Consumer Buying behavior of Organic food Products in India Through the Lens of Planned Behavior Theory


Dr. Yoginder Singh Kataria1, Dr. Hari G Krishna2, Vikas Kumar Tyagi3, Tarun Vashishat4

1Associate Professor and Head of the Department, Department of Management Studies, Panipat Institute of Engineering and Technology, Samalkha, Haryana

2Independent Researcher Chennai, Tamil Nadu

3Research Scholar, Department of Marketing and Supply Chain Management, School of Business and Management Studies, Central University of Himachal Pradesh, TAB 2 Dharamshala, Kangra, Himachal Pradesh 176215

4Research Scholar, Department of Tourism and Travel Management, School of Tourism, Travel and Hospitality Management, Central University of Himachal Pradesh, TAB 2 Dharamshala, Kangra, Himachal Pradesh 176215

*Corresponding Author Email:,,,



To investigate the consumer’s decision making process for organic food products by empirically extending the theory of planned behavior for the organic food products consumption in India. Researchers surveyed 188 respondents from Delhi NCT region. Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Partial Least Square- Regression, using SPSS, Jamovi, and Smart PLS.2 respectively in this study for analysis. The model explained in this paper had moderately significant coefficient of determination, high predictive relevance, and high goodness of fit index. This paper has practical as well as theoretical implication as it gives researchers a scope of extension and modification of Theory of Planned Behavior in organic food consumption behavior of Indian Consumers. This paper informs marketers that accessibility plays a key role in governing attitude towards purchase behavior and in turn indirectly to the actual purchase behavior. The study also studied the effect of age and income on the actual buying behavior.


KEYWORDS: Organic food products, buying behavior, PLS-Regression, Planned behavior theory, Consumer attitude







Organic food is a broadly defined as category of plants or animal products, which are grown without conventional chemical fertilizers and pesticides, growth hormones, antibiotics or genetically modified organisms.

It is grown naturally with an ethically, environmentally, and socially responsible approach (Agricultural and Processed Food Products Exports Development Authority) (APEDA, 2018, March 31); (Paul and Rana, 2012); (Hill and Lynchehaun 2002); (Davies, Titterington, nad Cochrane, 1995).  Organic food consumption is often associated to an alternative lifestyle, which includes active environmentalism, vegetarianism, and/or alternative medicine (Cicia, Giudice, and Scarpa, 2002). Over the last decade, the organic food sector has been one of the fastest growing segments in the global food market. Global market for organic produce have increased five times since 1999 (McCarthy, 2015).


The first Census after India’s independence was conducted in 1951 in which the India’s population was counted as 0.36 Billion (Census India, 2001). Today, according to the most recent estimate by the UNO, India’s population is 1.31 billion and we may reach 1.52 billion by 2030. By 2022, India will surpass even China in population bulge (United Nations, 2015). This will certainly raise the food demand exponentially. More demand will lead to increased production and more burden on the environment, until unless organic food productions are not increased, the demand cannot be met. Top five organic food firms in India are Organic India; 24 Letter Mantra; Morarka “Down To Earth”; Conscious Foods; and Ecofarm; Top three Indian states in terms of Organic agriculture area are Madhya Pradesh; Maharashtra; and Rajasthan (ASSOCHAM and Ernst and Young, 2018). Organic crop’s major share in India is Fruits and Vegetables (30%); Cotton (29%); Oil, Seeds, and Soybean (11%) followed by others and globally it is - fruits and vegetables (36%) sold most, followed by dairy products (32%) and grocery (28%) (Hill, and Lynchehaun, 2002).  India’s market size of the packed organic food and beverages in 2016 was INR 53.3 Crore and it is expected to reach INR 87.1 Crore by 2021 (ASSOCHAM and Ernst and Young, 2018). India tops the countries with the largest numbers of organic producers, but when it comes to shares of organic agricultural land or total market size, India is not even there in the top 10 list (Willer and Lernoud, 2017).


As per Kaur and Singh (2007) global consumers have become more interested in buying organic food products due to following factors:


Increased availability or/and accessibility:  

Entry of International firms has led to increased availability and variety of the organic food products at competitive prices. From the consumer’s perspective the accessibility of these products has also increased due to online retailing. Some of the players that have established their own web-portals are- Godrej Nature’s Basket; Farm2Kitchen; Organic Shop etc. New technological advancements have helped in raising supply of Organic Products. Retail shelf space for organic food products is also increasing due to increased demand, created due to increasing awareness.


Changes in business environment:  

Socio-economic and demographic changes in India i.e. increased urbanization and working population; increased spending power and disposable income; movement of households towards higher income groups; changes in lifestyle and family structure have led to increase in demand for Organic Food Products. According to ASSOCHAM and Ernst and Young (2018) metropolitan cities have witnessed a 95% increase in demand in the last five years. Increasing support to farmers from the government through organizations such as National Mission for Sustainable Agriculture (NMSA), Mission for Integrated Development of Horticulture (MIDH) has helped in increasing the production of organic products in India.


Increased health and environmental awareness: Modern day consumers are becoming gradually more concerned about nutrition, health and the quality of their food (Gil, Gracia, and Sanchez, 2000). Increased health concerns and environmental awareness might be due to higher level of education which has raised the demand of organic food products.  Increased adulteration and pollution are also forcing consumers to use organic food products. With growing incidences of diabetes, heart diseases, and cancer in urban India, consumers are now becoming more health conscious and are ready to bear any price. Many exclusive organic showrooms and restaurants are coming up these days. Increased presence of organic food products in trade fairs has raised both awareness and interest of consumers towards them.


Benefits of buying organic food products (ASSOCHAM and Ernst and Young, 2018) could be:  

Lower concentration of pesticides as compared to conventionally grown food, higher nutritional contents which make it a healthier option.  No antibiotics or growth hormones are given to livestock, and in plants toxic fertilizers, pesticides, or GMO are not used making the organic food products a safer option, this also reduces water pollution and maintaining soil quality, thereby protecting environment. Organic food is considered better in taste, more natural, healthy, and safer than conventional alternates are for both consumers and the environment (Hill, and Lynchehaun, 2002); (Paul and Rana, 2012). In contrast Magkos, Arvaniti, and Zampelas (2003) found that there were no evidences explaining that organic food products to be more nutritious than conventional products. Few reasons for not buying organic products could be that consumers are already satisfied from usual food products; limited choices of organic variants available in the market; and lack of trust. Indians are interested in purchasing organic food for their families especially children but the category suffers from poor availability and premium price perception (ACNielsen, 2006); (Paul and Rana, 2012). Which can be because of lack of knowledge and incentives among the farmers. Standardization, certification, and monitoring the organic food products is difficult. This research paper intents to explore the factors affecting consumer’s attitude, intention and actual purchase behavior of Organic Food Products.



Choo, Chung, and Thorndike Pysarchik (2004) tested the causal relationships among attitudes, subjective norms, intention to buy, and purchase behavior of an innovative product (processed food) by using In-depth and focus group interview of 40 consumers and surveying 307 respondents in 10 different cities of India. Confirmatory factor analysis, structural equation modelling were used for the analysis in those papers. The results indicate that subjective norms are a key factor in understanding Indian consumer’s new food purchase decisions and subjective norms are found to have direct effects on attitudes, intention to buy, and purchase behavior for new processed food products.


Gupta (2009) explored the major factors affecting food purchase decisions of Indian consumers. By surveying 326 respondents from Uttar Pradesh and Delhi NCR Region, he found that consumers mainly purchase organic food products due to their health purposes. Ali, Kapoor, and Moorthy (2010) also studied the consumer preference and buying behavior of food products in India. Gracia and Magistris (2007) examined the factors that influence organic food purchases by surveying 200 consumers in Italy and found that the Intention to purchase depends on attitudes, income, and knowledge of organic product. Makatouni, (2002) explored purchasing behavior and attitude for organic food products by using Focus group interviews and sample survey. He found that consumers consider organic food due to its health benefits for themselves and family and believe that it also protects environment


This paper studied Theory of Planned Behavior and their extensions in the consumers buying behavior of organic food products, such as in the papers Sparks and Shepherd (1992); Tarkiainen and Sundqvist (2005). Theory of planned behavior links, attitude, subjective norms, and behavioral control with behavioral intentions. In this study, researcher included accessibility in place of perceived control.


H1: Accessibility of the organic food products is positively associated with forming Social Norms towards organic food products.

Availability of organic food is an important factor governing consumer behavior, as according to researches it was important reason because of which consumers were unable to buy organic food products, organic products are less accessible than conventional products (Zanoli and Naspetti, 2002). Availability is also one of the most important factors, which encourage the purchase of organic food products (Davies et al., 1995). Therefore researchers tried to study the effect of accessibility on the consumer’s attitude towards organic food products.


H2: Accessibility of organic food products is positively associated with forming Consumers’ attitude towards organic food products.

Tarkiainen and Sundqvist (2005) and Chang (1998) studied the relationship between subjective norms and attitudes towards purchase behavior and found that Subjective norms influence attitudes significantly. Therefore researchers tried to study that aspect in this study. 


H3: Social Norms towards organic food products is positively associated with forming Consumers’ attitude towards organic food products.

Choo, Chung, and Pysarchik (2004) observed that attitudes have significant effect on behavioral intentions among Indian consumers for new food product purchasing behavior. Oliver and Bearden (1985) found that attitude has strongest influence on behavioral intentions than other attributes. Ajzen (1991) found that more favorable attitude with respect to a behavior leads to higher intention to perform the behavior. Therefore researchers studied this to explore that. In fact it’s even more important to study this because researchers believe positive attitude doesn’t always lead to positive buying behavior due to constraints like high prices, low awareness, and lack of accessibility.


H4: Consumers’ attitude towards organic food products is positively associated with forming Consumers’ buying intentions towards organic food products.

Bonfield (1974) found behavioral intentions to be an important mediator between attitude and actual purchase behavior. Ajzen (1991) explained how intentions could be significant predictors of actual behavior. Findings of the past studies have supported that the path from intentions of buying to the actual buying behavior of organic food products is positively significant (e.g. Tarkiainen and Sundqvist, 2005; and Saba and Messina, 2003). Therefore, researchers tried to explore that in this study as well.


H5: Consumers’ buying intention of organic food products is positively associated with forming Consumer’s buying behavior of organic food products.

Rimal, Moon and Balasubramanian (2005) found that older people were possibly lesser inclined towards buying organic foods than younger ones. Lockie (2006) found that organic food consumption does not differ across age categories. After analyzing such diverse results from various studies, researchers tried to explore following hypotheses in this paper.

H6: Consumers’ age is positively associated with Consumer’s buying behavior of organic food products.

Share of people consuming organic food products rises with an increase in their incomes (Torjusen, Lieblein, Wandel, and Francis, 2001) and Organic products’ comparative higher price has been the most important reason for customer’s not buying organic food products (Tregear, Dent, and McGregor, 1994), which can hinder lower income group people opting for them. It can also make them less attractive in comparison to conventional products, when it comes to price value of money. According to Loureiro, McCluskey and Mittelhammer (2001), higher income group customers purchase organic food products more frequently than conventional food products.



H7: Consumers’ income is positively associated with Consumer’s buying behavior of organic food products.


Figure 1: Proposed Model: Representing all the Hypotheses.



A structured questionnaire was used for the survey in this paper, formulated through observations and literature review. Before the actual study was conducted the questionnaire was sent to 10 experts including five academicians and five industry experts, for their preliminary reviews. Items were added, deleted, and modified according to the suggestions received by the experts. Final questionnaire was sent to 280 respondents in Delhi, NCT region, who were selected through purposive sampling out of which final complete and usable sample were 188, i.e. 67.14% response rate.  The survey was conducted from January to May 2018. Analysis was done using Exploratory Factor Analysis (EFA) using SPSS, Confirmatory Factor Analysis (CFA) using Jamovi, and Partial Least Square Regression using SmartPLS.2. For regression researchers used more robust, variance based, higher statistical powered Partial least Square Regression with lesser assumptions Hair, Ringle, and Sarstedt (2011). PLS-Regression was used because data was not normal Ringle, Sarstedt, and Straub (2012).


Demographic profile of the respondents: 30.9% (58) respondents were Female and 69.1% (130) of the respondents were Male. Average age of the respondents was 26.36 years ranging from 21 years to 65 years. Average monthly income of the respondents was INR 55,074, ranging from INR 5,000 TO INR 4,50,000. Approximate percentage of Organic Food Products in total food consumption among respondents was 23% ranging from 0% to 100%.



Exploratory Factor Analysis:

For the purpose of scale formation methodology given by Churchill Jr (1979) has been used in this study.  An Exploratory factor analysis was conducted on a total of 25 items, to find underlying factors on SPSS using Principal Axis Factoring, with Varimax method (Kim and Mueller, 1982) for rotation with Kaiser Normalization. Five items (SN1, Att1, Beh3, Int1, and Acc1 with Communalities less than min threshold level threshold level of 0.50 (Hair, Black, Babin, and Anderson, 2015) were deleted and the factor analysis was conducted again. Rest of the twenty variables were extracted with their communalities more than the threshold level of 0.50 as shown in Table 1. Bartlett test of Sphericity (4214.540) and Kaiser–Meyer–Olkin (KMO) ‘measure of sampling adequacy’ (0.897) were found to be satisfactory (Hair et al., 2015). After applying the rotated method of Varimax rotation with Kaiser Normalization, five latent factors were found having Eigen values more than 1, accounting for 79.95% of the total variance retained for the further analysis.


Table 1: Communalities



































































Extraction Method: Principal Axis Factoring. S_N=social norms; Att=attitude towards Organic Food Products; Beh=Organic Food Products Purchase behavior; In=Organic Food Products Purchase intentions; Acc=accessibility of the Organic Food Products.


TABLE 2: Rotated component matrix, Eigen value, Total Variance, Kaiser–Meyer–Olkin measure of sampling adequacy and Bartlett's test of sphericity.































































































































Eigen Value






% of Variance






Total Variance






Kaiser–Meyer–Olkin measure of sampling adequacy



Bartlett's test of sphericity Approx. chi‐square







Extraction method: Principal axis factoring

Rotation method: Varimax with Kaiser normalization.



Rotation converged in six iterations.



S_N=social norms; Att=attitude towards Organic Food Products; Beh=Organic Food Products Purchase behavior; In=Organic Food Products Purchase intentions; Acc=accessibility of the Organic Food Products.


Measurement Model:

After the exploratory factor analysis, measurement model (confirmatory factor analysis) is applied to maximum likelihood estimation (MLE) to test the validity of the constructs. In this study, confirmatory factor analysis presents that all the goodness‐of‐fit statistics were accepted to the threshold level of fit indices suggested by (Hair et al., 2015). (Chi square/df = 1.883, Comparative Fit Index = 0.972, and Root Mean Square Error of Approximation = 0.0684).


Cronbach's α coefficient values of each construct were ranging from 0.869 to 0.949 which are above recommended level of .70, suggested by (Hair et al., 2015). Convergent validity of the constructs were measured by using standardized factor loadings (>0.50), average variance extracted (>0.50), and composite reliability (>0.70), which were well above the recommendation criteria suggested by (Hair et al., 2015) shown in Table 3. And discriminant validity was evaluated by comparing average variance extracted (AVE) for each construct with squared correlations between constructs. Table 4 reported that square root of average variance extracted of each construct was more than their squared correlations that lead to the adequate discriminant validity (Hair et al., 2015).



TABLE 3: Measurement model: Reliability and validity

Construct items

Factor loading

Cronbach's α

Composite reliability


Social norms





S_N2: People who are very important to me think that I should use organic food products





S_N3: People who use organic food products have more prestige than those who don’t.





S_N4: I use organic food products because my family and friends uses it.





S_N5: I find using organic food products very trendy





Attitude towards Organic Food Products





Att2: I think organic food products are good for me





Att3: Overall my attitude towards organic food products is favorable





Att5: I think using organic food products is a wise Idea





Accessibility of the Organic Food Products.





Acc3: I have easy access to the organic food products





Acc4: I have knowledge and ability to differentiate organic food products from normal food products





Acc5: I can easily buy organic food products





Organic Food Products Purchase intentions





In2: I wish to purchase organic food products in near future





In3: Organic food products will always be my first preference while shopping





In4: I Intend to buy organic food products during my next shopping visit





Organic Food Products Purchase behavior





Beh4: I use organic food products more often than normal products





Beh5: I purchase organic food products over normal products while shopping





Beh1: I often buy organic food products whenever I go shopping






TABLE 4: Squared correlation matrix of constructs





































Note. The bold values represent the square root of average variance extracted for each construct, whereas the others represent the squared correlation between variables. S_N=social norms; Att=attitude towards Organic Food Products; Beh=Organic Food Products Purchase behavior; In=Organic Food Products Purchase intentions; Acc=accessibility of the Organic Food Products.


Structural model:

After EFA and measurement model PLS SEM was applied to test the hypotheses.


Figure 2 Research Model


Notes: * Significant at the 0.01 level. In the brackets t value is given with the factor loadings. And in the brackets with the constructs’ name R2 is mentioned.

TABLE 5 Summary of hypothesis testing


Coefficients (β)


Hypothesis Supported

H1Accessibility -----> Social Norms




H2 Accessibility -----> Attitude




H3 Social Norms -----> Attitude




H4 Attitude -----> Intentions




H5 Intentions -----> Behavior




H6 Age -----> Behavior




H7 Income -----> Behavior




*Significant at the 0.01 level

The research model was estimated with the help of standardized regression weights (β) and t-Statistics Values to test hypothesis in this empirical study shown in Figure 2 and Table 5.


The relationship of Att ‐‐‐‐> In (H4, β = 0.674, t-Value>1.96 ) was emerged to be the most significant path among the entire accepted hypothesis in the research model followed by the H5, the path between In‐‐‐‐> Beh (β = 0.637, t-value>1.96), Income  -----> Behavior, that is H7 (β = 0.462, t-value>1.96), Acc ‐‐‐‐> S_N, that is, H1 (β = 0.409, t-value>1.96), and the S_N‐‐‐‐> Att path, that is, H3 (β = 0.329, t-value>1.96). However, the path Acc ‐‐‐‐> Att (H2) was found to be the least significant effect (β = 0.317, t-value>1.96) in the research model. And in the end H6, Age -----> Behavior, (β = 0.040., t-value<1.96) was found insignificant.


Hence, the findings of the path coefficients established that all hypotheses were supported at the significant level except that the age doesn’t have any significant effect on the buying behavior. R2 of the model was 0.584 which is moderately significant according to Hair, Ringle, and Sarstedt (2011). The model also expressed predictive relevance as the cross-validated redundancy (Q2) of endogenous latent variable’s value was more than zero (Hair, Ringle, and Sarstedt, 2011). Goodness of fit (GoF) proposed by Tenenhaus, Vinzi, Chatelin, and Lauro (2005) was 0.72 which was very high as per the criterion of Wetzels, Odekerken-Schröder, and Van Oppen (2009).



Most important finding of the study was that organic food product’s buying behavior can be predicted by using consumers’ Social Norms, attitude, and accessibility of products. Accessibility of the organic food products was found to be more important factor than social norms to effect the consumer’s attitude towards buying organic food products. Also according to Sparks and Shepherd (1992), subjective norm’s explanatory power was relatively weaker, although significant. So to improve consumer’s attitude towards buying, first the marketers need to increase the accessibility of those products. Government and marketers need to endorse activities which can make organic products conveniently available to promote the consumers to use more of organic food products. This can be done by educating farmers about these practices and promoting the growth of organic products through incentives. In fact, Indian farmers were always good in organic farming but these days they have stated shifting towards inorganic farming, as it is more convenient and productive in short run, but can reduce soil self-sustainable fertility and increases pollution.

Effect of subjective norms was also found significant on the consumer’s attitude towards buying organic food products.  Subjective norm’s effect on attitudes is generally seen in the behaviors, which involve some kind of ethics in decision making and hence we can also associate buying organic food as ethical decision, involving environmental protection, animal welfare such as explained by Tarkiainen and Sundqvist, 2005) and Chang (1998). Government and marketers should work on increasing awareness and knowledge as it will lead to change the thoughts of the society and turn attitude more positive. Better advertising should be promoted as it will affect social norms and social norms do influence the customers to purchase organic food products.


Among Income and age, income was found to have significant effect on the Organic Food Products Purchase behavior, whereas age showed insignificant relationship. The results were similar to the studies done by Torjusen, Lieblein, Wandel, and Francis (2001); Loureiro et al. (2001); and Lockie (2006). This finding can help the marketers in making decisions about price, advertisement and other related policies. For example, Price should be reduced as people with low income group are inclined towards purchasing non-organic foods. Businesses and governments should work together to promote innovation in the field or organic farming to reduce the cost of production. Better production and distribution at lesser cost will reduce prices leading to increased demand, which will in turn will lead to economies of scale and lower production costs.



The sample size used for the purpose of this study was limited to 188 respondents and was selected through non-probability sampling method. In future researchers are suggested to conduct a more extensive research with larger sample. The researchers also suggest adding constructs or variables in future studies such as alternate attractiveness, perceived cost, perceived usefulness, perceived performance, education of the respondent, place of living, family member’s role distributions etc. for better understanding of attitude and behaviors. Additionally Experimental or longitudinal study can be performed in the future. Future researches can also study whether the reason for purchasing organic food products is just for the sake of fashion or whether the customers really wanted to purchase. Lastly, the impact of digitization on the sale of organic food products can also be studied in the future.



1.      ACNielsen- 2006. “Indians amongst the top 10 buyers of foods with “health supplements” globally but lack access to organic food products”, 20 February, Available from: www.acniels (accessed on 15 December 2006).

2.      Ajzen I. The theory of planned behavior. Organizational behavior and human decision processes. 1991 Dec 1;50(2):179-211.

3.      Ali J, Kapoor S, Moorthy J. Buying behaviour of consumers for food products in an emerging economy. British Food Journal. 2010 Feb 16;112(2):109-24.

4.      APEDA- 2018. Organic Products. Retrieved September 30, 2018, Available from

5.      ASSOCHAM, and Ernst and Young. The Indian Organic Market: A New Paradigm in Agriculture (Rep.). Retrieved September 30, 2018, Available from$File/ey-the-indian-organic-market-report-online-version-21-march-2018.pdf

6.      Bonfield EH. Attitude, social influence, personal norm, and intention interactions as related to brand purchase behavior. Journal of Marketing Research. 1974 Nov 1:379-89.

7.      Census India-2001. Variation in Population since 1901. Retrieved November 5, 2018, Available from

8.      Chang MK. Predicting unethical behavior: a comparison of the theory of reasoned action and the theory of planned behavior. Journal of business ethics. 1998 Dec 1;17(16):1825-34.

9.      Choo, H., Chung, J. E., and Thorndike Pysarchik, D. (2004). Antecedents to new food product purchasing behavior among innovator groups in India. European Journal of Marketing, 38(5/6), 608-625.

10.   Cicia G, Del Giudice T, Scarpa R. Consumers’ perception of quality in organic food: a random utility model under preference heterogeneity and choice correlation from rank-orderings. British Food Journal. 2002 Apr 1;104(3/4/5):200-13.

11.   Davies A, Titterington AJ, Cochrane C. Who buys organic food? A profile of the purchasers of organic food in Northern Ireland. British Food Journal. 1995 Nov 1;97(10):17-23.

12.   Gil JM, Gracia A, Sanchez M. Market segmentation and willingness to pay for organic products in Spain. The International Food and Agribusiness Management Review. 2000 Jun 1;3(2):207-26.

13.   Gracia Royo A, Magistris TD. Organic food product purchase behaviour: a pilot study for urban consumers in the South of Italy

14.   Gupta KB. Consumer behaviour for food products in India. In19th Annual World Symposium of the Indian Institute of Management, Bombay, India, June 2009 Jun 20 (pp. 20-21).

15.   Hill H, Lynchehaun F. Organic milk: attitudes and consumption patterns. British Food Journal. 2002 Aug 1;104(7):526-42.

16.   Kaur P, Singh R. Uncovering retail shopping motives of Indian youth. Young Consumers. 2007 Jun 19;8(2):128-38.

17.   Lockie S. Going organic: mobilizing networks for environmentally responsible food production. CABI; 2006.

18.   Loureiro ML, McCluskey JJ, Mittelhammer RC. Assessing consumer preferences for organic, eco-labeled, and regular apples. Journal of agricultural and resource economics. 2001 Dec 1:404-16.

19.   Magkos F, Arvaniti F, Zampelas A. Organic food: nutritious food or food for thought? A review of the evidence. International journal of food sciences and nutrition. 2003 Jan 1;54(5):357-71.

20.   Makatouni A. What motivates consumers to buy organic food in the UK? Results from a qualitative study. British Food Journal. 2002 Apr 1;104(3/4/5):345-52.

21.   McCarthy N. The World’s Largest Markets for Organic Products. Statista. 2015.

22.   McCluskey JJ, Loureiro ML , Mittelhammer RC. Assessing consumer preferences for organic, eco-labeled, and regular apples. Journal of agricultural and resource economics. 2001 Dec 1:404-16.

23.   Oliver RL, Bearden WO. Crossover effects in the theory of reasoned action: A moderating influence attempt. Journal of consumer research. 1985 Dec 1;12(3):324-40.

24.   Paul J, Rana J. Consumer behavior and purchase intention for organic food. Journal of consumer Marketing. 2012 Sep 7;29(6):412-22.

25.   Ringle CM, Sarstedt M, Straub D. A critical look at the use of PLS-SEM in MIS Quarterly.

26.   Saba A, Messina F. Attitudes towards organic foods and risk/benefit perception associated with pesticides. Food quality and preference. 2003 Dec 1;14(8):637-45.

27.   Sparks P, Shepherd R. Self-identity and the theory of planned behavior: Assesing the role of identification with" green consumerism". Social psychology quarterly. 1992 Dec 1:388-99.

28.   Tarkiainen A, Sundqvist S. Subjective norms, attitudes and intentions of Finnish consumers in buying organic food. British food journal. 2005 Dec 1;107(11):808-22.

29.   Tenenhaus M, Pages J, Ambroisine L, Guinot C. PLS methodology to study relationships between hedonic judgements and product characteristics. Food quality and preference. 2005 Jun 1;16(4):315-25.

30.   Torjusen H, Lieblein G, Wandel M, Francis CA. Food system orientation and quality perception among consumers and producers of organic food in Hedmark County, Norway. Food quality and preference. 2001 Apr 1;12(3):207-16.

31.   Tregear A, Dent JB, McGregor MJ. The demand for organically grown produce. British Food Journal. 1994 May 1;96(4):21-5.

32.   United Nations. (2015). World Population Prospects: The 2015 Revision (p. 26, ESA/P/WP.241). New York: Department of Economic and Social Affairs.

33.   Wetzels M, Odekerken-Schröder G, Van Oppen C. Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. MIS quarterly. 2009 Mar 1:177-95.

34.   Willer H, Lernoud J. Organic Agriculture Worldwide 2016: Current Statistics. Frick: Research Institute of Organic Agriculture (FiBL). 2016.

35.   Zanoli R, Naspetti S. Consumer motivations in the purchase of organic food: a means-end approach. British food journal. 2002 Sep 1;104(8):643-53.





Received on 20.11.2018         Modified on 12.12.2018

Accepted on 28.12.2018      ©AandV Publications All right reserved

Res.  J. Humanities and Social Sciences. 2019; 10(1):60-67.

DOI: 10.5958/2321-5828.2019.00011.1