Document Type : Research Article-en
Authors
1 Urmia University, Faculty of Agriculture, Agricultural Economics Department
2 Tabriz University
3 Agricultural Research, Education and Extension Organization (AREEO), Agricultural and Natural Resources Research and Educational Center, Urmia, Iran
Abstract
Introduction
Digital marketing in agriculture has evolved significantly over the past decade, driven by advancements in technology and the increasing internet accessibility in rural areas. The Integrating of digital tools has enabled farmers to access real-time market information, weather forecasts, and best practices, thereby enhancing productivity and profitability. Understanding consumer intentions towards digital marketing is crucial in today's rapidly evolving digital landscape. As businesses increasingly rely on digital channels to reach and engage their target audience, comprehending the underlying motivations and attitudes of consumers becomes essential for effective strategic development. This research investigates the factors influencing consumer intentions to engage with digital marketing of agricultural products in Urmia, Iran, a region where agriculture plays a central role in the local economy.
Literature Review
Digital marketing in agriculture encompasses online and technology-driven promotional activities, such as social media, content marketing, and e-commerce (Tiago & Veríssimo, 2014; Michaelidou et al., 2011; Yadav & Rahman, 2017). These strategies aim to increase brand awareness, enhance customer engagement, and drive sales of agricultural products (Kutter et al., 2011). The adoption of digital marketing is driven by consumers' growing reliance on digital channels (Dlodlo & Dhurup, 2013). Successful implementation requires an understanding of the unique characteristics and challenges of the agricultural sector, including perishability, seasonality, and producer diversity (King et al., 2010). Existing research highlights both the potential benefits and barriers, such as infrastructure constraints and data privacy concerns.
Data and Methods
Our study utilized a structured questionnaire to gather cross-sectional data on factors influencing digital marketing engagement in agriculture. The questionnaire encompassed three main groups of variables: (1) Perceptions and Trust, including perceived usefulness (PU), perceived ease of use (PEOU), trust (TR), information quality (IQ), and social influence (SI); (2) Demographic and Economic Factors, comprising age (AGE), education level (EDU), income (INC), and price sensitivity (PS); and (3) Experience and Behavioral Intention, covering prior online purchase experience (EXP) and the intention to engage with digital marketing of agricultural products. To ensure a representative sample, we employed a multi-stage sampling technique, selecting regions based on agricultural activity and accessibility, and then randomly choosing participants from lists provided by local agricultural associations. Following data cleaning to address incomplete or inconsistent responses, we analyzed a final sample of 385 valid questionnaires.
To empirically analyze the factors influencing Zi, the following logistic regression model is employed (Greene, 2019):
(4)
where, logitP(Zi = 1) denotes the log odds of Zi equating to one, thereby indicating a preference for digital marketing option j. Xi represents a vector of control variables that could potentially influence the consumer's choice, encompassing demographic characteristics, prior experience, and other pertinent factors. The terms and correspond to the intercept and the coefficient for the control variables, respectively. signifies the error term, encapsulating unobserved factors that may impact the decision-making process. The Logit model can be estimated using maximum likelihood (MLE) process. The MLE of the logit model involves finding parameter estimates that maximize the likelihood function, which is derived from the probability distribution of the logistic function. This approach ensures that the estimated coefficients best fit the observed data by maximizing the probability of obtaining the observed outcomes, as discussed by McFadden (1974) and Greene (2019).
Results
The findings reveal that “Perceived Usefulness’, ‘Perceived Ease of Use’, ‘Trust’, “Information Quality’, and “Social Influence” are all significant predictors of consumer engagement with digital agricultural platforms. Demographic factors, such as “age”, “education”, and “income’, also impact consumer behavior. Younger individuals are less likely to engage, while higher education and income levels positively correlate with greater engagement. “Prior Online Purchase Experience” was a strong predictor of engagement, emphasizing the importance of familiarity with digital platforms, while “Price Sensitivity” showed a slight negative influence on engagement intentions. The study highlights that trust and ease of use are critical for consumers when considering the adoption of digital marketing platforms, suggesting that marketers should focus on creating user-friendly and trustworthy systems to foster engagement. Additionally, demographic segmentation is important for targeting, as different groups exhibit varying levels of digital engagement based on their characteristics. This research provides valuable insights into the specific behaviors of consumers in developing economies, offering empirical evidence that can guide future marketing strategies for agricultural products. The study adds to the limited literature on digital agricultural marketing in Iran and offers practical recommendations for optimizing consumer engagement.
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