Evaluating forces associated with sentient drivers over the purchase intention of organic food products

Prabha Kiran ^a Abhishek Srivastava ^b Satish Chandra Tiwari, ^c* , T. Sita Ramaiah ^d

^aAssistant Professor; School of Business and Management, Christ (Deemed to be University), Bangalore, India

^bAssociate Professor; Faculty of Management Studies, Gopal Narayan Singh University, Rohtas, India

^c*Assistant Professor; Department of Finance and Accounting, IBS Hyderabad, Deemed to be University, Hyderabad, India. Corresponding author's email address: satish2bhu@gmail.com

^bDepartment of Finance, ICFAI Business School, IFHE (Deemed to be University), Hyderabad, Telangana, India

Keywords:Consumer awareness, Purchase intention, Organic food

ARTICLE HISTORY: Received: 04-Jan-2020, Accepted: 24-Apr-2020, Online available: 20-May-2020

Contribution/ Originality

In this paper, the research provided sufficient shreds of evidence against consumer awareness and purchase intention of organic food that will certainly help the organic food industries to promote their products according to the attribute of customers where nutritional values, awareness drivers & demographic variables played a significant role on the purchase intent of organic food products. This paper will also help firms and marketers to promote their brand and association with such manufacturers in consumer's perception.

1. INTRODUCTION

In recent years, there has been a growing increase in environmental conservation and food health practices around the world, which has drawn public attention and centered on organic food. Food protection consumers show a positive attitude towards organic food (Roddy et al., 1996). It leads to various researches in the organic food domain - like people's readiness to pay the excess amount for high organic food, people's knowledge about food items of organic and non-organic or consumer stimulus to purchase organic food, along with the factors that have an impact over the purchase objectives. Consumer attitude of purchase of organic food with deliberation and anxiety about health and environment, i.e., both aspects come from young people. In contrast, older people mostly concentrate on the health aspect (Magnusson et al., 2001). .Business firms, manufacturers, and farmers need consumer awareness programs to understand the significant difference between organic food products and non-organic food products. Nevertheless, it has been found a lack of awareness about food products of organic among consumers (Mithilesh and Verma, 2013). The research study in Brazil observed the relationship between personal value, attitude, and the purchase intention of organic-based food products and found a positive impact of them on conservatism and self-promotion. Personal value also changes the purchase behavior towards organic food products (Mainardes et al., 2017). Moreover, consumer conviction influences purchase intention, and the lack of conviction has adverse effects on the purchase behavior of organic food products (Nuttavuthisit and Thogersen, 2017). Various other dynamics determine the consumer's motivation to make use of food labeling. These factors derive the intention of consumers to seek information or not before the purchase of a particular product.

The name contains data about a specific food. The buyer's mentality towards food names can be slanted by different segment uniqueness like age, sex, instruction level, well-being status, and sustenance data. Elements of the circumstance as salary, time, and extraordinary eating routine additionally control a shopper to look for data about specific natural food and utilize the data to a food choice (Sunelle et al., 2010). This investigation examined the impact of consciousness of food names, extra data, affirmations; logos have the buy thought of the natural food among the shopper.

2. REVIEW OF LITERATURE

Organic food products generally do not use any kind of pesticide or synthetic fertilizers. There is a lot of anxiety among consumers regarding hormones and medicine in animal production and GMO and the use of the artificial additive in fruit and vegetables (Naspetti and Zanoli, 2006). Consumer relates or links personal health with nutritional content as a quality aspect. It has been noted that reasons for purchasing organic food are high content of vitamins, more nutritious meals, and a vigorous diet by 4%-7% of normal organic food consumers (Naspetti and Zanoli, 2006). The main reasons to purchase organic food products are the anticipation of a better and environmentally friendly means of production (Nilima, 2016). Higher consumption of organic food has been observed among consumers who are anxious about natural food engaged in green consumption practices (Lockie et al., 2004). The increasing awareness about global warming, pollution's harmful impact, non-bio degradable solid waste is impacting the consumers, marketers, and companies to switch to eco-friendly products. Also, the companies are taking up the responsibility for environmental protection as well as the rational utilization of natural resources (Sudhalakshmi and Chinnadorai (2014). One of the significant factors that affect the attitude of consumer and their buying behavior towards organic food is the socio-demographic profile. Uses of more organic food habits are influenced by demographic factors like income, age, level of education, household size, and gender (Magnusson et al., 2001; Wier et al., 2003). Health consciousness, as an attitude of the people, has, and they know and are aware of the healthiness in their diet and lifestyle. Consumers believe that organic food is good for one's health, which allows them to consume organic food without any suspicion and fear (Suh et al., 2012). Consumers concerned with food safety show a positive attitude towards organic food (Roddy et al., 1996). Environmental concerns like protection of the environment, environmentally friendly issues, and such concerned consumers be inclined to have an optimistic attitude towards organic food and also show a sturdy plan to purchase (Vermeir and Verbeke (2006) and Chen (2015). Environmental behavior includes the behavior towards production and consumption of food, transportation, and shopping, and buying a house (Jager, 2000). Product labeling is a quality signal that helps the consumer to identify the organic food products. The consumer might not be able to figure out that the product is organic or not without an organic label. It has been found that the knowledge of organic labels is low (e.g., Janssen and Hamm, 2012).

3. METHODOLOGY

The survey was spread over Bangalore City which is the major city in the province of Karnataka, India. The state has 15 organic farmer federations and 576 organic villages. These villages were declared organic under the state government's organic scheme.

The urban area holds a population more inclined towards the purchasing of organic products. People in such areas are always considered as having a high paying capacity for such products i.e. healthier and full of nutritious values. Now the demand is increasing day by day and results in dietary shifts people are moving towards plant-based lifestyles (Mohanraj, 2019). The items in the questionnaire were considered a Likert 5-point Scale i.e. rating from strongly disagrees to strongly agree. The questionnaire was dispersed & the response was collected from the persons aged above 18 years. The random sampling method opted from respondents to protect the ambiguity of respondents (Ooi et al., 2018). From the total 400 samples collected, only 358 were used during data analysis, and remaining were discarded due to incomplete responses (Attewell and Rule, 1991). Cronbach alpha has been used as a Data Analysis tool as a reliability test and KMO and Bartlett's Test for sampling adequacy (Hutcheson and Sofroniou, 1999). One-way ANOVA, regression, correlation, Chi-square, and cross-tabulation were performed too to examine the data and model prepared for the checking of fitness, association, and the awareness factor's influence on the purchase intention. SPSS software was used for data analysis. Through SPSS, the first descriptive and frequency tables were generated to check for the data error and demographic analysis.

3.1. Variables

3.1.1. Purchase intent

Purchase intent is considered as a dependent variable. Purchase intent is the readiness of a consumer in the decision to purchase a product.

3.1.2. Awareness

Awareness is considered an independent variable that is associated with other factors like nutritional information, labeling, certification, food standards, logos, price, and brand name.

4. ANALYSIS

4.1. Respondent profile

The demographics such as age, gender, employment, occupation were considered for the study and their effect on the intention to purchase.

Table 1: Profile of the respondent

4.2. Quantitative / Qualitative analysis

4.2.1. KMO and Bartlett's Test

The Kaiser-Meyer-Olkin Measure of sampling sufficiency is a measurement that demonstrates the extent of change in your factors that may be brought about by fundamental elements. For the data to be adequate, KMO and Bartlett's Test must be more significant than 60%. In this case, it is 85.7%, which means that the sample is adequate (Kaiser, 1974). The KMO sampling adequacy measure for each of the subscales ranged well above the required measure, indicating superb sampling appropriateness (Hutcheson and Sofroniou, 1999).

Table 2: Results of KMO and Bartlett's test

4.2.2. Chi-square test

The significance level of Chi-square is 0.751, i.e., p > 0.05; hence, there is no relationship exists between gender and purchase intention. There is no difference in purchase behavior among gender. The significance level of Chi-square is 0.000, i.e., p < 0.05; hence, there is a relationship that exists between age and purchase intention. There is a significant difference between the age group. There is a difference in purchase behavior among the age group. The significance level of Chi-square is 0.000, i.e., p < 0.05; hence, It does not accept the null hypothesis. P < 0.05 (i.e., Chi-Square); therefore, there exists a relationship between education and purchase intention. There is a considerable difference between education levels. There is a difference in purchase behavior among education levels. The significance level of Chi-square is 0.000, i.e., p < 0.05; hence, there exists a relation between the occupation and purchase intention. There is a significant difference between occupations. There is a difference in purchase behavior among professions. The significance level of Chi-square is 0.000, i.e., p < 0.05; hence, there exists a relation between income and purchase intention. There is a significant difference between income groups. There is a difference in purchase behavior among income groups. Chi-square 's degree of significance is 0.000, i.e., p < 0.05; thus, there is a connection between the preference for organic food and the desire to buy it. We observed a major difference in the buying preference for Organic. Between Organic buying preferences, there is a difference in buying behaviour.

Table 3: Results of Chi-square tests

a. 0 cells (0.0%) have expected frequencies < 5. The minimum expected cell frequency is 179.0
b. 0 cells (0.0%) have expected frequencies <5. The minimum expected cell frequency is 89.5
c. 0 cells (0.0%) have expected frequencies <5. The minimum expected cell frequency is 119.3
d. 0 cells (0.0%) have expected frequencies <5. The minimum expected cell frequency is 71.6

4.2.3. Chi-square of cross tabulation

Chi-square 's significance level is 0.000, i.e., p < 0.05; therefore, we found a significant difference between consumers ' purchasing frequency. There is a disparity in consumer behaviour, purchasing frequency of organic food products.

Table 4: Results of Chi-square cross-tabulation test

a. 125 cells (89.3%) have expected count less than 5. The minimum expected count is 12

Chi-square 's level of significance is 0.000, i.e. p < 0.05; thus, there is a relationship between the preference for organic buying and the intention to purchase. There is a difference in purchasing behavior among organic purchasing preferences for organic foods.

Table 5: Results of Chi-square cross-tabulation test

a. 123 cells (87.9%) have expected count less than 5. The minimum expected count is .05

Table 6: Results of Chi-square cross-tabulation test

a. 6 cells (40.0%) have expected count less than 5. The minimum expected count is .23

Chi-square 's meaning level is 0.008, i.e., p < 0.05; there is a significant difference between levels of education. There is a difference in buying preference among different consumer educational levels.

Table 7: Chi-square cross-tabulation test

a. 10 cells (40.0%) have expected count less than 5. The minimum expected count is .27

Chi-square 's value point is 0.043, i.e., p < 0.05; thus, occupations vary significantly. There is a disparity in purchasing preference between different market occupations.

4.2.4. Regression results

Table 8: Variables entered/removed

a. Dependent Variable: PurchaseIntention
b. All requested variables entered.

Table 9: Summary of adjusted R square

a. Predictors: (Constant), Brand name, Logos, Price, Nutritional Info, Certification, Food Standards, Labeling

Table 10: Results of ANOVA

a. Dependent Variable: Purchase Intention
b. Predictors: Brand name, Logos, Price, Nutritional Info, Certification, Food Standards, Labeling

The R column represents the R value and helps to calculate the consistency of the prediction of the dependent variable. The meaning of R is 0.747, suggesting the right degree of prophecy. The R square value describes the proportion of variance of the dependent variable which the independent variable can explain. The value of R square is 0.558, which notes that our independent variable describes 55.8 percent of our dependent variable's unpredictability.

Table 11: Results of multiple regression test

a. Dependent Variable: Purchase Intention
Purchase Intention= 0.824 + 0.447(Nutritional information) - 0.018(labeling) - 0.129(Price) + 0.143 (certification) + 0.019(Food Standards) + 0.116(Logos) + 0.195(Brand name).

The p-value for labeling and food standards is 0.736 and 0.697, i.e., p-value > 0.05; hence, we concluded that there is no significant relationship between labeling, food standards, and purchase intent of organic food products. In contrast, nutritional information, price, certification, logos, and brand name also contribute an essential role in impacting the purchase intension. In contrast, there is a significant relation to nutritional information, price, certification, logos, and brand name with purchase intention.

4.2.5. One-way ANOVA

Table 12: Results of one-way ANOVA test

The table indicates that the F-value is 0.000 (p < 0.05), and the relevant value is 0.495. There is, therefore, a significant disparity between the desire for organic shopping and the plan to purchase organic food and behavior, buying organic food products.

Table 13: Results of one-way ANOVA test results (purchase intention)

The table predicts that 0.002 (p < 0.05) is the F-value 4.284, and the relevant value. And we note a significant gap between the purchasing level and the desire to buy organic food.

4.2.6. Pearson correlation analysis

Table 14: Correlation test results

**. Correlation is significant at the 0.01 level (2-tailed)

The correlation matrix shows that all the variables are significantly and positively related to each other variables. Most of the variables are within the range of +0.42 to 0.688, which moderately defines the relationship. The correlation coefficients of all variables are not more than 0.9, and hence multicollinearity does not exist in these data.

Nutritional information

Table Correlation shows the correlation between Nutritional information and purchase intention of organic food products is r = 0.688 (p < 0.05). The coefficient range of environmental concern is high. This means that nutritional information is significantly related to the purchase intention of organic food products. Thus, nutritional information is supported.

Labeling

The correlation results between labeling and organic food purchase intention shown in the table are r = 0.513 (p < 0.05), which can be group into a modest relationship. Therefore, labeling is supported because it is significantly related to the purchase intention of the organic food product.

Price

Table Correlation shows r =0.339 (p < 0.05) to be the correlation between price and purchase intention of organic food. The price range is moderate in the coefficient. This means that price is strongly linked to the decision to buy organic food products. And the price is borne.

Certification

Table Correlation shows the correlation between certification and the intention to purchase organic foods is r = 0.476 (p < 0.05). The certification range is moderate in the coefficient. This means the certification has a huge impact on the decision to buy organic food products. Thus, certification is supported.

Food standards

Table Correlation shows the correlation between food standards and the intention to purchase organic foods is r = 0.477 (p < 0.05). The range of food standards is moderate in the coefficient. This means nutritional requirements are significantly related to the purpose of buying organic food products. Thus, food standards are supported.

Logos

Table Association reveals the r = 0.485 (p < 0.05) association between the logos and the purchasing goal of organic food products. The range of logos to the coefficient is limited. That means logos are especially linked to the intention to purchase organic food items. Thus, logos supported.

Brand name

Table Correlation reveals the association between brand name and desire to buy organic products is r = 0.527 (p < 0.05). The label name coefficient range is high. That means the brand name is closely linked to the purchasing target of organic food items. Thus, the brand name is supported.

4.2.7. Descriptive statistics

Table 15: Nutritional information

The above tables that the mean values are moderate, as they are p < 3. Also, from all the other sub-constructs, we can see that nutritionalinfo7 (Expiry date) has the highest mean value (of 2.90), and nutritionalinfo5 (Name of the manufacturer) has the least mean value (2.44) amongst all the variables in the nutritional information. Therefore, it can be said that consumers are more aware and check the expiry date variable of the nutritional information more than the name of the manufacturer.

Table 16: Showing mean and standard deviations for labeling and its various factors

The above table shows the mean and standard deviations for Labeling and its various factors (sub-constructs) that lead to labeling as a whole. The mean values are moderate, as they are p < 3. Also, from all the other sub-constructs, we can see that labeing2 (importance of food labeling) has the highest mean value (of 2.68), and labeling 9(producer's Identity) has the least mean value (2.33) amongst all the variables in the nutritional information. Therefore, it can be said that consumers are more conscious of the importance of food labeling variables more than the producer's identity.

Table 17: Showing mean and standard deviations for price and its various factors

The above table shows the mean and standard deviations for price and its various factors (sub-constructs) that lead to price as a whole. The mean values are moderate, as they are p < 3. Also, from all the other sub-constructs we can see that price3 (prefer discounts and offers) has the highest mean value (of 2.58) and price2 (price as an indicator of real organic food products) has the least mean value (2.21) amongst all the variables in the price. Therefore, it can be said that consumers are more likely to prefer discounts and offer while purchasing organic food products more than the price as an indicator of genuine organic products.

Table 18: Showing mean and standard deviations for certificate and its various factors

The above table shows the mean and standard deviations for certificate and its various factors (sub-constructs) that lead to a certificate as a whole. The mean values are moderate, as they are p < 3. Also, from all the other sub-constructs, we can see that certificate8 (meetings/seminars) has the highest mean value (of 2.78) and certificate2 (awareness of regulatory bodies of organic food like APEDA) has the least mean value (1.90) amongst all the variables in the certificates. Therefore, it can be said that consumers are more likely to aware of certifications from meetings and seminars more than the awareness of consumers about organic food body regulators like APEDA, USDA, etc.

Table 19: Showing mean and standard deviations for food standards and its various factors

The above table shows the mean and standard deviations for food standards and its various factors (sub-constructs) that lead to food standards as a whole. These are the multiple factors influencing the overall food standards variable. The mean values are moderate, as they are p < 3. Also, we can observe that food standards 3 and food standards 7 (AGMark and importance of regulatory standards and body in ensuring quality) has the highest mean value (of 2.66) and food standards 5 (National Programme for organic production) has the least mean value (2.03) amongst. Therefore, it can be said that consumers are more likely to be aware of AGMark standards more than the National program for organic production (NPOP) standards.

Table 20: Mean and standard deviations for loge and its various factors

The above table shows the mean and standard deviations for logos and its various factors (sub-constructs) that lead to logos as a whole. The mean values are moderate, as they are p < 3. Also, from all the other sub-constructs, we can see that logos3 (Logo Visibility and Legibility) has the highest mean value (of 2.66), and logo1 (logo recognition) has the least mean value (2.20) amongst all the variables in the food standards. Therefore, it can be said that consumers are more likely to be put more emphasis on logo visibility and legibility more than logos recognition.

Table 21: Mean and standard deviations for brand name and its various factors

The above table shows the mean and standard deviations for brand name and its various factors (sub-constructs) that lead to the brand name as a whole. The mean values are moderate, as they are p < 3. Also, from all the other sub-constructs we can see that brandname4 (Brand name signifies quality) has the highest mean value (of 2.51) and brandname3 (recognize organic brands like organic tattwa, farm2kitchen, etc.) has the least mean value (2.20) amongst all the variables in the food standards.

5. FINDINGS

We found that awareness drivers like nutritional information, price, certification, brand's name, and logos have a considerable influence on purchase intention. Independent variables such as nutritional content, price, certification, brand name, and logos help predict the importance of purchasing intentions, hence these variables are important predictors (i.e. influence) for the intention to buy organic foods. Drivers such as labeling and food quality do not indicate a substantial association between labeling and the decision to purchase organic food items. Therefore, these drivers do not help as a forecaster of purchase intention, very little control over the purchase intent of organic food products. Demographic variables like age, occupation, education, income, organic food buying frequency (frequency of purchase), and organic buying preference (i.e., buying organic food from the online, supermarket, organic boutique, etc.) show a significant relationship on the purchase target of organic food products. We found a considerable difference in the expected and observed value in case of age, occupation, education, income, organic food buying frequency, and organic buying preference concerning purchase intention.

The independent factors of awareness like organic buying preference and buying frequency had a considerable persuasion on the procure target of organic food. If the buying frequency of a health-conscious consumer increases from monthly to weekly, it affects the purchase intention drastically, as the purchase intention will increase for the health-conscious consumer who needs the organic food products more frequently, thereby her decision/intention to buy will increase. In the case of nutritional information drivers, subfactors like expiry date are more relevant or checked by consumers than the name of the manufacturer. It could probably be because it is more appropriate for them to check the expiry date of organic food. Also, they are probably not aware of the name of the manufacturers who manufacture organic food.

6. CONCLUSIONS

We understand the different drivers of awareness, and these drivers lead to consciousness and turn leads to purchase intention. After analysis, we concluded that some of the drivers are more strongly associated with purchasing intention than others and some of the influence more than others. Awareness factors such as nutritional content, packaging, and brand name display a strongly favorable linear relationship to the decision to buy organic products. Conversely, drivers such as certifications and food standards display moderate positive intensity, i.e. linear association with the desire to buy organic foods. Also, awareness drivers like nutritional information, price, certification, brand name, and logos have significant control on the purchase target and demographic variables like age, occupation, education, income, organic food buying frequency (frequency of purchase) and organic buying preference (i.e., buying organic food from online, supermarket, organic boutique, etc.) show a significant relationship on the purchase intent of organic food products.

7. SUGGESTIONS

All awareness drivers like - nutritional information, labeling, food standards, price, logos, brand name and certifications along with the demographic factors - age, gender, education, income, occupation, buying frequency, buying preference are required to recognize the purchase target of organic food products by consumers. Companies need to analyze these variables carefully and consider them as generators of perception and spread the same. Educate the consumer about the need and value of these factors which will allow consumers to differentiate between natural organic foods as opposed to non-organic food products in the long term. This will also help firms and marketers promote their brand name and in the perception of consumers associate themselves with organic food products manufacturers.

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