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Frontiers in Sustainable Food Systems ·

Volume 10 - 2026 | https://doi.org/10.3389/fsufs.2026.1815057

Drivers of organic food purchase intentions in India: implications for sustainable food systems using PLS-SEM and fsQCA

  • Ruqaiya Firdaus1
  • Zeba Danish2
  • Omama Zaman1
  • Areiba Arif3*corresponding author
  • Wajahat Mazahar Khan4
  • Ahmad Fallatah5
  • Shabana Urooj6
  1. 1Department of Business Administration, Aligarh Muslim University, Aligarh, India
  2. 2School of Business, University of Petroleum and Energy Studies (UPES), Dehradun, India
  3. 3Jindal Global Business School, O.P. Jindal Global University, Sonipat, Haryana, India
  4. 4Jindal School of Government and Public Policy, O.P. Jindal Global University, Sonipat, Haryana, India
  5. 5Future Mobility Institute, King Abdulaziz City for Science and Technology, Riyadh, Saudi Arabia
  6. 6Department of Electrical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
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Abstract

The growing interest in organic food is driven by heightened consumer awareness of health, food safety, and environmental sustainability. In India, rising income levels and lifestyle changes have accelerated this shift. This study explores the factors that affect consumers’ willingness to buy organic food by examining food safety concerns, natural content, nutritional concerns, health consciousness, attitude, and religion. The survey was conducted among 256 potential consumers in the Delhi NCR region using convenience sampling. A combined methodology was utilised, incorporating partial least squares structural equation modelling (PLS-SEM) alongside fuzzy-set qualitative comparative analysis (fsQCA) to gain both symmetric and asymmetric insights. PLS-SEM was employed to analyse the direct effects and validate the theoretical framework. Furthermore, fsQCA was employed to explore the necessary conditions and configurations of variables that lead to high purchase intention. PLS-SEM results demonstrate that concerns related to food safety, health consciousness, and nutritional concerns significantly impact purchase intentions. The fsQCA results reveal that health consciousness and attitude are necessary conditions, while various combinations serve as sufficient configurations, thereby supporting the principle of equifinality. These findings suggest that consumers’ intentions are shaped not only by individual drivers but also by the complex interplay of multiple factors. This study offers valuable insights for policymakers and marketers to design targeted, multidimensional strategies to promote organic food consumption.

Keywords

1 Introduction

In recent years, consumer preferences in the global food industry have shifted toward sustainable food choices (Lafram et al., 2024). Growing public awareness of health, food safety, and environmental issues has led to increased demand for eco-friendly products, particularly organic foods (Sedera et al., 2023; Azzurra et al., 2019). Consumers today are knowledgeable enough to select the right nutritious food, especially organic food items, thanks to the rise in living standards and education levels (Suleman et al., 2021). Such foods are grown or processed without pesticides, mineral fertilizers, or other chemicals (Singh and Verma, 2017). An increasing desire for food free of pesticides and chemical residues has been identified as an essential factor in consumers’ increased interest in organic food (Sedera et al., 2023). The major organic food categories are cereal food grains, organic meat, poultry, dairy, organic spices, pulses, organic processed food, and organic beverages. Beyond individual dietary choices, consumer demand for organic food plays a critical role in shaping sustainable food systems by influencing farming practices, certification regimes, supply chain investments, and economic viability of organic producers. Demand-side preferences are increasingly recognised as central to food system transitions because they affect how sustainability-oriented production models scale and stabilise within emerging economies.

Studies focusing on organic food in China, South Korea, and Thailand have increasingly examined the benefits of organic food consumption (Yang et al., 2014). At the global level, the organic food market was valued at approximately US$ 227.5 billion in 2023 and is projected to surpass US$ 510 billion by 2032, with a compound annual growth rate (CAGR) of 11.2% (Swar, 2024). In India, the organic food sector is expanding rapidly, driven largely by rising health consciousness among consumers (Kushwah et al., 2019). Growing concerns regarding nutritional value and food quality have contributed to increased demand for organic products. This trend is further supported by strong economic growth, rapid urbanisation, and rising income levels, which have collectively increased consumer expenditure on health and wellness products. Consistent with this, India’s organic food market is expected to grow at a CAGR of 25.25 per cent between 2022 and 2027 (Gandhi, 2025).

Beyond these market trends, recent studies highlight that technological platforms, product attributes, and consumer values are increasingly shaping organic food purchase intentions in both offline and online contexts (Khan et al., 2025; Putra et al., 2023). The literature also identifies multiple dimensions of consumer behavior, with integrative models emphasising both drivers and barriers influencing organic food consumption (Hansmann et al., 2020). Consumers often perceive organic foods as more nutritious, safer, and environmentally friendly than conventional alternatives (Shaharudin Rizaimy et al., 2010a,b). More recent research indicates that organic food purchase intention is shaped by the combined influence of health consciousness, environmental values, and consumer attitudes, with these factors jointly influencing intention formation within frameworks such as the theory of planned behavior (TPB). This underscores the multidimensional nature of decision-making in this domain (Khan et al., 2023; Li and Shan, 2025; Pant et al., 2024a,b).

Importantly, these drivers do not operate in isolation but interact in complex ways to influence consumer intentions, pointing to the need for approaches that capture such configurational effects (Pant et al., 2024a). While prior studies have identified key determinants of organic food purchase intention (Singh and Verma, 2017; Bazhan et al., 2024; Leyva-Hernández et al., 2025), there remains limited empirical evidence on how multiple factors combine to shape consumer decisions, particularly in emerging markets such as India. Existing research has largely relied on techniques including multiple regression (Kapuge, 2016), covariance-based structural equation modelling (CB-SEM) (Yadav and Pathak, 2016), and partial least squares structural equation modelling (PLS-SEM) (Singh and Alok, 2022), which focus on net effects and tend to overlook the causal complexity inherent in consumer behavior. In parallel, recent market trends and policy emphasis on sustainable consumption in India further highlight the need to move beyond isolated predictors and develop a more nuanced understanding of organic food demand.

Addressing this gap, the present study advances the literature by integrating variance-based modelling, specifically PLS-SEM, with a configurational approach, namely fuzzy-set qualitative comparative analysis (fsQCA), to capture both symmetric and asymmetric causal relationships. In doing so, it responds to the limited use of configurational methods in identifying the necessary and sufficient conditions underlying organic food purchase intention in India. By applying this integrated framework in the Indian context, the study enhances both methodological rigour and practical relevance by accounting for the complexity of consumer decision-making shaped by diverse socio-cultural and market conditions. This study has three objectives. First, it examines the influence of key factors on Indian consumers’ purchase intentions towards organic foods. Second, it identifies the necessary conditions associated with purchase intention. Third, it uncovers specific configurations of factors that lead to high levels of purchase intention using a configurational approach.

The proposed framework was developed by synthesising empirical evidence on the antecedents of organic food purchase intention and deriving the study hypotheses.

2 Research variables and hypotheses development

2.1 Theory of planned behavior

The TPB provides the theoretical foundation for this study by explaining purchase intention through the combined influence of attitude, subjective norms, and perceived behavioral control (Ajzen, 1985). In the context of organic food, perceived behavioral control influences consumers’ perceived ability to make purchasing decisions (Suryavanshi et al., 2023). TPB has been widely applied in Indian organic food research (Kamboj et al., 2023), making it an appropriate framework for examining the effects of food safety concern, natural content, nutritional concern, health consciousness, attitude, and religious beliefs on purchase intention. Furthermore, by integrating PLS-SEM with fsQCA, this study extends the conventional TPB perspective by examining how different combinations of these factors jointly shape purchase intention rather than assuming only independent linear relationships (Garg et al., 2024).

2.2 Food safety concern (FS)

Food safety concern is a well-established driver of organic food consumption because consumers are increasingly concerned about chemical residues, pesticides, artificial additives, and preservatives associated with conventional farming practices (Michaelidou and Hassan, 2008). This growing concern has heightened demand for transparency regarding food production processes and quality, particularly in emerging economies where awareness of food safety issues and the need for reliable information increasingly influence food-related decisions (Hsu et al., 2016).

Empirical evidence supports the role of food safety in driving organic food consumption. For instance, a study in Malaysia found that 57% of the respondents strongly preferred organic food primarily due to safety concerns (Lian and Yoong, 2019). Following the COVID-19 pandemic, consumers became more concerned about health and increasingly perceived organic food as safer than conventional alternatives. The overall opinion among consumers is that organic food is healthier and safer than conventional alternatives (Ahangarkolaee and Gorton, 2021). Prior research also highlights that food safety concerns significantly influence both consumer attitudes and purchase intentions towards organic food (Ashrafi and Maoua, 2024). These findings suggest that food safety concern is a critical determinant of organic food purchase behavior. Accordingly, the following hypotheses are formulated:

H1: Food safety concern significantly influences the purchase intention of organic food.

2.3 Natural content (NC)

Natural content is a valued attribute of organic food because consumers associate the absence of artificial colours and additives and minimal processing with authenticity and quality (Hsu et al., 2016). Consumers who prioritise health and authenticity are therefore more likely to prefer organic food because of its perceived natural composition (Tandon et al., 2021). Prior research suggests that the absence of synthetic ingredients and minimal processing enhance consumers’ perceptions of product quality and trust, thereby positively shaping their purchase intentions towards organic food. Accordingly, the following hypothesis is proposed:

H2: Natural content significantly influences the purchase intention towards organic food.

2.4 Nutritional concern (NUC)

Growing awareness of healthy lifestyles has led consumers to place greater emphasis on the nutritional value of the food they consume. Organic foods are often perceived as more nutritious than conventionally produced alternatives, which positively influences consumer preferences (Li et al., 2019). Consumers tend to associate higher nutritional value with improved health outcomes (Williams, 2002), and such perceptions play a significant role in shaping attitudes towards organic food. Younger consumers perceive organically produced foods as offering superior nutritional quality and greater health benefits compared to conventional products (Lian and Yoong, 2019). Furthermore, access to knowledge about dietary nutrients has been found to significantly influence consumer attitudes and purchase intentions (Ahmad et al., 2026; Ali et al., 2021). These findings suggest that perceived nutritional concern is an important determinant of organic food purchase intention. Accordingly, the following hypothesis is proposed:

H3: Nutritional concern significantly influences the purchase intention of organic food.

2.5 Health consciousness (HC)

Health-conscious consumers are more likely to prioritise preventive behaviors and prefer organic food because of its perceived safety and long-term health benefits (Michaelidou and Hassan, 2008; Iqbal et al., 2021). Individuals with higher levels of health consciousness tend to perceive organic foods as superior to conventional alternatives in terms of health, freshness, and quality (Liu and Zheng, 2019). Consumers primarily emphasise health over other factors, such as environmental, community, or personal considerations, when purchasing organic food (Ahmad et al., 2026). Similarly, health consciousness has been shown to significantly influence consumers’ attitudes towards purchasing organic food, with perceived health benefits strengthening both utilitarian and hedonistic evaluations of organic products (Nguyen et al., 2019). Therefore, the following hypothesis is proposed:

H4: Health consciousness significantly influences organic food purchase intention.

2.6 Attitude (AT)

Attitude is an acquired predisposition to respond favourably or unfavourably towards a specific object or behavior (Mehra and Ratna, 2014). Within the Theory of Planned Behavior, attitude is a central determinant of behavioral intention (Ajzen, 1985). In the context of organic food, previous studies have consistently reported a positive relationship between consumers’ attitudes and purchase intention (Irianto, 2015; Eberle et al., 2022; Khan et al., 2026; Khan et al., 2023; Teng and Wang, 2015). Accordingly, the following hypothesis is proposed:

H5: Attitude significantly influences organic food purchase intention.

2.7 Religious beliefs (RB)

Religious beliefs shape consumers’ values, attitudes, and dietary practices, making them an important influence on food-related decisions (Raggiotto et al., 2017). The extent to which religious teachings are followed can influence food choices, while religious beliefs and practices have been positively associated with healthier dietary behaviors (Imtiyaz et al., 2021). Previous studies have emphasised the significance of recognising religious beliefs as a crucial driver of fundamental values that impact consumer attitudes and behavior (Saeed and Mohy-Ud-Din, 2026; Minton et al., 2015). Hence, the following hypothesis is proposed:

H6: Religious beliefs significantly influence organic food purchase intention.

2.8 Purchase intention (PI)

Purchase intention reflects consumers’ likelihood of purchasing a product and is widely used as a predictor of purchasing behavior (Shrestha, 2020). Although intentions do not always translate into actual purchases, particularly in the context of organic food (Lee et al., 2016), purchase intention remains a widely used dependent variable in organic food research (Ahmad et al., 2026; Tewari et al., 2021). However, much of this evidence originates from developed economies, highlighting the need for further investigation in the Indian context despite recent contributions (Kushwah et al., 2019).

Given the complexity of consumer decision-making, this study therefore examines whether specific antecedent conditions emerge as necessary and whether different combinations of these conditions lead to high organic food purchase intention using a configurational approach.

3 Research methodology

A descriptive cross-sectional research design was adopted, and a convenience sample of 256 potential consumers from Delhi NCR was used. To ensure adequate statistical power for the structural model, an evaluation using GPower 3.1 confirmed that, for a specification with six direct predictors, a medium effect size (f 2 = 0.15), and a significance level of α = 0.05, the final sample of 256 yielded statistical power exceeding 0.99, comfortably surpassing the recommended 0.80 benchmark (Faul et al., 2009; Hair et al., 2017). Furthermore, this sample size aligns well with the recommended operational range for fsQCA (Ragin, 2008).

Data were collected using a structured questionnaire with 31 items and measured on a five-point Likert scale. The demographic profile of the respondents is presented in Table 1. To ensure content validity, a systematic approach involving expert review and cognitive pretesting was utilized to refine the survey items (Gehlbach and Brinkworth, 2011). Once the instrument had been validated by the expert panel, the questionnaire was administered to participants who had been fully briefed on the study’s academic purpose.

Table 1. Demographic profile of respondents.

Demographic variableCategoryNPercentage
GenderMale9035.2%
Female16664.8%
Age25 and below9737.9%
26–4013552.7%
41 and above249.4%
EducationGraduate7529.3%
Post graduate11946.5%
Other6224.2%
OccupationPrivate job15814.28%
Government job3821.42%
Student6034.12%
Monthly income (Indian Rupee or INR)Below 30,0004818.8%
31,000–60,0005019.5%
61,000–90,0004818.8%
91,000 and above11043%

Source: authors’ own computations.

Prior informed consent was obtained from all respondents, and data collection procedures ensured strict anonymity. The questionnaire items were adapted from previously validated measurement scales. Food Safety Concern was measured using four items from Roddy et al. (1996), while Natural Content was assessed using four items from Lockie et al. (2004). Health Consciousness included five items adapted from Kutnohorská and Tomšík (2013). Purchase Intention was measured using five items from Teng and Lu (2016), and Nutritional Concern was measured using four items from Tandon et al. (2021). Attitude was assessed using four items (Asif et al., 2018). Finally, religious beliefs were measured using three items from a study conducted by Bakar et al. (2013) and two additional items based on the work of Mathras et al. (2016).

PLS-SEM was utilised to evaluate the proposed relationships, while fsQCA was employed to explore alternative causal configurations leading to the outcome. This mixed-method approach was adopted to provide a deeper understanding of the research problem and is consistent with previous studies in the field of marketing, such as Silaban et al. (2023) and Sharma et al. (2024), which have combined PLS-SEM and fsQCA to capture both linear and configurational insights. PLS-SEM is a variance-based technique for structural equation modelling that is especially effective for analysing complex models, particularly in situations involving non-normal data distributions or small sample sizes (Hair et al., 2014; Henseler et al., 2016). The PLS-SEM analysis was conducted using SmartPLS® software. Moreover, fsQCA was used to enhance the results obtained from PLS-SEM and ensure rigour in the research.

fsQCA intends to offer insights into the observations within the sample by demonstrating the diverse combinations of conditions that can lead to identical outcomes (Woodside, 2013). The initial stage of conducting fsQCA involves calibrating data related to both the outcome and the antecedent conditions intended for analysis. Calibration involves converting raw data into scores that represent fuzzy sets, typically using external anchor points or in-depth case knowledge to form meaningful groupings (Ragin, 2008). In line with the recommendations of a prior study (Pappas and Woodside, 2021), three specific anchors for full membership, crossover point, and full non-membership were established as the maximum, average, and minimum values, respectively. fsQCA v3.0 was used for necessary and sufficient condition analyses (Ragin and Davey, 2017).

4 Data analysis and results

4.1 PLS-SEM analysis

Initially, the data were analysed using PLS-SEM. This analysis includes both structural and measurement models. Regarding the measurement model, the stages that need to be executed are validity analysis, reliability analysis, and model fit criteria on PLS-SEM.

Multiple stages of testing were performed to ensure the validity and reliability of the research. The construct validity of the study was assessed by comparing the loading values of each factor with a threshold of 0.7. This assessment aimed to evaluate the ability of the measuring instrument to accurately capture the research concept. The results confirmed construct validity, with factor loadings for each item exceeding the 0.7 benchmark. Some items were deleted to improve construct validity and internal consistency, that is, FS3, HC1, AT2, and PI4. Furthermore, the study evaluated convergent validity through the Average Variance Extracted (AVE) value, which was also set at 0.5 (Hair et al., 2017). The findings indicated satisfactory results, as the AVE values for each construct exceeded 0.5. To gauge the internal consistency of the constructs, Hair et al. (2017) recommended a Cronbach’s alpha (CA) of 0.7 and a Composite Reliability (CR) of 0.7. All the values were within the acceptable threshold, except for the Cronbach’s alpha of food safety concern, which fell below the conventional 0.70 threshold; however, this criterion should not be applied rigidly. As noted by Taber (2018), Cronbach’s alpha is sensitive to the number of items and the dimensionality of the construct. Overall, the results demonstrate strong internal consistency within the research. Table 2 provides a detailed overview of construct validity and reliability results.

Table 2. Construct validity and internal consistency.

ConstructItemsFactor loadingsCronbach alphaComposite reliabilityAverage variance extracted
Food safety concern (FS)FS10.6920.5810.8050.720
FS20.899
FS40.845
Natural content (NC)NC10.7010.7160.8220.538
NC20.819
NC30.781
NC40.618
Nutritional content (NUC)NUC10.7590.8130.8760.640
NUC20.851
NUC30.826
NUC40.760
Health consciousness (HC)HC20.8990.8050.8940.639
HC30.832
HC40.885
HC50.860
Attitude (AT)AT10.8260.7920.8660.619
AT30.832
AT40.811
Religious beliefs (RB)RB10.6980.8300.8790.593
RB20.893
RB30.720
RB40.801
RB50.791
Purchase intention (PI)PI10.8110.8050.8660.566
PI20.785
PI30.756
PI50.808

Source: authors’ own computations.

Factor loading ≥ 0.7; Cronbach’s alpha ≥ 0.7; composite reliability ≥ 0.7; average variance extracted ≥ 0.5.

Additionally, discriminant validity was assessed using the Heterotrait–Monotrait Ratio (HTMT) approach, a contemporary method for assessing this aspect, which determined an HTMT value of 0.85 (Henseler et al., 2015). Several HTMT values slightly exceeded the conservative threshold of 0.85; however, all values remained below the more liberal threshold of 0.90 recommended for conceptually related constructs (Henseler et al., 2015). Therefore, the discriminant validity of the constructs was considered acceptable overall. Table 3 presents the HTMT ratio results for discriminant validity. Collinearity among predictor constructs in the structural model was assessed using the Variance Inflation Factor (VIF). Following recent PLS-SEM recommendations, VIF values below 3 indicate that collinearity is not a critical issue (Legate et al., 2024; Panday et al., 2026). The results showed that all VIF values were below the recommended threshold, confirming the absence of multicollinearity concerns in the structural model. After checking construct validity, internal consistency, divergent validity, and multicollinearity, the data were deemed suitable for path analysis.

Table 3. Discriminant validity of Heterotrait–Monotrait Ratio (HTMT).

ConstructATFSHCNCNUCPIRB
AT
FS0.896
HC0.8240.876
NC0.8560.8660.858
NUC0.8790.8550.7730.841
PI0.9040.8470.7640.7480.797
RB0.3560.8040.3580.5840.3790.323

Source: authors’ own computations.

Structural model analysis was conducted to evaluate the ability of the research model to test the hypotheses. The strength of the endogenous constructs was assessed through path coefficients (Falk and Miller, 1992). The path coefficients for Food Safety, Health Consciousness, Natural Content, Nutritional Content, Attitude, and Religious Belief indicated that the Purchase Intention construct had an R2 value of 0.650. Since the endogenous construct has an R2 value greater than 0.1, the research model was considered suitable. Additionally, the structural model was evaluated using the fit criteria. The thresholds we used were a standardised root mean square residual (SRMR) of less than 0.10 or 0.08, and a Normed Fit Index (NFI) close to 1 (Hu and Bentler, 1998). The specific model fit was measured using the lower bounds for d_ULS and d_G at 95–99 bounds (Dijkstra and Henseler, 2015). Each model met the fit criteria (for example, SRMR = 0.067, d_ULS = 4.734, d_G = 94.81, and NFI = 0.85), which were deemed to be satisfactory. Therefore, there were no concerns regarding the model’s fit.

The results of the hypothesis testing are presented in Figure 1, Table 4. The findings revealed that natural content, religious beliefs, and attitudes did not significantly influence the intention to purchase organic foods. Consequently, H2, H5, and H6 are not supported, with corresponding coefficients (β) of 0.030, −0.049, and 0.025 and p-values (α) of 0.746, 0.434, and 0.885. In contrast, food safety, health consciousness, and nutritional content significantly impacted the purchase intention for organic foods. Therefore, H1, H3, and H4 are supported, with coefficients (β) of 0.196, 0.405, and 0.281 and p-values (α) of 0.018, 0.004, and 0.010.

Structural model.
Figure 1. Structural model.

Table 4. Hypothesis testing.

HypothesisPathβT statisticsp-values
H1Food safety → purchase intention0.1962.3570.018*
H2Natural content → purchase intention0.0300.3240.746
H3Nutritional content → purchase intention0.2812.5600.010*
H4Health consciousness → purchase intention0.4052.8950.004*
H5Attitude → purchase intention0.0250.1450.885
H6Religious beliefs → purchase intention−0.0490.7820.434

Source: authors’ own computations.

Standardized path coefficients (β) reported. t-values calculated based on 5,000 bootstrap resamples. Significance level evaluated at α = 0.05 (p < 0.05, *p < 0.01, two-tailed).

4.2 fsQCA analysis

In the first step of the fsQCA procedure, the antecedent conditions were assessed to determine whether they qualified as necessary conditions for the outcome variable. A condition is considered necessary when it consistently appears whenever an outcome is present (Villanueva et al., 2017). According to Ragin (2009), a consistency score of 0.90 or higher is the accepted benchmark for establishing the necessity. Identifying the necessary conditions is essential, as they represent the baseline requirements that must be met for an outcome to occur. Consistency values for all causal conditions were then evaluated (Eng and Woodside, 2012). As shown in Table 5, only AT and HC surpassed the 0.90 threshold, indicating that these two factors are likely necessary conditions for a high level of purchase intention toward organic food.

Table 5. Necessary conditions for the presence and absence of purchase intention.

ConditionPresenceAbsence
ConsistencyCoverageConsistencyCoverage
FS0.8460.8180.7440.547
~FS0.5310.7320.7520.788
NC0.8620.8470.7190.537
~NC0.5280.7120.7940.814
NUC0.8460.8670.6940.541
~NUC0.5520.7030.8290.803
HC0.9130.8570.7280.520
~HC0.4890.7030.8000.875
AT0.9130.8570.7280.520
~AT0.4890.7030.8000.875
RB0.8470.7120.8320.532
~RB0.4430.7770.5490.732

Source: authors’ own computations.

Bold values indicate consistency scores above 0.90, representing necessary conditions.

Following the necessity analysis, fsQCA was employed to uncover sufficient causal configurations that lead to high purchase intention for organic food. The analysis was conducted using a consistency threshold of 0.89 and a frequency cut-off of 1, ensuring that only empirically relevant configurations were retained. The intermediate solution was selected for interpretation because it balances empirical data with theoretically plausible counterfactuals. This solution met the minimum consistency benchmark of 0.74 recommended by Woodside (2013) and adhered to the acceptable coverage standards outlined by Ragin (2008). As presented in Table 6, the model accounts for approximately 92.3% of the empirical cases of purchase intention, indicating strong explanatory power.

Table 6. Sufficient configurations for purchase intention.

Frequency cutoff: 1
Consistency cutoff: 0.895416
ConfigurationRaw coverageUnique coverageConsistency
~FS * HC * AT0.5010.0100.899
NC * HC * AT0.8330.0430.913
HC * AT * RB0.8000.0140.890
NC * NUC * ~HC * ~AT0.4020.0030.915
Solution coverage: 0.923
Solution consistency: 0.818

Source: authors’ own computations.

The analysis identified four configurations as sufficient pathways for purchase intention. Notably, the configuration NC * HC * AT demonstrated the highest raw coverage (0.833) and strong consistency (0.913), suggesting that when consumers value natural content, are health-conscious, and are positively predisposed, the likelihood of purchasing organic food increases substantially. Similarly, HC * AT * RB (coverage: 0.800; consistency: 0.890) and ~FS * HC * AT (coverage: 0.501; consistency: 0.899) emerged as significant combinations, further emphasising the central role of health awareness and attitude. The fourth configuration, NC * NUC * ~HC * ~AT, with low coverage (0.402) but high consistency (0.915), demonstrated that concern for natural content and nutritional value, in the absence of health consciousness and attitudes, can lead to purchase intentions.

In addition to these dominant pathways, other configurations, such as NC * ~HC * ~AT * RB (consistency score 0.869), also contributed to the outcome, albeit with consistency scores slightly below the cut-off. Hence, these are not reported in the results table for the sufficient conditions. These alternative paths illustrate that multiple routes can lead to PI, even in the absence of certain commonly expected conditions. Importantly, HC and AT appear most frequently across configurations, underlining their core status in shaping consumers’ intention to purchase organic food.

Furthermore, Table 7 demonstrates a sufficient condition for the formation of a truth table. The configurations highlight the distinct roles of core and peripheral conditions in shaping outcomes. The presence of conditions is shown with a black circle (●), absence with a cross inside a circle (⊗), and blank means irrelevant. Larger circles indicate core conditions, and smaller circles indicate peripheral conditions. Core conditions represent the primary drivers consistently present across configurations, indicating that HC and AT emerge as dominant core conditions in most solutions, reinforcing their central importance in achieving this outcome. In some configurations, NC and NUC also act as core conditions, suggesting alternative, dominant pathways. In contrast, peripheral conditions, such as the absence of food safety concerns, play a supportive or contextual role, refining rather than determining outcomes. Thus, while core conditions drive configurations, peripheral conditions adjust their contextual effectiveness.

Table 7. Truth table.

ConfigurationFSNCNUCHCATRBConsistencyRaw coverage
1⊗●●0.8990.501
2●●●0.9130.833
3●●●0.8900.800
4●●⊗⊗0.9150.402

Source: authors’ own computations.

Table 8 provides a comprehensive overview of the hypotheses examined in this study. Of the six hypotheses, three were supported, whereas three were not.

Table 8. Summary of hypotheses.

HypothesesStatementResult
H1Food safety concern significantly influences organic food purchase intentions.Supported
H2Natural content significantly influences organic food purchase intentions.Not supported
H3Nutritional concern significantly influences organic food purchase intentions.Supported
H4Health consciousness significantly influences organic food purchase intention.Supported
H5Attitude significantly influences organic food purchase intention.Not supported
H6Religious beliefs significantly influence organic food purchase intention.Not supported

Source: authors’ own computations.

5 Discussion

The empirical findings from both symmetric (PLS-SEM) and asymmetric (fsQCA) analyses offer granular insights into how product attributes, psychological motivations, and cultural factors shape consumer intentions toward organic food in an emerging market context.

The structural model demonstrates distinct variations in the predictive power of the evaluated determinants. Health consciousness emerged as the primary direct driver of organic food purchase intention (β = 0.405 , t = 2.895 , p = 0.004), exerting the largest relative effect among all symmetric paths. This is followed by nutritional content (β = 0.281 , t = 2.560 , p = 0.010) and food safety concerns (β = 0.196 , t = 2.357 , p = 0.018). In contrast, natural content (β = 0.030 , p = 0.746), attitude (β = 0.025 , p = 0.885), and religious beliefs (β = − 0.049 , p = 0.434) exhibited statistically non-significant net effects in the linear model.

Crucially, the complementary fsQCA findings reveal causal complexity that single net-effect models mask. While attitude and natural content did not manifest direct linear significance in the PLS-SEM estimation, fsQCA establishes both attitude and health consciousness as essential necessary conditions and core elements within high-purchase-intention configurations. Furthermore, natural content serves as a core enabling condition across specific causal recipes. This divergence demonstrates that factors lacking standalone linear sufficiency still act as indispensable enabling conditions when combined within multi-attribute recipes.

Contextualizing these empirical patterns within the broader literature, the dominant influence of health consciousness substantiates established consumer behavior research demonstrating that wellness-oriented motivations consistently drive sustainable food choices (Mai et al., 2023; Michaelidou and Hassan, 2008; Leyva-Hernández et al., 2025). Recent experimental insights further confirm that targeted health and environmental communications substantively amplify consumer purchase propensity (Nifatova et al., 2026). Health-conscious consumers evaluate organic products as vehicles for long-term well-being, intrinsically associating organic production with preventative healthcare (Li and Shan, 2025; Teng and Lu, 2016).

Regarding specific product attributes, the significant symmetric effect of nutritional content corroborates prior findings that the perceived presence of essential nutrients and the absence of synthetic inputs are primary health-benefit heuristics (Hoefkens et al., 2009; Hsu et al., 2016). Similarly, the positive relationship between food safety concerns and purchase intentions aligns with contemporary research (Pan and Wu, 2024; Zhang et al., 2025), reflecting rising consumer vigilance toward adulteration and chemical residues. While this contrasts with earlier Western studies where food safety played a peripheral role (e.g., Michaelidou and Hassan, 2008), it highlights the heightened sensitivity to safety protocols in developing market ecosystems.

Although attitude did not display an independent net effect in the PLS-SEM estimation—differing from conventional behavioral frameworks (Ahmad et al., 2026; Pant et al., 2024a)—its ubiquity across fsQCA causal pathways confirms that a favourable mindset remains a baseline prerequisite rather than an isolated catalyst. Conversely, the marginal effect of religious beliefs mirrors earlier empirical patterns (Bu and Go, 2008; Shaharudin Rizaimy et al., 2010a, 2010b). Although religion dictates normative dietary boundaries across India, such as vegetarianism or halal practices (Koc et al., 2025), these boundaries do not automatically translate into a preference for organic food unless the organic attribute directly intersects with explicit dietary sanctifications.

From a sustainable food systems perspective, these findings illustrate how demand-side drivers can accelerate or constrain the scaling of organic supply chains. Rather than representing contradictory outcomes, the differing results between PLS-SEM and fsQCA offer complementary explanatory depth: PLS-SEM isolates the net impact of individual attribute evaluations, whereas fsQCA uncovers equifinal configurations where diverse attribute combinations achieve identical behavioral outcomes. Consequently, consumer decision-making in emerging organic markets is governed not by isolated variables in a vacuum, but by structured constellations of personal health goals and product assurance attributes.

6 Conclusion and implications

Producers and marketers of organic food in India should focus on the nutritional value and natural ingredients of organic products, as these are significant factors for consumers. Thus, all such information should be printed on food packaging to maintain transparency. Food safety is another essential factor for consumers when choosing food; therefore, marketers and concerned authorities should ensure that their products adhere to specific food safety standards, such as the standards of the National Programme for Organic Production (NPOP) and Food Safety and Standard Authorities in India (FSSAI). At the food systems level, strengthening consumer trust in organic certification and food safety governance is essential for sustaining the organic sector. Clear labelling, credible regulatory oversight, and transparent certification mechanisms can reduce information asymmetries between producers and consumers, thereby supporting a broader transition toward sustainable food systems in India.

Marketers of organic food may also consider incorporating subtle references to religious beliefs in their messaging, emphasising that consuming organic products aligns with both personal health and ethical values. This approach could gently appeal to consumers whose dietary choices are influenced, even partially, by their spiritual or cultural practices. To further encourage the purchase of organic food, both the Government of India and marketers should provide robust support to organic food producers, particularly in areas such as technological innovation and brand development, which can help reduce production costs and mitigate the associated risks. Strengthening the organic food supervision system is also crucial, as it enhances credibility and public trust in organic labels. Building a strong reputation for organic food is essential for sustaining consumer interest and loyalty.

The present study explored the key determinants of organic food purchase intention in the Indian context using a dual-method approach combining PLS-SEM and fsQCA. PLS-SEM revealed that food safety concerns and health consciousness significantly influenced consumers’ intention to purchase organic food, whereas attitude did not emerge as a significant independent predictor in the symmetric model. In contrast, religious beliefs had minimal impact, suggesting that consumers prioritise health- and product-related attributes over religious considerations when choosing organic options. The fsQCA results complement the PLS-SEM findings by revealing multiple sufficient configurations leading to high purchase intention, highlighting the causal complexity of sustainable food consumption beyond the average net effects. The most prominent combinations involved natural content, health consciousness, and attitude, often in conjunction with nutritional concerns. This indicates that organic food purchase intention is not driven by a single factor but by the interplay of several influential conditions. The study’s findings highlight the importance of promoting the health, safety, and nutritional benefits of organic products to increase their consumption. Marketers should focus on shaping positive attitudes and highlighting the natural qualities of organic foods. Policymakers should support awareness campaigns and regulatory frameworks that reinforce consumer trust. By addressing both psychological and product-based motivators, stakeholders can contribute to the growth of organic food consumption in India.

This study had certain limitations that should be acknowledged. First, the use of a cross-sectional design restricts causal inferences (Hair et al., 2014). Second, the sample was based on a convenience sample from the Delhi NCR region, which may limit the generalisability of the findings to other geographic and socioeconomic contexts within India. Third, reliance on self-reported measures may be subject to social desirability bias, which can lead respondents to overreport socially desirable behaviors, such as organic food consumption (Turaga and Arif, 2026). Fourth, this study examined organic food as an overarching consumption category rather than examining specific product matrices (e.g., plant- vs. animal-derived products). Given that safety and nutritional evaluations may differ between fresh produce and organic dairy or poultry, future studies could test whether these linear and configurational relationships vary across distinct food matrices. Additionally, this study focuses on purchase intention rather than actual purchase behavior, which, although consistent with prior research grounded in the theory of planned behavior, may not fully capture real consumption patterns. Measuring actual purchase behavior often requires longitudinal or experimental designs, which are beyond the scope of the present study.

Building on these limitations, future research may extend this study in several ways. Longitudinal designs could provide deeper insights into how organic food purchase intentions evolve over time and help establish stronger causal relationships between variables. Expanding the geographic scope beyond the Delhi NCR would allow for the examination of regional and rural–urban variations in consumer behavior across India and other emerging economies. Additionally, incorporating variables such as environmental concerns, trust in certification, perceived value, and digital platform influence may offer a more comprehensive understanding of organic food consumption. Finally, future studies could extend the configurational approach by integrating experimental or mixed method designs to examine how different combinations of factors influence actual purchase behavior rather than stated intentions.

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Footnotes

Edited by: Olutosin Ademola Otekunrin, Federal University of Agriculture and Development Studies, Iragbiji (FUADSI), Nigeria

Reviewed by: Rakotoarisoa Maminiaina Heritiana Sedera, ISCAM Business School, Madagascar

Satish Chandra Pant, Dr. B. R. Ambedkar National Institute of Technology Jalandhar, India

Lubna Ahmed, Dundalk Institute of Technology, Ireland

Statements

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author SU declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This research is funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2026R79), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Data availability

The study is based on primary data, which is available in CSV and SPSS formats. The dataset used and analyzed during the current study can be obtained from the authors upon reasonable request.

Ethics approval

Ethical approval was not required for the studies involving humans because the Department of Business Administration, Aligarh Muslim University determined that the study fell under the category of minimal-risk research. The study was conducted through an anonymous questionnaire survey, prior informed consent was obtained from all participants, participation was entirely voluntary, and no personally identifiable information was collected. Accordingly, formal Institutional Review Board (IRB)/Ethics Committee approval was not required. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

RF: Software, Methodology, Writing – original draft, Formal analysis, Data curation, Conceptualization, Investigation, Writing – review & editing. ZD: Writing – review & editing, Formal analysis, Writing – original draft, Methodology, Data curation, Validation. OZ: Formal analysis, Investigation, Writing – review & editing, Software, Writing – original draft. AA: Writing – review & editing, Methodology, Writing – original draft, Visualization, Formal analysis. WM: Writing – original draft, Writing – review & editing. AF: Writing – review & editing, Writing – original draft. SU: Writing – original draft, Writing – review & editing, Funding acquisition.

Acknowledgements

Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2026R79), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

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Citation

Firdaus, R., et al. (2026). Drivers of organic food purchase intentions in India: implications for sustainable food systems using PLS-SEM and fsQCA. Frontiers in Sustainable Food Systems, Volume 10. https://doi.org/
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10 - 2026

Copyright

Copyright © 2026 Firdaus, Danish, Zaman, Arif, Mazahar Khan, Fallatah and Urooj.

License: https://creativecommons.org/licenses/by/4.0/

*Correspondence: Areiba Arif

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