores.

Model Summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate	Change Statistics
					R Square Change	F Change	df1	df2	Sig. F Change
1	0.578 a	0.506	0.502	0.85845	0.506	1.653	1	269	0.000
a. Predictors: (Constant), AT

Through the model summary table, it is clear that the R-Value for developed model is 0.578; the R² value is 0.506; and the Adjusted R² value is 0.502. It shows that the independent variable concentrates and explains 50.6% of the dependent variables’ variability. It is understood that the model’s variables are a good fit for research with a higher R-value than R² value.

ANOVA a
Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	1.218	1	1.218	1.653	0.000 b
	Residual	198.235	269	0.737
	Total	199.454	270
a. Dependent Variable: FOS
b. Predictors: (Constant), AT

From the above ANOVA table, the dependent variable is predicted from the significant independent variable to be 1.653, i.e., F(269, 1), and the sig value 0.000 is <0.0005 proving that the regression model is significantly a good fit.

Coefficients a
Model		Un-standardized Coefficients		Standardized Coefficients	T	Sig.
		B	Std. Error	Beta
1	(Constant)	2.629	0.381		6.903	0.000
	AT	0.123	0.095	0.078	1.286	0.000
a. Dependent Variable: FOS

The formulated hypothesis H4 “There is a correlation between farmers’ attitude towards organic farming and frequency of sales through online stores” is accepted, and the regression equation from the model’s coefficient estimation, where the farmers’ attitudes towards organic farming are predicted with the frequency of sales through online stores, is estimated as:

AT = 2.629 + (0.123 * FOS)

(5)

4.3.5. Farmers’ IT Literacy versus Farmers’ Profitability with COVID-19 as Moderator

H5: 

The relationship between farmers’ IT literacy and farmers’ profitability through online sales is moderated by the outbreak of the COVID-19 pandemic.

“Hayes PROCESS Macro” is used for examining the impact of the moderator on the dependent and independent variables. Generally, in order to examine the model’s path analysis, PROCESS Macro is used by researchers. It is used for examining the effect of one or more moderating (mediating) variables on the dependent and independent variables involved in research. The following

Table 4. PROCESS Macro analysis.

Run MATRIX Procedure:
Model: 1
Y: Profitability
X: IT literacy
W: Outbreak of COVID-19
Sample Size: 271
OUTCOME VARIABLE
PR
Model Summary
R	R-sq	MSE	F	df1	df2	p
0.5052	0.4111	0.7387	0.9967	3.0000	267.0000	0.000
Model
	Coeff	Se	t	p	LLCI	ULCI
constant	1.0199	1.4718	0.6929	0.0000	−1.8780	3.9178
MITL	0.5699	0.5007	1.1382	0.0000	−0.4159	1.5557
MOOC	0.5098	0.3695	1.3798	0.0000	−0.2177	1.2372
Int_1	0.1368	0.1247	−1.0969	0.0000	−0.3823	0.1087
Product terms key
Int_1: MOP × MOOC
Test(s) of highest order unconditional interaction(s)
	R2-chng	F	df1	df2	p
X * W	0.4045	1.2033	1.0000	267.0000	0.0000

* Level-of-Confidence for all confidence-intervals in retrieved output: 95.0000.

is the PROCESS Macro table for the developed model.

The Figure 2 above represents how the variable of the outbreak of COVID-19 moderates the variables profitability and IT literacy of the farmers. It can be inferred from Figure 2 that the outbreak of COVID-19 significantly moderates the relationship between the level of IT literacy of farmers and farmers’ profitability through online sales.

5. Findings and Discussion

This study aims to analyze the impact of farmers’ IT literacy and their attitude toward organic farming especially post COVID-19 in impacting the increase of profitability and sales through an online mode. Through the developed model and outcome, it is understood that COVID-19 correlates and moderates farmers’ IT literacy and their profitability through online sales along with their attitude towards organic farming in Tamil Nadu, India. The analysis of the demographic details of the respondents revealed that the educational level of the farmers who participated in the survey was very low (majorly secondary-schooling) whereas a majority of the respondents belonged to the age group 30+ years. The farmers were found to be earning approximately 5 Lakhs/year which could be increased with more online sales especially during and post pandemic situations [36]. However, in order to improve and gain IT literacy, it is necessary to analyze the cost, operations, and capital (finance management) and to study the supply-and-demand (market analysis) for organic produces. Analyzing costs such as environmental cost, information system cost, production cost, capital and investment transportation cost, operational cost, and transaction cost is very much important, since the lack of analysis could negatively impact the farmers and lead them toward bankruptcy. Similarly, analyzing the competitors and understanding their performance, technicality, strategies operations, logistics, supply chain, and demand (market analysis) is also important since the farmers could gain more knowledge and information to outrun the competitors and market players and attain sustainability.

Globally, more than 300 companies have been identified to be dealing with the sale of organic food products especially vegetables and fruits through e-commerce platforms. Companies such as WayCool (Chennai, India), FarmLead (Saskatoon, Canada), TaniHub (Jakarta, Indonesia), VnF (Mumbai, India), Crofarm (Gurgaon, India), Twiga (Nairobi, Kenya), Ninjacart (Bengaluru, India), Procsea (Montreux, Switzerland), Tridge (Seoul, South Korea), and Frubana (Bogota, Colombia) are noteworthy in the industry. Though there are several other companies, the above-mentioned are popular and known for their strategies in selling agro-products through an online (e-commerce) model at a global level. They are known for their better pricing strategy, transportation, transaction safety, reliability, quality, and reputation in meeting the consumers’ demand within a lesser span of time through a speedy delivery [37]. Local farmers developing their knowledge of e-commerce and adopting the strategies adapted by these global players can certainly gain better returns and profitability in business. By analyzing the transportation costs, delivery time, quality, and product availability, consumers might change the sellers, and hence it is necessary for the organic farmers to maintain balanced cost that will not affect their profitability and at the same time also increase the frequency of purchase by consumers in the long run.

6. Conclusions

The study aimed at exploring the relationship between the dependent variables of farmers’ profitability through online sales and the farmers’ frequency of sales through online stores and the independent variables of attitude towards organic farming and IT literacy level of farmers. The study analyzed the data collected from 271 farmers from Tamilnadu, India, who produced agricultural products through organic farming. The study also examined how the variable of the outbreak of COVID-19 moderated the relationship between the proposed dependent and independent variables. SPSS software has been used for analysis of the primary data. ANOVA and regression methods were employed for the testing of hypotheses.

It can be concluded from the research findings that the farmers’ profitability through online sales and the farmers’ IT literacy are moderated by the outbreak of COVID-19; similarly the frequency of online sales through stores is correlated to farmers’ attitude towards organic farming resulting in increased profit. The outbreak of COVID-19 has increased the digital marketing and sales when people were restricted to visit traditional stores due to social distancing issues. Further, the outbreak of COVID-19 has directly affected the traditional farmers who lacked digital literacy and stood as a hindrance to market their products through online sales. The farmers who transitioned from traditional marketing to online marketing sustained their market position; however, those who lacked digital marketing strategy encountered a huge loss.

It can be concluded that profitability of farmers is significantly correlated to farmers’ IT literacy, and at the same time online sales frequency of consumers is correlated to farmers’ attitude towards organic farming. COVID-19 positively moderates the online sales, which relatively were found to increase the profitability. It also was found to contribute toward successful and sustainable e-commerce, which in turn gives the farmers an upper-hand by facilitating dynamic pricing, increased production and operation in an electronic-based agro-market (demand-and-supply), management of transaction cost, the decision of transportation and delivering costs (cost management), and implementation of many more business strategies.