Agricultural Input Subsidies and the Marketing of Agricultural Products

doi:10.21203/rs.3.rs-4615995/v1

The effect of agricultural input subsidies for farmers and other factors on the marketing of agricultural products is investigated in this paper. We first constructed a PLS-SEM; based on that, we designed survey questionnaires for Karaj city in the Alborz Province. The internal model shows that the method of production has the strongest effect on purchase intentions, followed by online purchases and input subsidies. Empirical analysis based on the questionnaire and PLS-SEM shows that the advertising variable has the strongest effect on the economic factors variable, followed by input subsidies and the distribution system. The path relationships between advertising, input subsidies, and the distribution system and economic factors are significant and show that all three variables are relatively strong predictors of economic factors.

Agricultural subsidies

Marketing

PLS_SEM

Social stability and economic growth are considered the two main objectives of transitional economies. To achieve these objectives, governments often provide various aids to intervene in economic activities. One of the most widely used aids is subsidies (Frye & Shleifer, 1997; Hemming et al., 2018; Jayne & Rashid, 2013), which are especially required to support vulnerable groups. In Iran, for example, the government spends high amounts annually on employment, innovation, and development, especially in some strategically important industries, such as agriculture, food, and high-tech sectors, for firm subsidies. Subsidies in the agricultural sector in Iran are mainly rural employment and food security. Fertilizer and other input subsidies have been used in agricultural policy in many countries since the 1960s (Holden, 2019). Input subsidies that target agricultural production are recurring and controversial development strategies (Messina et al., 2017).

Almost every country spends a significant portion of its income on agricultural subsidies. Agricultural subsidies are an essential aspect of agriculture and play an important role in trade and the market (Vozarovaa & Kotulic, 2016). Farmers and agricultural traders are paid to manage the supply of agricultural products or influence the price and supply of these products in international markets (Swain, 2009). Given that mass production practices have greatly contributed to increasing food production regardless of potential environmental hazards and thus have changed the emphasis on objectives related to the sustainable development approach, developing a sustainable trade system requires that countries and companies integrate economic, biological, and human practices (Quazi et al., 2001). The biological characteristics of agricultural products, decreased supply and demand elasticity, and climate sensitivity cause a relatively high degree of variability in agricultural prices that harms farmers' incomes (Gilbert, 2011).

Although political elements such as subsidies are important factors determining market power, whether subsidies strengthen corporate market power is still controversial (Shepherd, 1972). The positive relationship between subsidies and market power may arise from the following factors. First, subsidies tend to cause rents and unfair competition. As a result, the market power of firms changes. In particular, subsidies are seen as a signal recognized by the government or as a symbol of a good relationship with the government in a politically led society. In either way, it is relatively easier for companies to obtain subsidies for bank loans (Feldman & Kelley, 2006; Kleer, 2010) and other invisible benefits. Second, the positive effect of subsidies on innovation may cause a positive relationship between subsidies and market power, given that innovation has proven to be an important source of monopoly (Aghion & Howitt, 1992; Klette & Griliches, 2000; Liu & Huang, 2016; Zhang & Jia, 2011). Subsidies should be part of a broader strategy that provides complementary inputs and strengthens product markets. Following a strong tradition in economics, they also emphasized that input use should be economically efficient (Morris et al. 2007).

The small farmers' lack of access and low market participation are attributed mainly to entry barriers (Barrett, 2008; Jayne et al., 2010). These barriers include input needs for land, chemicals, fertilizers, and processing; demands for quality products; and high marketing exchange costs (Barrett, 2008; Heltberg & Tarp, 2002; Mather et al., 2013; Poulton et al., 2006). Global agricultural market conditions are relatively volatile due to many factors, including changes in agricultural policies in high-income countries and significant reductions in financial and government support for the agricultural sector (Jayne et al., 2010; Jayne et al., 2018). Some believe that in the current price system, agricultural subsidies could improve the supply of the agricultural workforce and subsequent agricultural production. By increasing the agricultural income of households, this policy can improve welfare. Empirical analysis has shown that subsidies cannot have as much effect as a limited level of subsidies or a reduction in the ratio of agricultural income to total household income (Zhong et al., 2013; Gautam, 2015). In many countries, price stability, including the price of agricultural and manufacturing products, is crucial. While there is a body of literature on the effect of government policies on the wholesale price of agricultural products, Quiggin and Anderson (1981), Don et al. (1992), Lai et al. (1996), Chen et al. (2013), Gouel (2013) and Yu (2014) discussed different policies using different methodologies.

On the other hand, increasing demand for agricultural products has made people stricter about agricultural quality (Antle & Stöckle, 2017). High-quality agricultural products are defined based on the production principles and methods of high-quality agriculture, green agriculture, and smart agriculture. The principle behind this approach is that no stage of production should put pressure on the ecological capacity of agriculture and follow its natural circle (Huang & Chen, 2021).

The marketing model refers to the methods used during the product marketing process and is considered a system (Pham et al., 2020). Two aspects need to be considered before product marketing: the market and consumers (Paramati et al., 2018). For the market, the marketing model for the realization of production marketing relies on four factors and their combinations, i.e., products, prices, channels, and advertising. The marketing model considers consumption psychology its core for starting production marketing for the consumer. The marketing model should change according to the real needs of consumers. The continuous improvement of marketing services can only achieve long-term development in a market economy (Nguyen et al., 2019; Aydoğan and Vardar, 2020).

The marketing model reflects people’s willingness to buy a product and the variables that affect it. Recently, different countries have conducted marketing studies using partial least squares path modeling (PLS-PM) at the industrial or national level. PLS-PM is a component-based approach to SEM. Two models describe a PM PLS path model: 1) the measurement or external model and 2) the structural and internal model, which links some endogenous LVs to other LVs.

In this study, a marketing model of agricultural products emphasizing the role of agricultural input subsidies is presented, which investigates the factors affecting the intention to purchase agricultural products using the PLS‒PM approach. This study will focus on a reflective measurement model and parameterization of the PLS‒PM route scheme commonly used for marketing modeling. Our proposed model is shown with the internal model specifications in Fig. 1, where the direct arrows show the causal relationships in one direction.

The proposed model is based on the following assumptions:

1. Agricultural subsidies, both directly and through economic factors, affect the intention to purchase agricultural products.
2. An online purchase is related to the intention to purchase agricultural products.
3. Advertising affects purchase intentions both directly and through economic factors.
4. The distribution system affects purchase intentions both directly and through economic factors.
5. Product safety affects purchase intentions.
6. Economic factors affect purchase intentions both directly and through product safety.

3.1 Data collection

Although PLS is known for handling small sample sizes, this does not mean that the objective should simply be set to meet the minimum sample size requirements. The literature has shown that a sample size of 100–200 is usually a good starting point for path modeling (Hoyle, 1990). Data from this study were collected using random sampling through face-to-face interviews with farmers in Alborz Province, Iran. A total of 159 farmers participated in the study. The sample is also representative of the socioeconomic situation of the population.

3.2 PLS-SEM

Structural equation modeling (SEM) has become quasi standard in marketing research (Babin et al., 2008; Bagozzi, 1994; Hulland, 1999; Hair et al., 2012a) because it allows authors to test complete theories and concepts (Rigdon, 1998). In particular, researchers use SEM to assess latent variables at the observational level (external or measurement model) and test the relationships between latent variables at the theoretical level (internal or structural model) (Bollen, 1989). The use of SEM in academic research has grown in many academic disciplines (Hair et al., 2012b), and SEM is considered one of the most important statistical developments in the social sciences in recent years. SEMs offer several features that appeal to researchers and distinguish them from first-generation regression tools (e.g., linear regression, analysis of variance [ANOVA], and multivariate analysis of variance [MANOVA]). In particular, research questions can be answered in a single, systematic and comprehensive analysis by modeling the relationships between several independent and dependent structures (structural models) simultaneously (Gefen et al., 2000; Hair et al., 2010).

PLS is a soft modeling approach for SEM without assumptions about the data distribution (Vinzi et al., 2010). Therefore, PLS-SEM is a good alternative to CB-SEM for many researchers. PLS is useful for modeling structural equations in applied research projects, especially when there are few participants and the data distribution is not normal; for example, in surveys of female CEOs or multinational CEOs (Wong, 2011). PLS-SEM is used in many fields, such as behavioral science (Bass et al., 2003), marketing (Henseler et al., 2009), organization (Sosik et al., 2009), management information systems (Chin et al., 2003), and business strategy (Hulland, 1999).

PLS-SEM has several desirable features for marketing modeling because it uses a causal modeling approach to maximize the explained variance of dependent structures. Since it is similar to multiple regression analysis, it is suitable for prediction (Hair et al., 2011). In addition, PLS-SEM avoids the problem of uncertainty and displays factor scores (Fornell, 1982), allowing the use of latent variable scores measured by one or more indicators in subsequent analyses (Henseler, 2009). As a result, PLS-SEM is especially useful for measuring the effectiveness of marketing campaigns by attributing sales to each of the advertising channels and calculating marketing returns (Méndez-Suárez & Monfort, 2021).

The advent of SEM with latent variables has changed the nature of marketing and management research. Researchers recognize the possibility of distinguishing between measurement and structural models and considering measurement error. As Gefen et al. (2000) stated, SEM is accurate for validating tools and testing links between structures. In addition, they distinguish between two SEM families: covariance-based techniques, as demonstrated by linear structural relations (LISREL), and variance-based techniques, of which PLS modeling is the most prominent representative.

3.3 Research Variables

The variables collected in the survey were the respondent's characteristics and the indicators of the latent variables. The respondent characteristics included sex, age, marital status, level of education, number of household members, agricultural background, and participation or nonparticipation in informal marketing classes. The indicators of the latent variables are summarized in Table 1. The latent variables were collected using a 5-point Likert scale.

Table 1

Summary of the factors affecting the marketing of agricultural products
latent variable	indicator
Economy	Consumer's prices
	Producer's cost
	Inflation
	Exchange rate
	Government economic policies
Advertising	Word-of-mouth advertising (marketing)
	Online advertising
	Simplicity in finding information or making an easy transaction
Distribution system	Proper product shipping methods
	Consumer's awareness of the origin of the product
	Distance to the sales center
	Having a personal vehicle to transport cargo to the market
input subsidies	Fertilizer subsidies
	Poison subsidies
	Other input subsidies
Product safety	Product safety (absence of heavy metals)
Product safety	Chemicals (no chemicals in the product)
Method of production	Comply with production standards in the field
Method of production	Integrated production methods (production methods that generally reduce the level of agricultural inputs such as poisons and other chemicals)
Willingness to use online purchase	Promote online transactions between producers and consumers
	Secure software and applications
	Ensure appropriate online platforms to provide adequate access to the product and facilitate its use
Attitude toward using online purchase	Positive attitude toward online purchase
Attitude toward using online purchase	Learning attitudes toward new online technology
Purchase intention	Consumer's satisfaction with the purchased product
	Customer loyalty to the purchased product
	Consumer's time, money, and effort savings
	Consumer's trust

4.1 Information about Respondents and Descriptive Statistics

The survey results showed that 23.9% of the respondents were female and 76.1% were male. The highest frequency of education among the respondents was diploma and less (33.3%), followed by diplomas and less (32.7%), master's degree (24.5%), associate degree (5%), and Ph.D. (4.4%). Regarding marital status, 22% of the respondents were single, and 78% were married. The mean number of household members among the respondents was 3.61. Moreover, farmers' mean agricultural work experience was estimated to be 17.41 years.

The destinations where farmers sell their products were considered. The results showed that most destinations for farmers' products were markets for agricultural products (50.3%), followed by markets far from fields (more than 100 km or similar) (13.8%), unions (11.9%), retail shops (7.5%), standing by the road or field side (6.3%), export markets (4.4%), restaurants (4.4%) and online shops, market cafés, and/or restaurants on the side of fields (0.6%).

Table 2 shows the mean input used by the respondents. According to the results of this table, the mean land area of the farmers was 2.63 hectares, the mean product sales were 298075471.69 RLS, the mean use of free fertilizer was 463.86 kg, the mean use of subsidized fertilizer was 56.81 kg, the mean use of nonsubsidized poison was 18.05 liters, the mean use of subsidized poison was 2.99 liters, the mean water use was 17426890.47 liters, and the mean hire of the workforce was 115.22 people-days.

Table 2

Mean use of agricultural inputs
workforce	water	Subsidized poison	Nonsubsidized poison	Subsidized fertilizer	Nonsubsidized fertilizer	Product sale	Land area
People - day	l	l	l	kg	kg	Rls.	ha
115.2201	17426890.4780	2.9937	18.0566	56.8113	463.8679	298075471.6981	2.6352

4.2 Analysis of Factors Affecting Purchase Intention

The first stage of analysis was to assess the measurement model and the structural model that were applied to both the external and the internal models. The results of the analysis are given below. Path modeling estimates were performed using the software Smart-PLS. There are two types of numbers in the PLS-SEM diagram:

1. Numbers in a circle: These numbers indicate how much other latent variables explain the variance of the hidden variable.
2. Numbers on the arrow: These are called path coefficients. They explain how strong the effect of one variable is on another. The weight of the different path coefficients enables us to rank their relative statistical significance.

The modeling estimation of the PLS path is shown in Fig. 2.

Among all the estimation results, the 3 key pieces of the Smart-PLS software used for the model are as follows:

1. External loads (in the reflective measurement model) or external weights (in the shaping measurement model)
2. Path coefficients for the structural model relationship
3. R² values of the endogenous latent variables

4.2.1 Evaluation of the PLS-SEM Model by Reflective Measurement

By examining the PLS-SEM estimation diagram in Fig. 2, we can make the following initial observations:

Explaining the variance of the endogenous objective variable

The coefficient of determination, R², is 0.141 for the endogenous latent variable of purchase intention. This means that the seven latent variables (online purchase, input subsidies, advertising, economy, method of production, distribution system, and product safety) explain 14.1% of the variance in purchase intentions.

The coefficient of determination of 0.533 for the economy variable shows that the three latent variables, advertising, input subsidies, and distribution system, explain, on average, 53.3% of the variance in the latent variable of the economy.

A coefficient of 0.605 for the variable of product safety shows that the two latent variables of economy and method of production explain, on average, 60.5% of the variance in the latent variable of product safety.

Size and importance of the internal model path coefficient

The internal model shows that the method of production has the strongest effect on purchase intentions (0.148), followed by online purchases (0.132) and input subsidies (0.127).

• The hypothetical path relationship between the method of production and purchase intention is statistically significant.

• The hypothetical relationship between an online purchase and purchase intention is statistically significant.

• The hypothetical path relationship between input subsidies and purchase intentions is statistically significant.

• The hypothetical paths connecting product safety, the economy, advertising, the distribution system, and purchase intentions are not statistically significant.

If the standard path coefficient is less than 0.1, that relationship is insignificant. Therefore, it can be concluded that the method of production, online purchase, and input subsidies are relatively strong predictors of purchase intention. Nevertheless, the economy, advertising, and distribution system do not directly predict purchase intention.

Another result obtained from the model is that the variable of advertising has the strongest effect on the variable of the economy (0.354), followed by input subsidies (0.3) and the distribution system (0.262). Moreover, the relationships between advertising, input subsidies, the distribution system and the economic variables are significant. This shows that all three are relatively strong predictors of the economy variable.

Another result of the model shows that the production method variable has the strongest effect on the product safety variable (0.608), followed by the economic variable (0.341).

4-2-1-1-Reliability Indicator

Like in other marketing research, to complete the measurement model review, it is necessary to confirm the reliability and validity of the latent variables. A confirming reliability indicator (Table 3) showed that all the indicators had values greater than the minimum acceptable level of 0.4 and close to the preferred level of 0.7.

Table 3

Summary of results for reflective external models
Latent variables	Indicator	loading	Indicator reliability	Composite reliability	Rho_A	AVE
Economic factors	Consumerprice	0.727	0.53	0.839	0.761	0.511
	Exchangerate	0.716	0.51
	Inflation	0.720	0.52
	Producercost	0.703	0.49
	Publiceconomicpolicy	0.709	0.5
advertising	Easytransaction	0.843	0.71	0.889	0.824	0.729
	Mouthtomouthadvertising	0.789	0.62
	Onlineadvertsing	0.924	0.85
Input subsidy	Fertilizersubsidy	0.884	0.78	0.923	0.882	0.8
	Othersubsidy	0.945	0.89
	Poisonsubsidy	0.853	0.73
Online purchase	Application	0.964	0.93	0.961	0.945	0.891
	Onlineplattform	0.957	0.92
	Onlinetransaction	0.909	0.83
Online attitude	Learningattitude	0.958	0.92	0.958	0.913	0.919
Online attitude	Positiveattitude	0.960	0.92	0.958	0.913	0.919
Distribution system	Consumerawareness	0.788	0.62	0.862	0.811	0.611
	Distance	0.825	0.68
	Goodtransportation	0.813	0.66
	Havingavehicle	0.692	0.48
Method of production	Integration	0.842	0.71	0.843	0.629	0.728
Method of production	Standards	0.864	0.75	0.843	0.629	0.728
Product safety	Chemicals	0.938	0.88	0.931	0.854	0.871
Product safety	Safety	0.929	0.86	0.931	0.854	0.871
Purchase intention	Confidence	0.648	0.42	0.824	0.766	0.541
	Customerloyalty	0.677	0.46
	Customersatisfaction	0.807	0.65
	Saving	0.797	0.64

4-2-1-2-Internal Consistency Reliability

Cronbach's alpha has traditionally been used to measure internal reliability in social science research, but the literature has suggested using composite reliability as an alternative (Bagozzi and Yi, 1988; Hair et al., 2012). According to Table 3, such values are greater than 0.6; thus, high levels of internal consistency and reliability are shown among all the reflective latent variables. However, the modern PLS view suggests that instead of using Cronbach's alpha and composite reliability, the coefficient rho_A should be used to confirm the reliability of PLS structural scores. Generally, a value of "rho_A" of 0.7 or greater is preferred to confirm composite reliability. Moreover, a value of "rho_A" above 1 is abnormal and should not occur in the model. Table 3 shows rho_A values above 0.6 for all the latent variables.

4-2-1-3-Convergent Validity

For convergent validity, each latent variable's average variance extracted (AVE) was assessed. According to Table 3, it is clear that all the values of AVE are greater than the acceptable threshold of 0.5, so convergent validity is confirmed.

With a literature review, we found that consumers' intention to purchase agricultural products can be a function of various factors, including the economy, advertising, input subsidies, product distribution systems, online purchases, methods of production, and product safety. Due to the small sample size, SEM-PLS was used. SEM-PLS analysis revealed that seven latent variables (online purchase, input subsidies, advertising, economy, production method, distribution system, and product safety) explained 14.1% of the variance in purchase intentions. While the coefficient of determining the hidden variable of economic factors such as consumer price, exchange rate, inflation, producer cost, and government economic policies showed that advertising, input subsidies, and the distribution system, on average, explained 53.3% of the variance in the latent variable of the economy, product safety was another latent variable that explained the economy, and the production method explained 60.5% of the variance in this variable. The three variables that had the strongest effect on the intention to purchase a product included production method, online purchase, and input subsidies.

These results are based on a survey conducted in Alborz Province, Iran. Given that the characteristics and style of farmers' activities (apart from specific climatic conditions) and government policies affecting this sector in Iran follow a certain trend, the results can be generalized to general cases.

Consent

This research study confirms that informed consent has been obtained from all participants involved in the study. Each participant was fully informed about the nature, purpose, procedures, and potential risks and benefits of the study before their participation.

For participants under the age of 18, informed consent was obtained from their parents and/or legal guardians. This consent includes approval for both participation in the study and the subsequent publication of the data collected.

All participants, or their guardians in the case of minors, provided written consent, affirming their understanding and agreement to the terms of the study. This process ensures compliance with ethical standards and guidelines set forth and relevant regulatory bodies.

Bioethics and Guidelines

This study will be conducted in accordance with the ethical guidelines and principles laid out by Islamic Azad University and Relevant Ethical Guidelines. The research team is committed to ensuring the highest ethical standards are maintained throughout the study. All data collected will be used solely for research purposes and handled in a manner that ensures confidentiality and anonymity.

Data availability

The data collected during this study will be stored securely and only accessible to the research team. Aggregated and anonymized data may be shared with other researchers for the purpose of further analysis and publication. Any data shared will not contain any information that could identify you. If you wish to access the data you have provided, or have any concerns regarding the data handling, you may contact

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No competing interests reported.

Agricultural Input Subsidies and the Marketing of Agricultural Products

Status:

Version 1

Abstract

Figures

1. Introduction

2. Conceptual Framework and Hypotheses

3. Research Methodology

3.1 Data collection

3.2 PLS-SEM

3.3 Research Variables

4. Results and Discussion

4.1 Information about Respondents and Descriptive Statistics

4.2 Analysis of Factors Affecting Purchase Intention

4.2.1 Evaluation of the PLS-SEM Model by Reflective Measurement

4-2-1-1-Reliability Indicator

4-2-1-2-Internal Consistency Reliability

4-2-1-3-Convergent Validity

5. Conclusion

Declarations

References

Additional Declarations

Status:

Version 1