Modelling Consumer Demand for Organic Agricultural Products: Sustainable and Digital Marketing Approach

Abstract

This paper examines the forecasting and modelling of consumer demand for organic agricultural products in the context of sustainable development and digital marketing. The study is based on an online survey of 423 consumers in Ukraine and Slovakia and applies factor analysis, regression modelling, and scenario forecasting. The results indicate that demand formation is driven by a combination of economic, social, and behavioural factors, with income level, price sensitivity, trust in certification, and awareness of organic benefits playing a central role. The findings further confirm that digital marketing tools significantly influence purchasing decisions by enhancing information transparency and strengthening consumer trust. From a theoretical perspective, the study contributes to the literature by integrating economic determinants of demand with digital marketing and sustainability-related factors within a unified analytical framework. From a managerial perspective, the results provide practical guidance for organic producers and marketers regarding the communication of environmental value and the effective use of digital channels to stimulate demand and build long-term consumer loyalty. The novelty of the study lies in the combined application of demand modelling and digital marketing analysis within a comparative cross-country context, offering an integrated approach to forecasting organic product demand and supporting the development of sustainable marketing strategies.

1. Introduction

Modelling consumer demand for organic agricultural products is an important component of the effective development of the organic market. In the context of changing economic conditions, household income levels, and food price dynamics, the consumption of organic products remains limited for a certain part of consumers. However, the market for organic products demonstrates a clear upward growth trend, indicating shifts in consumer preferences and an increasing interest in healthy and sustainable eating.

The current development of the organic market is closely linked to the concept of sustainable development, which is based on balancing economic efficiency, social welfare, and environmental responsibility. Within this framework, organic production supports biodiversity conservation, rational resource use, and the reduction in negative environmental impacts.

The growing demand for organic products, therefore, reflects not only market dynamics but also a broader societal shift toward sustainable consumption, grounded in environmental awareness and ethical values.

In the context ongoing digital transformation, digital marketing plays a key role in shaping consumer demand for organic products. Tools such as social media platforms, data analytics, content marketing, and search engine optimization enable firms to identify target segments more effectively, increase awareness of organic benefits, and strengthen consumer trust.

Moreover, digital communication channels facilitate two-way interaction between producers and consumers, enhance transparency, and contribute to the diffusion of sustainable consumption practices.

Organic products are widely associated with health benefits and environmental protection. Rising income levels and living standards tend to increase their consumption, as consumers perceive these products as healthier, safer, and more environmentally friendly. Despite their higher prices, a growing share of consumers is willing to purchase organic food in exchange for higher perceived quality.

Forecasting demand for organic agricultural products is a key research challenge in the context of globalization and evolving consumer preferences. The analysis of demand drivers and the development of forecasting models have attracted increasing scholarly attention in agribusiness, economics, and organic marketing. Due to the multidimensional nature of consumer decision-making, demand forecasting requires an interdisciplinary analytical approach.

Ukraine and Slovakia were selected as case countries due to their contrasting yet comparable positions within the European organic food landscape. While Slovakia represents a more mature EU organic market with higher income levels and institutionalized sustainability policies, Ukraine represents a transitional market characterized by rapid digitalization, growing environmental awareness, and higher economic volatility. This contrast enables a comparative assessment of how sustainable and digital marketing mechanisms operate under different macroeconomic and institutional conditions.

Demand for organic products is shaped by consumer income, social and cultural preferences, and environmental trends. Previous studies identify income, health awareness, and lifestyle changes as the key drivers of organic food demand. Higher income levels increase consumers’ willingness to purchase organic products, which typically carry a price premium over conventional alternatives [1]. At the same time, awareness of the health benefits of organic food has been shown to stimulate demand even among middle-income consumers [2]. Changes in dietary habits, particularly the growing interest in healthy eating and sustainable lifestyles, further reinforce this trend [3]. Together, these factors explain the steady growth of organic product demand observed in developed markets, especially in Europe and North America.

Previous research identifies economic and environmental factors as central determinants of demand for organic agricultural products. Income level plays a crucial role, as higher-income consumers are more likely to purchase organic products due to their perceived qualitative advantages [4]. At the same time, price remains a significant barrier for middle- and low-income consumers, limiting broader adoption of organic products [5]. Alongside economic factors, environmental motivation has been consistently shown to influence organic food choices, with consumers valuing reduced environmental impact, resource efficiency, and sustainability-related attributes [6,7,8]. These considerations underpin the concept of sustainable development in agriculture and form the foundation of sustainable marketing, which extends beyond short-term profitability toward long-term environmental and social outcomes. Effective communication and marketing further reinforce these drivers: emphasizing environmental benefits through targeted advertising has been shown to significantly stimulate consumer interest and demand for organic products [9].

Recent studies highlight the growing role of digital technologies and digital marketing in modelling and forecasting consumer demand. Digital communication platforms enable more effective dissemination of information about the quality and benefits of organic products, thereby strengthening the image of sustainable brands [10]. Advances in big data analytics further support the identification of new consumer segments and improve the accuracy of demand forecasting [11]. In addition, the application of machine learning and digital data analysis facilitates the development of adaptive demand models that account for dynamic changes in consumer behaviour [12,13]. These approaches are particularly relevant in the context of digital transformation, where marketing increasingly functions not only as a sales instrument but also as a mechanism for informing consumers and promoting sustainable consumption patterns.

A wide range of forecasting methods is applied in research on organic product demand, including factor and regression analysis, econometric modelling, machine learning, and survey-based approaches. Regression models are commonly used to identify relationships between key economic variables—such as income levels, prices, and demand for organic products—and have proven effective in explaining consumption patterns [14,15]. Econometric modelling further enables the construction of complex forecasting systems and scenario-based analyses that account for changes in external conditions, including price dynamics and shifts in consumer behaviour [16,17].

In recent years, machine learning techniques have gained increasing attention due to their ability to process large datasets and capture nonlinear relationships in demand forecasting. Neural network–based models have demonstrated strong potential for predicting organic product demand in dynamic market environments [18]. In parallel, survey research remains a widely used method, as questionnaires provide direct insights into consumer preferences, awareness, and purchase determinants. Previous studies emphasize that combining survey data with economic indicators—such as income, price levels, and product availability—offers a comprehensive and robust approach to forecasting demand for organic products [19].

Research on consumer preferences and satisfaction constitutes an integral part of organic demand analysis. Prior studies demonstrate that satisfaction with organic products is closely linked to perceived quality, health benefits, and ethical aspects of production [20]. Health- and environmentally conscious consumers therefore exhibit a higher propensity to purchase organic products and place strong emphasis on product quality rather than price alone. Empirical evidence further confirms that consumers are willing to pay a price premium for organic products when they are perceived as healthier and safer alternatives [21].

Assessing consumer satisfaction is particularly important for demand forecasting, as it influences repeat purchases and long-term consumption patterns. Previous research shows that satisfaction with organic products depends on consumers’ expectations regarding quality, taste, and food safety [22]. In addition, behavioural factors—such as attitudes toward health and environmental protection—play a significant role in shaping organic food preferences. Recent studies also indicate that external shocks, including the COVID-19 pandemic, have reinforced health-oriented consumption behaviours and altered demand patterns in favor of organic products [23].

A review of the literature suggests that effective demand forecasting for organic products requires a comprehensive approach that includes analysis of economic, social, and cultural factors, as well as the use of diverse statistical and analytical methods. Estimating consumer demand and forecasting organic sales, therefore, requires considering changes in socio-economic conditions, environmental trends, and consumer behaviour.

Based on the reviewed literature, the empirical analysis is guided by three conceptual expectations. First, consumer demand for organic products is expected to be positively influenced by sustainability-related attitudes, including environmental awareness and trust in organic certification. Second, economic accessibility factors, such as income and price sensitivity, are expected to condition purchasing behaviour. Third, digital communication tools (social media, online reviews, and digital promotion) are expected to enhance consumer awareness and strengthen demand for organic products. These expectations are empirically examined through regression and factor-based modelling.

Thus, combining sustainable development and digital marketing approaches in forecasting demand for organic agricultural products is a relevant and promising area of scientific research. It allows not only the identification of key economic and behavioral factors, but also the development of effective demand models aimed at achieving sustainable economic growth and ecological balance.

Despite the growing body of literature on organic food consumption, several research gaps remain. Existing studies often examine economic determinants of demand, sustainability motivations, or digital marketing effects in isolation. There is limited empirical research that integrates these dimensions within a single analytical framework, particularly studies that combine quantitative demand modelling with consumer survey data. Moreover, comparative cross-country analyses that consider differences in income levels and market maturity remain underexplored.

In response to these gaps, the objective of this study is to model consumer demand for organic agricultural products by integrating economic, social, behavioural, and digital marketing factors. Specifically, the study aims to identify key determinants influencing consumers’ purchasing decisions, assess the role of digital marketing in shaping sustainable consumption patterns, and develop demand forecasts and managerial implications based on comparative evidence from Ukraine and Slovakia. This approach contributes to balancing consumer accessibility, market growth, and environmental sustainability.

2. Materials and Methods

To achieve the research objective of identifying factors influencing organic product purchases, forecasting sales volumes, and developing marketing strategies aligned with the principles of sustainable development and digital marketing, an integrated set of interrelated methods was applied. Economic (income, prices, and food expenditure) and social (age, education, and purchase motives) variables were included in the model as contextual control factors that shape sustainable consumption patterns and condition the effectiveness of digital marketing tools, rather than as independent analytical dimensions.

The study employed a non-probability purposive sampling approach, targeting adult consumers who had purchased organic agricultural products at least once within the previous 12 months. Respondents were recruited through online channels, including social media platforms. Participation was voluntary and anonymous, and only respondents who confirmed prior experience with purchasing organic products were included in the final sample. For the purposes of this study, organic product consumers were defined as individuals who consciously purchase certified organic food products for personal or household consumption. Sample diversity was ensured by including respondents from different age groups, income levels, educational backgrounds, and places of residence in both countries. The final sample size of 423 respondents was considered sufficient for factor and regression analysis, in line with commonly accepted methodological recommendations for survey-based consumer research.

First, an online survey was conducted among 423 consumers of organic products between September and October 2024. A total of 517 questionnaires were collected, of which 423 were retained for analysis after data screening and the removal of incomplete responses, resulting in an effective response rate of approximately 82%. The questionnaire covered the level of awareness of organic products, certification and environmental benefits; the impact of digital marketing (social networks, online advertising, online reviews and testimonials); and key choice factors, including quality, freshness, packaging, delivery, price, and trust in information sources.

Table 1. Measurement constructs and example questionnaire items.

Measurement Constructs	Example Questionnaire Items	Source
Awareness of organic products	I am well informed about the benefits of organic food. I understand the differences between organic and conventional food products. I am familiar with the main characteristics of organic agricultural products	Adapted from prior studies
Trust in certification	Organic certification increases my trust in the product. I believe that certified organic products meet established quality standards. Certification labels help me distinguish organic products from conventional ones.	Literature-based
Influence of digital marketing	Online advertising influences my decision to purchase organic products. Social media content affects my perception of organic product quality. Online reviews and recommendations influence my choice of organic products.	Literature-based
Purchase intention	I intend to buy organic products regularly in the future. I am likely to increase my consumption of organic products. I would recommend organic products to others.	Literature-based
Environmental concern	Environmental considerations influence my food choices. I prefer products that are produced in an environmentally friendly way. I am willing to support sustainable agriculture through my purchasing decisions.	Literature-based
Price sensitivity	The high price of organic products limits my purchasing frequency. I would buy organic products more often if they were cheaper. Price differences strongly influence my choice between organic and conventional products.	Literature-based

summarizes the measurement constructs and selected example items used in the questionnaire. All attitudinal items were measured using a five-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree).

Respondents represented diverse age groups, income levels, educational backgrounds, and both urban and rural areas in Ukraine and Slovakia. Although the sample was divided by country, the analysis is exploratory in nature, and factor structures were derived from the pooled dataset, while country-level regressions are interpreted as context-specific estimations rather than high-powered inferential models. Although the sample is not statistically representative of the entire population, its analytical representativeness is ensured by the diversity of socio-demographic characteristics, which allows for reliable comparative and exploratory analysis consistent with the study objectives. The survey allowed not only the identification of priority factors, but also the assessment of the impact of digital marketing on consumer decisions.

Content validity of the questionnaire was ensured through alignment with established studies on organic food consumption and sustainable consumer behaviour. The internal consistency of the main multi-item constructs was assessed ex post, yielding acceptable reliability levels, with Cronbach’s alpha coefficients exceeding 0.70, which confirms the reliability of the survey instrument.

Second, factor analysis and survey-based reduced-form regression models were applied to identify key groups of variables influencing individual-level consumer decision-making. Prior to factor analysis, sampling adequacy was assessed. The Kaiser–Meyer–Olkin measure exceeded the recommended threshold of 0.60, and Bartlett’s test of sphericity was statistically significant (p < 0.001), confirming the suitability of the data for factor analysis. The internal consistency of the main multi-item constructs was again evaluated using Cronbach’s alpha, with all values exceeding 0.70, indicating acceptable reliability. Regression analysis enabled the assessment of how income and food expenditures, relative prices of organic versus conventional products, information channels (especially digital marketing), and environmental awareness shape consumer demand. Given the high explanatory power of the regression models, multicollinearity diagnostics were conducted using the Variance Inflation Factor. All predictors exhibited VIF values below conventional critical thresholds, indicating that multicollinearity was not severe and did not distort coefficient estimates. The model specification was guided by theoretical considerations to avoid overfitting and ensure interpretability. A functional relationship between population income and the average price of organic products was constructed, explicitly accounting for the price premium of organic products relative to conventional alternatives. Sustainability-related attitudes were captured through survey items reflecting environmental concern, perceived environmental benefits of organic products, and trust in organic certification. Based on these items, a composite indicator (Eco) was constructed for the Slovak sample and introduced into the regression model as a proxy for consumers’ sustainability orientation. In the Ukrainian model, sustainability attitudes were included within factor-based constructs and therefore not entered as a separate variable to prevent multicollinearity.

Third, behavioural demand modelling was carried out using survey-based reduced-form econometric models, integrating socio-economic, sustainability-related, and digital factors. The main characteristics of the modelling included survey data and statistical observations on income, food expenditure, and prices of organic and conventional products; consideration of the impact of digital channels (social networks, online advertising, and reviews) on demand; and the integration of sustainability principles, such as consumer preferences for environmentally friendly products and ethical production practices. This approach enabled the development of demand forecasting scenarios under different economic, social, and digital marketing conditions. Demand forecasting was conducted using statistical regression models. The forecasts were based on historical data and accounted for changes in income, prices, and socio-environmental factors affecting consumer choice.

The matrix method was used to visualize and assess the combined impact of economic factors (price, income, organic premium); social factors (consumer preferences and awareness); environmental factors (attitudes toward sustainable development and environmental certification); and digital factors (the influence of social networks, online advertising, and online reviews). The matrix facilitated the identification of priority factors and the selection of effective marketing strategies.

The integrated use of survey data, factor and regression analysis, behavioural modelling, matrix analysis, and forecasting enabled the identification of key demand drivers and sales projections for organic products. This provides a more comprehensive understanding of the organic market and allows for the development of marketing strategies for enterprises engaged in the production and sale of organic products.

The primary data sources were consumer surveys and official statistical observations. Statistical data covered information on population income levels, the structure of food expenditures, and consumer habits. Agrarian reports and sectoral studies summarized the current state of the organic products market and development trends in the organic production sector. Excel was used for data preprocessing and descriptive statistics, while inferential statistical analyses, including factor analysis, regression modelling, and reliability testing, were performed using specialized statistical software. Online analytical tools, including Google Analytics (GA 4), were applied to monitor digital trends and consumer behaviour based on data collected from online sources. Prior to analysis, the collected data were verified for consistency and errors by cross-checking respondents’ answers and comparing results with secondary data sources, thereby enhancing the reliability of the findings.

3. Results

A survey of organic consumers represents one of the key stages in analysing consumer demand for organic agricultural products in Ukraine and Slovakia. This stage enabled the collection of detailed information on consumer habits, preferences, and factors influencing the choice of organic products, as well as insights into purchasing motivation. The survey was conducted using a structured questionnaire. The questionnaire was designed to collect data relevant to identifying the determinants of organic product purchases and was organized into five thematic blocks. The first block covered demographic characteristics, including age, gender, education level, place of residence, and income. The second block focused on consumer habits, such as purchase frequency, categories of organic products purchased, purchase locations, and sources of information about new organic products. The third block examined purchase motives, identifying the most important factors influencing product choice. The fourth block addressed consumer behaviour and pricing, including price sensitivity, perceptions of the price–quality relationship, and willingness to pay a price premium for organic products compared with conventional alternatives. The fifth block assessed consumer awareness and education, focusing on knowledge of organic certification and perceived product benefits. The main results of the survey are presented in

Table 2. Main results of the survey of organic food consumers in Ukraine and Slovakia.

Indicator	Ukraine	Slovakia	Trends
Age groups	18–35: 41%	18–35: 44%	Younger consumers demonstrate a higher propensity to purchase organic products, which is consistent with prior studies highlighting stronger health awareness and environmental attitudes among younger age groups [2,4,23].
	36–55: 37%	36–55: 32%
	56+: 24%	56+: 23%
Income Level	Low: 28%	Low: 24%	Higher income levels are associated with a greater willingness to pay for organic products, as documented in prior studies identifying income as a primary factor mitigating price sensitivity in organic food markets [4,5,6].
	Average: 43%	Average: 48%
	High: 29%	High: 28%
Place of residence	City: 71%, Village: 29%	City: 66%, Village: 34%	Organic product consumption is more prevalent among urban consumers, in line with previous research emphasizing the role of market accessibility and information availability in cities [16,19].
Educational level	Secondary: 19%, Higher: 79%	Secondary: 16%, Higher: 84%	Consumers with higher education levels show greater engagement in organic food purchases, consistent with earlier research emphasizing the role of education in shaping sustainable consumption patterns [2,22].
Purchase frequency	Several times a week: 25%	Several times a week: 32%	Occasional buyers constitute the dominant consumer group, reflecting prior findings that identify price and availability as key constraints on regular organic purchases [2,5,19].
	Once a month: 36%	Once a month: 36%
Product categories	Fruits/vegetables: 62%	Fruits/vegetables: 65%	The predominance of fruits and vegetables in organic purchases aligns with earlier evidence indicating that consumers are more inclined to choose organic alternatives for fresh products with direct health implications [2,16,23].
	Dairy products: 46%	Dairy products: 54%
	Meat/poultry: 24%	Meat/poultry: 37%
Reasons for purchase	Quality/Usefulness: 76%	Quality/Usefulness: 82%	Health and quality emerge as the dominant purchase motives, consistent with prior research emphasizing their central role in organic food choice [2,6,21].
	Environmental friendliness: 54%	Environmental friendliness: 63%
	Naturalness of the product: 45%	Naturalness of the product: 57%
Willingness to pay and pricing	Willingness to pay more: 42%	Willingness to pay more: 54%	Willingness to pay for organic products increases with income level, corroborating previous studies that identify income as a major determinant of organic food demand [1,4,5].
	Price impact on decision: high	Price impact on decision: high
Awareness of certificates	Yes: 55%, No: 45%	Yes: 65%, No: 35%	Consumer awareness of organic certification is consistent with prior research highlighting certification as a key trust-building mechanism in organic product choice [1,2,16].
How to find out about organic products	Internet/social media: 58%	Internet/social media: 62%	Online sources dominate information search for organic products, in line with previous research emphasizing the growing influence of digital communication channels [9,10,11].
	Advertising/stores: 32%	Advertising/stores: 24%

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The sample of respondents from Ukraine consisted of 232 individuals, while the sample from Slovakia included 191 respondents. The demographic characteristics of the sample were as follows: age groups (18–35, 36–55, and 56+), income level (from low to high), place of residence (both urban and rural areas), and educational level (from secondary to higher education). After data collection, the questionnaires were analysed using Excel 2019 (version 16.0) for data preprocessing and descriptive statistical analysis. For each question, frequency distributions and bivariate correlation analyses were calculated. The frequency analysis indicates that regular purchases of organic products remain relatively limited in both countries, with many consumers purchasing organic food occasionally rather than on a weekly basis. Bivariate correlation analysis reveals a positive association between income level and willingness to pay; however, these correlations are interpreted as descriptive indicators only, rather than evidence of causal relationships. Specifically, higher income levels are associated with a greater willingness to pay for organic products. In addition, a negative correlation was identified between respondents’ age and the frequency of organic product purchases, indicating that younger consumers demonstrate higher purchase frequency. This pattern suggests the influence of healthy lifestyle trends that are more prevalent among younger age groups. Overall, the correlation analysis confirms that income level is a key driver of consumer choice in the organic products market, as wealthier consumers exhibit a higher willingness to pay for organic products. At the same time, younger consumers show greater interest in organic products, further supporting the role of health-oriented consumption patterns.

As part of the study of demand for organic products in Ukraine and Slovakia, data on household income levels and food expenditures, prices of organic and conventional products, and the dynamics of organic product demand were compared. The results of this comparative analysis are presented in

Table 3. Analysis of demand in the market of organic agricultural products in Ukraine and Slovakia [24,25,26,27,28,29].

Evaluation Criteria	Ukraine	Slovakia
Estimate of income and food expenditure	According to 2023 statistical data, the average monthly household income in Ukraine was approximately UAH 12,000. Food expenditures accounted for about 40–45% of total household spending. Moreover, expenditure on organic products represented only 5–7% of total food spending, indicating limited consumer access to organic products, primarily due to their higher prices.	The average monthly household income in Slovakia in 2023 was approximately EUR 1500. Food expenditures accounted for about 25–30% of total household spending. The share of organic products in food consumption reached 10–12%, indicating higher availability of organic products and a greater willingness to pay compared with Ukraine.
Prices for organic and conventional products	Organic food prices in Ukraine are typically 25–50% higher than those of conventional products. For example, organic vegetables cost, on average, about 30% more than their conventional counterparts, while organic dairy products are approximately 40% more expensive. This price differential constitutes a significant barrier to the widespread consumption of organic products, particularly under conditions of limited household incomes.	In Slovakia, organic products are also priced above conventional alternatives, but the price differential is relatively smaller. For example, organic vegetables are typically 15–20% more expensive than conventional ones. This more moderate price premium allows a larger share of the population to access organic products.
Dynamics of demand for organic products	Compared with 2018, demand for organic products in Ukraine has increased by approximately 10–15%. However, most consumers purchase organic products only occasionally rather than regularly. This pattern is confirmed by statistical data on retail sales volumes, which indicate that the share of organic products in total food sales remained at approximately 5–7% in 2023.	Demand for organic products in Slovakia has been growing more rapidly, increasing by approximately 20–25% over the same period. This growth is driven by higher consumer awareness, higher income levels, and improved market availability of organic products. As a result, the share of organic products in the total food supply in Slovakia reached 12–15% in 2023.

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To assess whether the observed differences between Ukraine and Slovakia are statistically significant, formal cross-country tests were conducted. Independent-sample t-tests were applied to continuous variables, while chi-square tests were used for categorical variables. The results indicate that differences in income level, willingness to pay for organic products, and awareness of organic certification are statistically significant (p < 0.05), whereas some demographic differences are not statistically significant.

Based on the analysis, the main factors influencing demand for organic products were identified, including consumers’ economic status, the price barrier, and the level of awareness. In countries with higher income levels (such as Slovakia), demand for organic products is significantly higher than in countries with lower income levels (such as Ukraine). This is confirmed by statistical evidence on organic food expenditures, indicating that higher-income consumers are willing to spend more on organic products. The high price level remains the primary barrier to organic product consumption in Ukraine. Even increasing awareness and a growing preference for healthier food choices do not always overcome this barrier, particularly among low- and middle-income consumers. Over time, consumer awareness of the benefits of organic products has increased in both Ukraine and Slovakia, a trend that is especially pronounced among younger consumers, who actively seek information related to healthy lifestyles.

Thus, income level has a significant effect on demand for organic agricultural products, as higher income is associated with greater affordability and a stronger willingness to pay, a finding supported by statistical evidence. In addition, price remains a key determinant of demand: the high cost of organic products constitutes a major barrier to wider consumption, particularly in Ukraine, where the share of spending on organic food remains relatively low compared with conventional alternatives. Finally, evolving consumer trends also shape demand, with rising awareness and interest in organic products observed in both countries; however, demand growth is faster in Slovakia due to more favourable economic conditions and greater market availability. These findings provide a basis for formulating policy and managerial recommendations, including the need to reduce price barriers, increase product availability, and enhance consumer awareness of the benefits of organic production.

Modelling consumer demand for organic agricultural products in this study is based on a survey-based reduced-form demand model, which captures behavioural relationships between individual-level demand intensity and its key determinants, such as price perception, income level, sustainability-related attitudes, and marketing exposure. The selection of appropriate modelling methods is critically important for forecast accuracy and the development of effective strategies in the organic products market. Linear regression is employed as a behavioural demand modelling approach, allowing the identification of directional and relative effects of key factors on individual-level demand indicators, rather than estimating a structural macroeconomic demand function. Using this method, we can assess how changes in these factors affect demand:

$${\mathrm{Q}}_{\mathrm{d}}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1\mathrm{P}+{\mathsf{\beta }}_{02}\mathrm{I}+{\mathsf{\beta }}_3\mathrm{A}+\cdots +\mathsf{\epsilon },$$

(1)

where Q_d represents an individual-level indicator of demand intensity for organic products, derived from survey responses and purchase behaviour measures.

P denotes the price of the organic product.

I represent consumer income.

A captures other relevant factors (e.g., educational level or consumer awareness of organic benefits).

β₀, β₁, β₂, β₃ indicate the marginal effects of each factor on demand.

ε is the error term.

To develop a model for forecasting demand for organic products in Ukraine using linear regression, demand was specified as a function of product price, income level, seasonality, and marketing expenditures. The results of estimating the linear regression model using the ordinary least squares method are presented in

Table 4. Estimation results of the linear regression model (ordinary least squares) for the Ukrainian organic market.

Parameter	Coef	Std Err	T	P > |t|	[0.025]	[0.975]
const	50.0000	12.345	4.053	0.002	25.564	74.436
Price	−0.25	0.08	−3.125	0.010	−0.38	−0.12
Income	0.02	0.01	2.347	0.027	0.005	0.035
Marketing	0.05	0.02	2.348	0.023	0.012	0.088
Season	10.0000	5.678	1.763	0.089	−1.123	21.123

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The model for forecasting demand for organic products for Ukraine has the following form: $Y=50-0.25X_1+0.02X_2+0.05X_3+10X_4$. The linear regression model enables the optimization of production and sales decisions for organic products, as well as the development of strategies aimed at stimulating demand.

The obtained results of the regression model indicate its high explanatory power and adequacy of the model. The coefficient of determination (R-squared) equals 0.987, while the adjusted coefficient of determination (Adjusted R-squared) equals 0.973, indicating that the model explains more than 97% of the variation in the dependent variable without excessive complexity. The high value of the F-statistic (72.44), together with the very low significance level (Prob(F-statistic) = 0.00235), confirms that the model is statistically significant as a whole and that the included independent variables jointly explain the dependent variable. Based on the developed model, quarterly forecasts of demand for organic products in Ukraine for the period 2026–2030 were developed under three alternative scenarios, which are presented in

Table 5. Forecast of demand for organic food in Ukraine for 2026–2030 (million USD).

Year, Quarter	Pessimistic Scenario	Most Likely Scenario	Optimistic Scenario
2026 Q1	7.2764	7.66199	7.91654
2026 Q2	9.58093	10.0741	10.401
2026 Q3	9.37926	9.98481	10.3878
2026 Q4	6.67122	7.39413	7.87727
2027 Q1	6.45661	7.30202	7.86949
2027 Q2	8.73523	9.70848	10.3647
2027 Q3	8.50687	9.61349	10.363
2027 Q4	5.77132	7.01704	7.86472
2028 Q1	5.52837	6.9191	7.87002
2028 Q2	7.77778	9.31967	10.3791
2028 Q3	7.51932	9.21873	10.3923
2028 Q4	4.75276	6.61627	7.90983
2029 Q1	4.47784	6.51226	7.93193
2029 Q2	6.69433	8.9067	10.4589
2029 Q3	6.40194	8.79958	10.4911
2029 Q4	3.60042	6.19087	8.02872
2030 Q1	3.28949	6.08056	8.07221
2030 Q2	5.46886	8.46865	10.6219
2030 Q3	5.13824	8.35512	10.6781
2030 Q4	2.29731	5.73995	8.24124

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To forecast the demand for organic products in Ukraine for 2026–2030, a multivariate linear regression model was employed, in which the dependent variable was demand, and the independent variables included product price, average population income, marketing expenditures, and seasonality. The estimated model coefficients confirm the expected economic relationships: an increase in price reduces demand, while higher income levels and increased marketing expenditures stimulate demand. The seasonal effect results in higher demand during the second and third quarters of the year.

The forecast is conducted on a quarterly basis under three alternative scenarios. The pessimistic scenario assumes high price growth (3%), slow income growth (0.5%), and limited growth in marketing expenditures (1%). Under this scenario, demand declines or grows only marginally, and seasonal peaks are less pronounced. The most likely (baseline) scenario assumes moderate growth rates for prices (2%), income (1%), and marketing expenditures (3%), resulting in stable demand with pronounced seasonal peaks in Q2–Q3. The optimistic scenario assumes low price growth (1.5%), rapid income growth (1.5%), and strong growth in marketing expenditures (5%), leading to demand growth across all quarters, with particularly strong seasonal peaks.

The reported quarterly values represent scenario-based point estimates rather than exact predictions and should therefore be interpreted as indicative trends. For the baseline scenario, the plausible uncertainty range is estimated at approximately ±10–15%, reflecting potential volatility in prices, income dynamics, and macroeconomic conditions, particularly in transitional and unstable markets.

The results demonstrate that the scenarios primarily differ in the rate of demand growth: the pessimistic scenario yields the lowest demand levels, the optimistic scenario the highest, while the baseline scenario lies between the two extremes. Seasonal fluctuations persist across all scenarios, although their amplitude depends on the relative growth of income and marketing expenditures compared with price growth.

Regarding forecast accuracy, the model exhibits a high coefficient of determination (R² = 0.987), indicating a strong ability to explain historical demand variation. However, the accuracy of long-term forecasts is inherently limited due to extrapolation, and the results should therefore be interpreted as indicative projections, with a baseline uncertainty interval of ±10–15%. It is recommended to regularly update the model as new data becomes available and to account for potential structural changes in the market.

To develop a model for forecasting demand for organic products in Slovakia, a linear regression framework was applied, in which demand depends on product price, income level, seasonality, marketing expenditures, and environmental benefits. The results of estimating the linear regression model using the ordinary least squares method are presented in

Table 6. Estimation results of the linear regression model (ordinary least squares) for the Slovak organic market.

Parameter	Coef	Std Err	T	P > |t|	[0.025]	[0.975]
const	50.0000	10.345	4.827	0.005	35.123	64.877
Price	−0.20	0.07	−2.857	0.015	−0.35	−0.05
Income	0.03	0.01	2.567	0.027	0.008	0.052
Marketing	0.04	0.02	2.001	0.034	0.005	0.078
Season	8.0000	4.234	1.887	0.065	−0.456	16.456
Eco	0.10	0.03	3.237	0.010	0.050	0.150

.

The model for forecasting demand for organic products for Slovakia has the following form: $Y=50-0.2X_1+0.03X_2+0.04X_3+8X_4+0.1X_5$.

The resulting model demonstrates a high level of explanatory power. The coefficient of determination (R-squared = 0.952) indicates that approximately 95.2% of the variation in the dependent variable is explained by the factors included in the model. The adjusted coefficient of determination (Adj. R-squared = 0.910) is also high, confirming the stability of the model and the absence of substantial overestimation due to the number of predictors. The F-statistic (25.4) and the corresponding significance level (Prob(F) = 0.0027) indicate that the model is statistically significant overall. This implies that the included independent variables jointly exert a significant effect on the dependent variable and that the regression results are not attributable to random variation. Overall, the model is appropriately specified, exhibits strong explanatory power, and is statistically robust.

Table 7. Forecast of demand for organic food in Slovakia for 2026–2030 (million EUR).

Year, Quarter	Pessimistic Scenario	Most Likely Scenario	Optimistic Scenario
2026 Q1	152	170	185
2026 Q2	167	191	212
2026 Q3	191	222	248
2026 Q4	162	186	205
2027 Q1	149	176	196
2027 Q2	163	197	224
2027 Q3	188	229	262
2027 Q4	159	191	216
2028 Q1	147	182	208
2028 Q2	161	204	237
2028 Q3	186	236	276
2028 Q4	157	198	228
2029 Q1	145	188	220
2029 Q2	159	210	251
2029 Q3	184	243	291
2029 Q4	155	204	240
2030 Q1	143	194	233
2030 Q2	157	217	265
2030 Q3	182	250	306
2030 Q4	153	210	252

presents the forecasted demand for organic food in Slovakia for the period 2026–2030.

The constructed forecast demonstrates a strong dependence of demand for organic products in Slovakia on the trajectory of key economic and behavioural factors. The results indicate that demand in 2026–2030 may evolve along three fundamentally different trajectories, ranging from a moderate decline (pessimistic scenario) to stable growth (optimistic scenario). The pessimistic scenario assumes accelerated price growth for organic products and a low rate of household income growth. The high price sensitivity of demand, confirmed by the negative regression coefficient (β₁ = −0.20), leads to a gradual decline in demand over the forecast period. Demand levels, therefore, exhibit a moderate but steady decrease, resulting in an overall decline of approximately 7%. This outcome indicates the dominant role of the price factor under conditions where income growth and marketing activity do not sufficiently offset price increases. The baseline scenario is based on moderate macroeconomic assumptions, including stable price dynamics, a gradual increase in household incomes, and higher marketing investments. Under these conditions, both income growth (β₂ = 0.03) and increased environmental awareness (β₅ = 0.10) exert a positive influence on demand. The results indicate a steady growth trajectory, with demand increasing by approximately 17%. This scenario can be considered the most realistic, assuming that current economic and market conditions are maintained. The optimistic scenario assumes a reduction in organic product prices due to expanded supply and increased competition, combined with rapid growth in household incomes and marketing expenditures. Under these conditions, demand exhibits the highest growth rate, increasing by approximately 31%. Income growth and environmental awareness make the largest contributions to this expansion, which is consistent with global trends observed in the development of organic food markets.

Overall, the comparison shows that the development of the organic market in Slovakia is highly sensitive to price dynamics. Changes in price trajectories explain the divergence between pessimistic and optimistic scenarios to a greater extent than other variables. In general, the market demonstrates considerable growth potential; however, under adverse economic conditions, a gradual decline in demand cannot be ruled out. Based on the results, the model exhibits high statistical significance (R² = 0.952), indicating a strong relationship between the dependent and independent variables. The developed linear regression model for forecasting demand for organic products in Slovakia incorporates key determinants, including price, income level, marketing expenditures, seasonality, and environmental benefits. The model, therefore, enables reliable demand projections under alternative scenarios and can be effectively used for strategic planning in the organic sector.

For a comprehensive and integrative interpretation of the quantitative results, an exploratory matrix framework was applied as a decision-support tool to structure and summarize the key findings of the empirical analysis. Each factor was assessed according to two criteria: the strength of impact (high, medium, low) and the feasibility of intervention, defined as the practical possibility of influencing demand through marketing or policy actions.

Accordingly, economic factors were evaluated using the following parameters: the price of organic products, consumer income, and the ratio between the organic price premium and marketing expenditures. The price of organic products exhibits high demand sensitivity to price changes (especially in Ukraine) and was therefore assessed as high impact–medium feasibility. Consumer income demonstrates a direct positive effect on willingness to purchase organic products and was assessed as high impact–high feasibility, particularly in Slovakia, where income levels allow for increased spending on organic food. The organic premium/marketing expenditure ratio contributes to demand stimulation and was assessed as medium impact–high feasibility, as it can be adjusted through advertising, promotion, and educational campaigns. Social factors were assessed based on consumer preferences and habits, as well as consumer awareness and education. Consumer preferences and habits indicate that younger consumers are more inclined to purchase organic products, especially when relevant information is readily available; this factor was rated as medium impact–medium feasibility. Consumer awareness and education reveal a growing level of knowledge regarding the benefits of organic products, which has a positive effect on demand; this factor was rated as medium impact–high feasibility, as it can be effectively reinforced through educational initiatives and media campaigns. Environmental factors were defined in terms of attitudes toward sustainable development and certification, as well as perceived environmental benefits. Awareness of sustainability principles and organic certification increases the likelihood of choosing organic products and was therefore rated as high impact–medium feasibility. The environmental benefits factor exerts an additional positive influence in Slovakia and was assessed as medium impact–high feasibility, particularly through targeted communication via media and social networks. Digital factors include the influence of social networks and online advertising, as well as online reviews and ratings. Social networks and online advertising serve as effective channels for shaping positive attitudes and building trust in organic products and were rated as medium impact–high feasibility. Online reviews and ratings contribute to brand reputation formation and influence purchasing decisions; this factor was assessed as medium impact–medium feasibility.

The matrix is based on two qualitative dimensions: impact on demand, reflecting the relative strength of effects observed in the regression and descriptive analyses, and feasibility of influence, reflecting the practical ability of firms to affect a given factor through pricing, marketing, or communication strategies. The high/medium/low categories represent relative rather than absolute evaluations. All assessments were conducted by the authors based on the empirical results of the study and prior literature, with the aim of ensuring internal consistency across factors. The integrated assessment is presented in the form of a matrix summarizing the impact of key factors on demand for organic products (

Table 8. Exploratory matrix framework for structuring key factors influencing demand for organic products.

Group of Factors	Factor	Impact on Demand	Realization of Impact	Features
Economical	Price of organic products	High	Average	Especially critical in Ukraine
	Consumer incomes	High	High	Strong stimulus in Slovakia
	Organic/Marketing Award	Average	High	Can be adjusted through advertising and promotions
Social	Consumer preferences and habits	Average	Average	Young people buy organic products more often
	Awareness and education	Average	High	Reinforced by educational and media campaigns
Environmental	Attitude towards sustainable development and certification	High	Average	Informed consumers are more likely to choose organic
	Environmental benefits	Average	High	Especially important in Slovakia
Digital	The impact of social media and online advertising	Average	Average	Builds a positive image and trust
	Online reviews and ratings	Average	High	Influences purchase decisions

).

Thus, the impact on demand is assessed as high, medium, or low, reflecting the degree of influence on consumers’ decisions to purchase organic products. The feasibility of impact evaluates the practical possibility of influencing demand through marketing, economic, or educational measures.

The matrix method enabled the systematization of key economic, social, environmental, and digital factors and the identification of conditions for maximizing demand for organic agricultural products. The constructed matrix facilitated the identification of five strategic zones, each representing a distinct set of solutions for producers and marketers.

The first strategy–price accessibility enhancement (economic factors: high; social factors: medium) addresses the high price level and limited purchasing power of certain consumer groups as the main barriers. Key actions include cost optimization and efficiency improvements in production; expanding the range of basic organic products at lower prices; flexible pricing instruments (discounts, seasonal offers, loyalty programs); cooperation with retail chains to reduce marketing costs. This strategy aims to reduce the price barrier and expand coverage among middle-income consumers.

The second strategy–building awareness and trust (social factors: high; environmental factors: medium) recognizes the decisive role of social motivation and consumer values, while noting insufficient understanding of the environmental value of organic products. Strategic actions include educational campaigns on environmental benefits; greater transparency of product origin (certification, standards, farming practices); partnerships with educational and public organizations; and content focused on healthy lifestyles and consumer well-being. This strategy reduces information asymmetry and strengthens brand trust.

The third strategy–ecological differentiation (environmental factors: high; economic factors: medium) focuses on enhancing competitiveness through ecological uniqueness and compliance with sustainable development principles. Core actions include expanding product ranges with clearly articulated environmental value; “green marketing,” certification, and labelling to verify sustainability; communication of low carbon footprint, naturalness, and locality; and investment in sustainable packaging and closed-loop production processes. This strategy positions organic products as goods with added environmental value.

The fourth strategy—digital personalization and engagement (digital factors: high; social and economic factors: medium) establishes a strategic zone centred on personalization and active consumer interaction driven by digital channels. Key initiatives include targeted online advertising by consumer segment; personalized marketing messages based on behavioural data; engagement via social networks, influencer marketing, and interactive content; use of big data analytics and machine learning for demand forecasting and supply adaptation; and development of omnichannel sales platforms (online stores, mobile applications). This strategy aims to build long-term brand–consumer relationships in the digital environment.

The fifth strategy–an integrated sustainable digital development strategy (all factors: high) represents the most promising approach, emerging when economic, social, environmental, and digital factors are simultaneously significant. This strategy emphasizes synchronization of traditional and digital marketing; promotion of sustainable practices across all communication channels; creation of a brand ecosystem combining environmental value, digital convenience, and social orientation; use of digital tools to monitor environmental footprint, quality control, and customer communication; and full supply chain transparency. It aligns most closely with current trends in sustainable consumption and digitalization of the agricultural sector.

The application of this comprehensive approach enabled a systematic assessment of how economic, social, environmental, and digital factors shape demand for organic agricultural products in Ukraine and Slovakia and facilitated the identification of five strategic directions for market development. The identified strategies demonstrate that different factor combinations yield distinct pathways for stimulating consumption—from affordability and trust-building to ecological differentiation and digital personalization. The integrated sustainable digital development strategy emerges as the most promising, delivering synergies across traditional and digital channels, environmental orientation, and social value. Overall, the results confirm the effectiveness of the combined method as a strategic planning tool and support the formulation of targeted solutions for producers and marketers amid the continued growth of the organic products market.

4. Discussion

The observed differences between Ukraine and Slovakia can be interpreted in light of broader socioeconomic and institutional contexts. Slovakia represents a more mature EU organic market, characterized by higher average income levels, stronger institutional support for organic certification, and more established consumer trust in quality labels. These conditions help explain the higher willingness to pay and greater awareness of organic certification observed among Slovak respondents.

In contrast, Ukraine represents a transitional market characterized by lower average incomes, higher price sensitivity, and less institutionalized consumer protection mechanisms. In this context, economic constraints play a more dominant role in shaping demand, and sustainability-related motivations tend to be conditional on affordability. Cultural factors, such as lower trust in formal certification systems and uneven exposure to sustainability-related communication, may further contribute to the observed cross-country differences.

The results of the study confirm that demand for organic agricultural products is shaped by a combination of economic, socio-ecological, and digital factors, which is consistent with findings in the contemporary literature. The survey data demonstrate that the primary consumer motives remain product quality, safety, and environmental benefits, while price continues to act as a constraining factor. These results are aligned with the findings of Bernabéu et al. and Jarossova et al., who emphasize the heterogeneity of demand depending on income level and environmental awareness [4,5].

Factor analysis revealed distinct clusters of variables reflecting consumers’ economic conditions, environmental orientation, and trust in digital information sources. This supports the multidimensional nature of demand, which necessitates the use of integrated forecasting models. Regression estimates indicate a significant impact of income and relative price levels between organic and conventional products, as well as the importance of intangible factors, including consumer awareness and perceived environmental benefits. These findings are consistent with the work of Hansmann et al. and Zheng et al., who highlight the growing role of environmental motivation in shaping consumer behaviour [1,2].

In addition, the identified role of digital marketing is particularly noteworthy. The data show that social networks, recommendations, and online reviews enhance consumer trust in organic products and stimulate purchasing decisions. This confirms the findings of Singh & Glińska-Neweś and You et al. regarding the importance of digital communication channels in promoting sustainable consumption patterns [10,11]. Thus, digital marketing not only increases awareness but also drives behavioural change, making it a key instrument in organic market development. Differences in the role of digital marketing between the two countries can be attributed to varying levels of digitalization and media consumption habits. In Slovakia, digital channels primarily reinforce existing awareness and trust, whereas in Ukraine, they perform a more educational function, compensating for information gaps and limited offline access to organic markets. These patterns are consistent with differences in digital infrastructure, market transparency, and consumer experience with online information sources.

The results of behavioural demand modelling indicate that income growth and digital communication intensity are associated with more favourable demand trajectories at the individual level, which subsequently translate into positive scenario-based projections. Conversely, price increase scenarios reveal substantial demand sensitivity, confirming the high price elasticity of middle-income consumer segments. The combined inclusion of economic and behavioural variables in the model allows for more accurate demand forecasts, which is consistent with advanced modelling approaches, including those discussed by Li et al. and Yuan et al. [13,18].

Overall, the findings indicate that effective development of the organic market requires the integration of sustainable development principles and digital marketing tools. While growing environmental awareness and the expansion of digital communication channels create favourable conditions for market growth, economic constraints remain substantial. This underscores the need for balanced strategies that combine product affordability, clear communication of environmental value, and innovative promotional instruments.

Thus, the study confirms the interdependence of economic, socio-ecological, and digital factors in shaping demand for organic agricultural products and highlights the importance of an integrated approach to forecasting and strategic planning in this market.

5. Conclusions

5.1. Theoretical Contributions

From a theoretical perspective, this study contributes to the literature on organic product demand by integrating economic, sustainability-related, and digital marketing factors within a unified analytical framework. Unlike prior studies that examine these drivers in isolation, the research demonstrates how income, price sensitivity, environmental attitudes, and digital communication jointly shape consumer demand. In addition, the comparative perspective between a mature EU market (Slovakia) and a transitional market (Ukraine) extends existing knowledge on how institutional and socioeconomic contexts condition sustainable consumption behavior.

5.2. Practical Implications

The findings offer several important practical implications. For policymakers, the results highlight the importance of strengthening consumer trust in organic certification systems and supporting awareness-raising initiatives that emphasize environmental and health benefits, particularly in transitional markets. Policy measures aimed at reducing price barriers—such as targeted subsidies, support for domestic organic producers, or improved market access—may significantly stimulate demand among middle-income consumer groups.

For businesses, the results suggest that pricing strategies should be complemented by transparent communication of sustainability value. Digital marketing tools, including social media platforms and online reviews, play a crucial role in shaping consumer perceptions and should be strategically leveraged to build trust and long-term customer loyalty. In price-sensitive markets, educational and informational digital content appears particularly effective in supporting demand growth.

5.3. Study Limitations

This study has several limitations that should be acknowledged. First, the analysis relies on available statistical data, which may involve time lags and may not fully capture the dynamics of informal, shadow, or uncertified market segments. Second, demand modelling is based partly on aggregated indicators, which may obscure regional heterogeneity and specific behavioural patterns of individual consumer groups. Third, the matrix framework employed in the study is qualitative and exploratory in nature. Although it provides a structured interpretation of key factors, it does not constitute a formal quantitative evaluation and does not account for potential nonlinear interactions characteristic of contemporary sustainable consumption markets. Accordingly, the matrix results should be interpreted as illustrative and supportive, rather than as statistically testable evidence. Finally, the comparative analysis is constrained using survey-based data and a limited set of socioeconomic indicators. Cultural and institutional differences between Ukraine and Slovakia are discussed conceptually rather than measured directly, which should be considered when interpreting cross-country contrasts. Moreover, despite the application of formal significance tests, the findings should be viewed considering the exploratory research design and the non-probability sampling approach.

5.4. Directions for Future Research

Future research could build on this study by applying longitudinal or panel data to better capture changes in consumer behaviour over time. Further studies may also employ discrete-choice experiments or experimental research designs to deepen the analysis of willingness to pay and causal mechanisms. Expanding the comparative framework to include additional countries would allow for a more nuanced understanding of how cultural and institutional factors influence sustainable consumption patterns.