Use of social media in the marketing of agricultural products and farmers’ turnover in South-South Nigeria

2.1 Channels of Advertisement

The traditional marketing of agricultural products consists of determining the farm products, producing in the right quality, storing to ensure that the products are available; and transporting the products to the places where they are required. It consists mainly of non-verbal communication between the producer and the consumer. The producer (farmer) conceives a product that is required by consumers, produces it in a manner perceived to be consistent with customers` requirement, stores it, and distributes to locations perceived to have adequate demand.

Use of print and electronic media in advertising. Beyond the product-price-storage-distribution strategy, large organisations and government have been known to market their products through advertisements in print and electronic media. The essence of such advertisements is to create awareness of the availability of the products, the locations where they are available as well as the expected benefits of using those products, especially if the products can be substituted for other products.

Adoption of social media for agricultural marketing in South-South Nigeria. Given the shortcomings of the print and electronic media in marketing agricultural products, the need for a more efficient strategy has been long overdue. The rapid growth of the mobile phone use around the world in the last few decades provided a viable marketing alternative for agricultural products in Nigeria and other emerging economies. Mobile phones have contributed significantly to the empowerment of people in developing countries in spreading information networking coverage in remote areas. Consequently, many rural areas are getting great benefit out of its usage in various spheres of endeavour. This has culminated in the improvement of the living standards of poor farmers in developed nations.

2.2 Theoretical review

2.2.1 Technology acceptance model (TAM). As one of the frequently used theories to underpin internet usage, the major thrust of TAM is that the users’ beliefs in perceived usefulness and perceived ease of use of the internet can be used to explain their acceptance of internet (Chuttur, 2009; Dwivedi et al., 2017). Thus, the extent to which the user believes that technology is useful for the cause and the extent to which he feels that he can easily use technology are critical to his/her willingness to use it. Recent modifications and refinement to TAM have culminated in the extension of its use to enable a better understanding of users` intention to use internet technology.

2.2.2 Theory of agricultural marketing cooperatives. This theory visualizes a model that studies how an agricultural marketing cooperative (AMC), specifically an AMC that is made up of many farmers as members, can adopt a direct selling approach. Under this arrangement, the farmers who are the members of the AMC can sell their products either to the cooperative or in a local market. The model shows that if the farmers decide to sell to the AMC, the decision will induce an anti-competitive effect on the direct selling market, whereas, the option of direct selling has the propensity to create a "healthy emulation" among farmers (Agbo et al., 2013). This will then stimulate production and ultimately benefit the cooperative.

2.2.3 Theoretical framework. This study adopts the TAM and the theory of agricultural cooperatives as its framework. TAM was chosen because the ease with which the farmers feel they can utilize social media to market their products and the usefulness of social media marketing to the attainment of their marketing goals will influence adoption of social media in the marketing of their products. Furthermore, the cooperative societies to which the farmers belong can play a critical role in the marketing of the farmers’ products because farmers that belong to an agricultural marketing cooperative (AMC) can be encouraged to sell directly to the AMC.

2.3 Empirical review

The empirical review focuses on cost of marketing agricultural products as well as sales turnover.

2.3.1 Use of social media in marketing agricultural products and cost of marketing. Balkrishna & Deshmukh (2017) investigated the “role of social media in agriculture marketing and its scope.” They employed questionnaire and in-depth interviews to collect data. Results indicated that social media is very useful in agricultural marketing.

Vassiliadou et al. (2011) investigated “the use of social media among students of Technology Agriculture and their role in promoting agribusiness.” The results showed that social media usage enhances facilitation and flow of knowledge/information, as well as cheap advertisement of products. The results are thus consistent with those of Balkrishna & Deshmukh (2017). The outcomes of the two studies suggest that social media usage enhances efficiency in information flow and marketing, hence the likelihood that its usage will also enhance the efficiency of agricultural products marketing.

2.3.2 Use of social media in agricultural marketing and sales turnover. Mwangi & Wagoki (2016) surveyed leading media groups to investigate “the effect of social media on the performance of advertisement business in the mainstream media in Kenya.” They employed stratified random sampling to select 82 respondents. A questionnaire served as the research instrument. Descriptive and inferential statistics were employed. The findings indicate that the interactivity of social media had a significant positive relationship with the performance of the advertisement. Lashgarara et al. (2011) examined “ICT capabilities in improving the marketing of agricultural productions of Garmsar Township, Iran.” The survey research design was employed with questionnaire serving as the research instrument. The results indicated that ICT capabilities determined 57% variance of agricultural products marketing. Mwangi & Wagoki’s (2016) and Lashgarara et al.’s, (2011) studies indicate that social media has a significant influence on the performance of advertisement and on agricultural products marketing respectively. Based on the outcomes of these studies, the following null hypotheses were formulated:

H0₁:  There is no significant relationship between the use of social media in agricultural marketing and cost of marketing agricultural products

H0₂ :    There is no significant relationship between the use of social media (Facebook, WhatsApp and Instagram) in agricultural marketing and farmers’ turnover from agricultural products

Other related studies are “use of information communication technologies among agricultural extension officers in Lesotho” (Akintunde & Oladele, 2019), “adoption of agricultural e-marketing,” (Alavion et al., 2017), as well as the studies of Eze et al. (2019), among others.

2.3.3 Gaps in the literature. Adoption of social media and other communication technologies in the marketing of agricultural products is not deficient in the empirical literature. Balkrishna & Deshmukh (2017); White et al. (2014) as well as Khou & Suresh (2018), investigated use of social media in agricultural marketing; Akintunde & Oladele (2019) and Alavion et al. (2017) examined determinants of ICT usage in the marketing of agricultural products. Jose & Lokeswari (2018) investigated users and non-users of mobile communication technologies in agricultural marketing; Lashgarara et al. (2011) examined ICT capabilities in improving the marketing of agricultural productions, Ogunniyi & Ojebuyi (2012) investigated “Mobile phone use in agribusiness by farmers in Southwest Nigeria while Mwangi & Wagoki (2016) investigated the effect of social media on the performance of advertisement business in in Kenya. Although a few of the studies investigated the use of social media, none appears to have focused on the implications of the usage of social media in agricultural marketing on marketing efficiency; neither is adequate empirical evidence available on how the use of social media in the marketing of agricultural products can impact on efficiency and sales turnover. Besides, related studies on the research problem in South-South Nigeria are either scanty or non-existent. This study sought to fill these gaps.

Study population

3.1 The population of the study consisted of 4280 farmers registered in cooperative societies. Of this number, 1620 are from Edo State, 1460 are from Ondo State and 1200 are from Delta state. Yamane’s formula was used to estimate a sample size of 366 (Yamane, 1967). Thereafter, proportional allocation was used to assign 139, 125 and 102 to Edo, Ondo and Delta States, respectively to ensure that each State’s representation was proportional to its’ population. Of the 366 respondents that were sampled, 246, representing 67.2% of them voluntarily participated in the study. The participants were randomly selected from farmers’ cooperative societies in the three states. The lottery method was used to achieve randomisation after stratifying the farmers on the basis of the type of farming that they engage in (crop, poultry and fish farming). Subsequently, the farmers were contacted through any of the social media that they use (Facebook, WhatsApp or Instagram). The major inclusion criterion was membership of farmers’ cooperative while the exclusion criterion was the non-usage of any of the three social media platforms (Facebook, WhatsApp or Instagram). The choice of these states was informed mainly by convenience. Specifically, samples were taken from the current members of the cooperative societies. The sample consisted of crop, poultry and fish farmers with evidence of usage of social media (Facebook, WhatsApp or Instagram). The sampling frame was requested from the management of the cooperative societies.

3.1.1 Request for consent of respondents to participate in the study. After the sampling of the respondents, the verbal consent of the management of the cooperative societies was sought. Having obtained the consent of the leaders of the cooperatives, the consents of all the 366 sampled respondents across the three States were sought through the social media. Following the request to participate in the study, through social media 120 of them across the three states declined to participate in the study through their response to the social media message requesting their participation. While a few cited personal reasons for declining, majority of them did not advance any reason, they just refused to respond to the request. Of those approached, 246 gave their consent and thus participated in the study.

3.1.2 Validity of instrument. A pilot test was conducted on 20 of the sampled respondents. Based on the results obtained from the pilot test, validity and reliability of the instrument were determined. For validity, two approaches were used. First, the instrument was given to experts in management and marketing in the authors’ institution for their expert opinion, this served to fulfil the condition of face validity. Thereafter, content validity index (CVI) was computed. Both scale and item content validity measures were used. The results obtained were 0.66 for scale as well as 0.67 and 0.67 for item CVI of use of social media in marketing agricultural products and cost reduction as well as use of social media in the marketing of agricultural products and farmers’ turnover respectively, thus showing that the instrument was valid since all the values are approximately 0.7 and a value of 0.7 is indicative of a valid instrument (see Table 1).

3.1.3 Reliability of the instrument. Cronbach’s alpha (α) was used to determine the reliability of the instrument. The computed Cronbach alpha values were 0.70 and 0.75 for use of social medial and cost reduction as well as use of social media and sales turnover respectively while the computed alpha of the entire instrument was 0.84. These values were considered significant since they are approximately 0.7 or more, thus indicating that the items in the instrument are internally consistent. In other words, the instrument is reliable. The foregoing indicates that the instrument is valid and reliable (see Table 2).

3.2 Data Sources

Based on the sampling frame, a sample of respondents was selected. Thereafter, the sampled respondents were requested to participate in the study through social media. A survey was constructed and used to examine the use of social media in agricultural marketing and its implication for efficiency and sales turnover through the administration of a questionnaire, which served as the research instrument. The questionnaire contained bio-data questions and 5-point Likert scale questions dealing with social media usage in agricultural marketing and its implication for efficiency and sales turnover in South-South Nigeria. Information was elicited from the respondents via structured questionnaires through the social media channels (Facebook, Instagram and WhatsApp). The questionnaire is available as Extended data (Inegbedion et al., 2020).

The authors sought and got ethical approval to conduct the study from the Landmark University Research Ethical Board. Furthermore, the authors complied with conventional ethical standards in conducting the study, including the request for the consent of the sampled respondents to participate in the study

3.3 Model specification

CMAP  =  f (UFB, UWA and UINS)            . . .             (1)

DAP    =  f (UFB, UWA and UINS)            . . .              (2)

In specific terms, equations 1 and 2 yield

CMAP  =  β₀ + β₁ UFB + β₂ UWA + UINS+ e      . . .     (3)

DAP    =  β₀ + β₁ UFB + β₂ UWA + UINS + e      . . ..    (4)

Where

      CMAP = Cost of marketing agricultural products;

      DAP = Demand for agricultural products;

      UFB = usage of Facebook;

      UWA = usage of WhatsApp;

      UINS = usage of Instagram; and

      e. = random error observed along with the variables

3.4 Estimation technique

Research data were analysed using the one-sample t-test and least-squares regression. One sample t-test was used to test for significance of the usage of social media constructs in cost reduction and sales turnover while the least-squares method was used to test for the predictive power of the entire constructs (collectively) with respect to cost reduction and sales turnover; besides, the signs of the coefficients of the constructs in the regression model were used to infer the direction of the relationships between usage of social media in agricultural marketing and cost of marketing agricultural products on one hand as well as usage of social media in agricultural marketing and sales turnover on the other hand. Data analysis was conducted with SPSS 20.

Social media usage and reduction in cost of advertising agricultural products

A comparison of the usage of Facebook in agricultural marketing with cost reduction in marketing of agricultural products revealed that respondents who agreed that the usage of Facebook enhances cost reduction had a mean score 3.11. Given the five-point Likert scale, a test value of 3 was used. The computed t- and p-values were 2.04 and 0.046 respectively. This shows that the test was significant at five percent level. We thus conclude, at the 95% confidence level, that usage of Facebook in marketing agricultural products significantly reduces the cost of marketing (see Table 3).

A comparison of the usage of WhatsApp in agricultural marketing with cost reduction in marketing of agricultural products revealed that respondents who agreed that the usage of Instagram enhances cost reduction had a mean score 3.59. Given the five-point Likert scale, a test value of 3 was used. The computed t- and p-values were 14.58 and 0.001, respectively. This shows that the test was significant at the one per cent level. We thus conclude, at the 99% confidence level, that usage of WhatsApp for marketing agricultural products significantly reduces the cost of marketing (see Table 3).

A comparison of the usage of Instagram in agricultural marketing with cost reduction in marketing of agricultural products revealed that respondents who agreed that the usage of Facebook enhances cost reduction had a mean score 3.09. Given the five-point Likert scale, a test value of 3 was used. The computed t and p values were 1.99 and 0.048, respectively. This shows that the test was significant at the five per cent level. We may thus conclude, at the 95% confidence level, that usage of Instagram in marketing agricultural products significantly reduces the cost of marketing (see Table 3).

A regression model of usage of social media in the marketing of agricultural products and cost of marketing agricultural products revealed that the R-Square value was 0.48, thus implying that 48% of the variation in the cost of marketing agricultural products is explained by variation in the usage of social media channels (see Table 4). The ANOVA table shows a calculated F of 10.94 with an associated significant probability of p < 0.001, thus indicating that the F value is significant. The implication is that the overall significance of the model is good (see Table 4). The regression coefficients show that the computed t-values and associated p-values were 3.152 (0.002), 0.337 (0.076), 5.015 (p < 0.001) and 2.570 (0.011) for constant, Facebook usage, WhatsApp usage and Instagram usage, respectively (see Table 4). The implication is that the explanatory variables, consisting of the usage of social media channels (WhatsApp and Instagram) are significant and are thus predictors of cost reduction in the marketing of agricultural products. However, Facebook usage is not significant and is thus not a predictor of cost reduction in the marketing of agricultural products.

Social media usage and sales turnover from agricultural products

A comparison of the usage of Facebook in agricultural marketing with sales turnover of farmers revealed that respondents who agreed that the usage of Facebook enhances farmers’ sales turnover had a mean score 3.214. Given the five-point Likert scale, a test value of 3 was used. The computed t and p values were 5.44 and p <0.001 respectively. This shows that the test was significant at one percent level. We thus conclude, at the 95% confidence level, that usage of Facebook in marketing agricultural products significantly enhances the sales turnover of farmers (see Table 5).

A comparison of the usage of WhatsApp in agricultural marketing with sales turnover of agricultural products revealed that respondents who agreed that the usage of WhatsApp enhances sales turnover had a mean score 3.289. Given the five-point Likert scale, a test value of 3 was used. The computed t and p values were 5.84 and (p<0.001), respectively. This shows that the test was significant at one per cent level. We thus conclude, at the 99% confidence level, that usage of WhatsApp in marketing agricultural products significantly optimises the sales turnover of farmers (see Table 5).

A comparison of the usage of Instagram in agricultural marketing with sales turnover of agricultural products revealed that respondents who agreed that the usage of Instagram enhances sales turnover had a mean score 3.115. Given the five-point Likert scale, a test value of 3 was used. The computed t and p values were 4.32 and P < 0.001 respectively. This shows that the test was significant at one percent level. We thus conclude, at the 99% confidence level, that usage of Instagram in marketing agricultural products significantly optimises the sales turnover of farmers (see Table 5).

A regression model of usage of social media and turnover of agricultural products revealed that the R-Square value was 0.58, thus implying that 58% of the variation in turnover of agricultural products is explained by variation in the usage of social media (see Table 6). The ANOVA table shows a calculated F of 7.21 with an associated significant probability of P < 0.001. The implication is that the overall significance of the model is good (see Table 6). Lastly, the regression coefficients show that the computed t and associated significant probabilities were 12.58 (0.00), 0.816 (0.415), 2.77 (0.006) and 4.614 (p < 0.001) for constant, Facebook usage, WhatsApp usage and Instagram usage, respectively (see Tables 6). The implication is that WhatsApp and Instagram usage are predictors of turnover of agricultural products.

Demographic variables and social media usage

F test was conducted to compare respondents’ perception of the use of social media in the marketing of agricultural products with their demographic variables. A comparison of respondents’ perception of usage of social media in cost reduction and demographic variables had the following computed F and associated significant probabilities 1.12 (0.348), 1.36 (0.25), 0.673 (0.671) and 1.28 (0.28) for age, sex, educational qualification and farm categories, respectively. The implication is that respondents’ perception of the significance of the usage of social media in the reduction of agricultural marketing cost is not influenced by demographic variables (see Table 7).

Lastly, a comparison of respondents’ perception of the significance of the usage of social media in enhancing the turnover of agricultural products had the following computed F and associated p-values 0.29 (0.83) 1.26 (0.27), 0.961 (0.51) and 0.734 (0.73) for age, sex, educational qualification and farm categories, respectively. The implication is that respondents’ perception of the significance of the usage of social media in enhancing the turnover of agricultural products is not influenced by demographic variables (see Table 8).

Proposed model of usage of social media and expected outcomes

Based on the research findings, a model of social media marketing of agricultural products, efficiency of agricultural marketing and sales turnover was proposed. The model shows that two social media channels (WhatsApp and Instagram) predict social media impact on the marketing of agricultural products in terms of efficiency and enhanced turnover. The model shows that the use of social media (WhatsApp and Instagram) leads to optimal marketing cost and thus efficient marketing of agricultural products. The optimal cost of marketing agricultural products translates to cost savings and hence optimal turnover in agricultural products. Also, effective use of social media (WhatsApp and Instagram) stimulates demand which also results in optimal sales turnover from agricultural products (see Figure 1).

Figure 1. A model of social media marketing of agricultural products and sales turnover.

Implication of findings/recommendation

The significance of the use of social media in the marketing of agricultural products to marketing efficiency and sales turnover implies that policy makers in government and strategic managers of agro-allied firms can embrace the use of social media in the marketing of agricultural products to minimize the cost of marketing and enhance turnover from the sales of agricultural products. Growth in the agricultural sector has implications on a nation’s gross domestic product and by implication, on national development. Given the problem definition and research findings, the following recommendations are suggested. Policymakers in government should be concerned about increasing agricultural production as well as the marketing of agricultural products to enhance earnings from agricultural production. This will attract many unemployed youths to the agricultural sector and thus help to guarantee food security as well as drastically reduce the level of unemployment in the country. Consequently, policymakers in government and other stakeholders like managers of cooperative societies and other farmers’ associations should promote the adoption of social media in agricultural marketing through sensitization of the farmers as well as the outright provision of modern communication gadgets to farmers at subsidized prices.

Future studies should attempt to minimize the limitations associated with cultural bias by including more south-south states in the sample as well as try to include non-members of the farmers’ cooperative society in the sample.

Underlying data

Dryad: Use of social media in the marketing of agricultural products and farmers’ turnover in South-South Nigeria. https://doi.org/10.5061/dryad.jwstqjq76 (Inegbedion et al., 2020).

File ‘Data-Use_of_Social_media_in_Agric_Markt-22’ contains basic demographic information, questionnaire responses and social media use data from each participant.

Extended data

Dryad: Use of social media in the marketing of agricultural products and farmers’ turnover in South-South Nigeria. https://doi.org/10.5061/dryad.jwstqjq76 (Inegbedion et al., 2020).

File ‘Questionnaire-Use_of_Social_Media_in_Agric_Marketing.docx’ contains a blank copy of the questionnaire used in this study.

Data are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).