Accepted Manuscript

Narrowing the gap: Factors driving organic food consumption

Brahim Chekima, Aisat Igau Oswald, Syed Azizi Wafa Syed Khalid Wafa, Khadidja
Chekima

PII: S0959-6526(17)31804-8
DOI: 10.1016/j.jclepro.2017.08.086
Reference: JCLP 10344

To appear in: Journal of Cleaner Production

Received Date: 21 November 2016

Revised Date: 11 August 2017
Accepted Date: 11 August 2017

Please cite this article as: Chekima B, Oswald AI, Wafa SAWSK, Chekima K, Narrowing the gap:
Factors driving organic food consumption, Journal of Cleaner Production (2017), doi: 10.1016/
j.jclepro.2017.08.086.

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 ACCEPTED MANUSCRIPT
Narrowing the Gap: Factors Driving Organic Food Consumption

Brahim Chekima1, *, Aisat Igau @ Oswald 2, Syed Azizi Wafa Syed Khalid Wafa3,
Khadidja Chekima4
1
Faculty of Business, Economics & Accountancy, Universiti Malaysia Sabah, Jalan UMS,
88400, Kota Kinabalu, Sabah, Malaysia

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2
Faculty of Business, Economics & Accountancy, Universiti Malaysia Sabah, Jalan UMS,
88400, Kota Kinabalu, Sabah, Malaysia

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3
Faculty of Business, Economics & Accountancy, Universiti Malaysia Sabah, Jalan UMS,
88400, Kota Kinabalu, Sabah, Malaysia

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4
School of Chemical Sciences & Food Technology, Faculty of Science and Technology,
National University of Malaysia, 43600, UKM Bangi, Selangor, Malaysia.

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Corresponding Author:
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Brahim Chekima,
Faculty of Business, Economics and Accountancy
Jalan UMS, 88400, Kota Kinabalu, Sabah, Malaysia
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Tel: +60128444253
Email: b.chekima@gmail.com
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 ACCEPTED MANUSCRIPT
Narrowing the Gap: Factors Driving Organic Food Consumption

Abstract

In the recent years, organic food has gained a great attention from researchers, yet the
consumption amount and growth in the market share is relatively low compared to
conventional food. The most consistent findings from previous studies have been the
inconsistency between consumers’ claims and their actual behavior – the so-called intention-
behaviour or attitude-behaviour gap. The majority of past studies focused on investigating the

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motivational factors to purchase organic food as a proxy to foster organic food consumption.
However, it is argued that preceding studies’ focus does not readily embrace the consumption
itself where purchasing may come secondary to consumption decision/motivation. The

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objective of this paper is to propose a new approach to determine factors influencing organic
food consumption by focusing on those who are consuming and not those considering
purchasing organic food. Indeed consumption is a more appropriate measure not confounded

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with shopping habits. Consumption also reflects high involvement with the product; and the
barriers and motivations are as real as the product itself, which makes it an ideal moment to
examine the motivation. A model that was developed was tested using the Partial Least
Square statistical analysis of an empirical sample of 133 consumers. The results indicated that

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product-specific attitude (PSA), sensory appeal (SA) and health orientation (HO) had a
significant positive influence on individuals’ organic food consumption. The moderating role
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of future orientation between PSA, HO and consumption were examined and found to be
significant. The result suggests that PSA and HO are stronger when future orientation is high.
The research provides a significant insight and better understanding of narrowing the attitude-
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behavior gap. It also adds a new momentum to the growing literature and prior findings on
consumer behavior towards organic foods.

Keywords: Attitude-Behaviour Gap, Organic Food Consumption, Product-Specific Attitude,

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Sensory Appeal, Health Orientation, Future Orientation.

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1. Introduction

Exploitation and destruction of the environment and natural resources have raised
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awareness of environmental protection, which in turn has encouraged "green consumerism"

(Moisander, 2007). Due to this condition, over the last 15 years, the practice of organic
agriculture and organic food, in general, has gained a huge interest. Organic agriculture
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sustains the health of people, the ecosystem, and the soil. According to the National Organic
Standards Board of the US Department of Agriculture (USDA), organic food provides long-
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term benefits to both the environment and people based on its emphasis on producing
nutritious food through the conservation of soil and water and use of renewable resources to
enhance environmental quality (Gold, 2007). According to Fisher et al. (2013) groceries
including food, account for up to one-third of the environmental impact of the overall
household consumption.

According to the latest global data on organic farming as presented in the 2016 edition
entitled “The World of Organic Agriculture” by the Research Institute of Organic Agriculture
(FiBL) and the International Foundation for 6Organic Agriculture (IFOAM), at the end of
2014 more than 43.7 M ha of agricultural land was recognized as organic producing with an
approximate growth of 0.5 M ha from the year 2013 having reached 80 billion US Dollars of
economic value (Willer and Lernoud, 2016). Australia leads with the largest organic

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agricultural area (17.2 M ha), followed by Argentina and the United States of America at 3.1
M ha and 2.2 M ha. Being the subject of this study, Malaysia is among the countries that have
the smallest share of the organic agricultural area (0.002 M ha) accounting for only 0.02% of
the total agricultural land (DOA, 2015), covering a total of 151 certified organic farms
(Suhaimee et al. 2016).

Despite the growth in organic food agriculture and a large number of studies
investigating factors driving consumer purchasing intention of organic food, yet the organic
food market share and consumer expenditure share of organic food and beverages are

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relatively low (FiBL-AMI, 2014). Belz and Peattie (2009) describe this problem by
summarizing it in perhaps the most consistent finding which is inconsistency between what
people claim (or express via values, attitudes, etc.) and what their actual behavior – the so-

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called “intention-behaviour”, “attitude-behavior” or green gap in consumer’s organic food
purchasing behavior and intention (e.g. Moser, 2015; Gleim and Lawson, 2014; Carrington et
al. 2010; Chatzidakis et al. 2007; DePelsmacker et al. 2005; Carrigan and Attalla, 2001). In

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fact, individuals surveyed by Greenindex (2012) who identify themselves as
‘environmentally-friendly’ are reported to be significantly higher by 50% than the actual
‘environmentally friendly’ buyers (Greenindex, 2012). Mahoney (2011) too reported that only
16% of people who expressed their concern about the environment actually act on these

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concerns. Following this green gap, there has been a great practical and theoretical challenge
in narrowing the gap between sustainable consumption “attitudes-behavior”, “intention-
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behavior” and actual consumption. Many current researchers call for further investigation to
address this challenge to help minimize or narrow the attitude-behavior and intention-
behavior gap that hinder consumers from translating their intention and attitude into practice
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through monitoring actual behavior to avoid the potential bias of their stated intention (Moser,
2015; Gleim and Lawson, 2014).

Various psychological/behavior theories have been applied to incorporate more

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dimension to investigate factors that form the intention and drive behavior to purchase organic
food, however, the gap is still evident today. Moisander et al. (2010) recommended observing
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broader consumer behavior changes than focusing solely on individual purchases. Miniero et
al. (2014) also point to the idea that marketing studies mainly focused on the intention to buy
than on effective consumer choice. However, there has been a lack of effort in re-assessing
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these theories and possible factors influencing actual consumption than just the purchase
intention of organic food. Past studies mostly focused on investigating the motivational
factors to purchase or intention to purchase organic food as a proxy to foster organic food
consumption. It is argued that preceding studies’ focus does not readily embrace the
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consumption itself where purchasing may come secondary to consumption

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decision/motivation. While a percentage of purchasing intention is realized through

purchasing behavior leading to actual purchasing of the product that ultimately leads to
consumption; if such approach is reversed to start with actual consumption behavior which is
succeeded by purchasing behavior only then the gap can be narrowed. It is important to
remember that food is a daily consumed product which means recurring consumption is very
solid and strong predictor of recurrence purchasing behavior.

The objective of this paper is to propose a new approach by assessing actual

consumption in which the focus is on individuals who are consuming and not those who are
merely considering purchasing organic food (not representative of those who consumes
organic food) as a sample since consumption is a more appropriate measure, not confounded
with shopping habits (Squires et al. 2001) to determine factors influencing organic food

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consumption. Consumption also reflects high involvement with the product; and the barriers
and motivations are as real as the product itself, which makes it an ideal moment to examine
the motivation and avoid bias relationship or effect. Verbeke and Vackier (2004) and Beharrel
and Dennison (1995) enlightened that involvement influences the formation of beliefs,
attitudes as well as behavioral outcomes, such as frequency of product usage via a
considerable cognitive effort. For the most part, the argument for the rationale of organic food
consumption as a construct does not seek to undermine past research of organic food purchase
intentions. Rather, it is an attempt to add to these efforts and the wider environmental
consumer behavior literature by reassessing the situation through the lens of consumption for

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it can be useful for producers, marketers and policy makers in their effort to foster organic
food consumption among citizens leading towards a more sustainable development.
Consequently, organic agricultural practice and consumption will continue to grow

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significantly due to increasing demand, and this will ensure conservation of the environment
through sustaining soil fertility, reduction in pesticide usage and pollution reduction compared
to conventional agricultural practice.

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The products’ quality cue which is sensory appeal along with health orientation,
product-specific attitude and the moderating role of future orientation are investigated in this
study to explain the variance in organic food consumption behavior. The rationale of

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investigating these factors is discussed in the literature review. The remainder of this paper is
structured as follows. Literature pertaining to factor influencing organic food consumption is
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reviewed and hypotheses developed. Next is the methodology of the study, findings,
discussions, implications, limitation of study and suggestions for future research.
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2. Research Model and Hypotheses

The research model begins with the development of three hypotheses regarding product-
specific attitude, sensory appeal and health orientation as determinants of organic food
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consumption. Formulation of the moderating effect of future orientation between product-

specific attitude, health orientation and organic food consumption follow. The proposed
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framework is shown in Fig.1.

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Fig. 1. Research model.

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2.1 Product-Specific Attitude

Understanding individuals attitudes better can aid marketers, policy makers, and even
producers to promote ethical consumer habits and encourage them to use or consume products
that are environmentally-friendly (Lin and Huang, 2012). Attitude plays a strong role in
influencing the behavior and is necessary to consumer behavior research (Follows and Jobber,
2000). In accordance with the theory of planned behavior (Ajzen, 1985), attitude postulated as
a dimension that shapes an individual’s behavior in question. In the context of organic food
purchase intention, the influence of attitude in inconclusive, as such several studies found a

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weak relationship (Rodríguez-Barreiro et al. 2013; Gupta and Ogden, 2009) and others
observed insignificant relationship (Moser, 2015; Verhoef, 2005). This seems due to the
typical utilization of attitude in a sense of environmental attitude. It could also be argued, in

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its entirety, attitude is viewed as attitude towards the environment in the context of
environmental behavior. Such approach is not realistic for it does not account for the objective
of sustainable consumption of organic food which emphasizes on the attribute of the product

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and not the merely environmental aspect. As Vermeir and Verbeke (2006) put forward, a
positive attitude towards sustainable products is a good starting point to stimulate sustainable
consumption. This aspect is parallel with the understanding that general attitude is commonly
not decisively predictive of specific behavior (Ajzen, 2008), including in the event of

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environmental consumer behavior (Bamberg, 2003).
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Marchand and Walker (2008) point out that individuals’ search for more sustainable
lifestyles is not only because they are environmentally conscious (hold a positive attitude
towards the environment) and understand the role they play in the environment, but also
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because they expect personal benefits. Personal benefits are attached to the product and could
be gained through consumption or usage of the product and would induce favorable attitude
towards the product solely. Hereby, product-specific attitude is defined as a predisposition to
answer favorably or not to a product in a consistent manner (Richard et al. 2016). The more
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closely matching, reflective or corresponding the attitude to a particular product the more
predictive is the attitude towards the behavior (Heberlein and Black, 1976; Ajzen, 1985). This
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evidenced by past studies asserting that general attitude regarding environment would not
necessarily spill over to another environmentally friendly context (Thogersen and Olander,
2003). Polonsky et al. (2012) suggested that in investigating behavior, it is important for
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researchers to examine both general as well as carbon-specific behavior to identify if attitudes

impact both. For instance, Oreg and Katz-Gerro (2006) in their cross-national study of 27
countries, found that the impact of environmental attitude varies across different types of
environmentally friendly behaviors such as reduced driving, recycling and environmental
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stewardship and that strongest effect was regarding the most general behavior of which is
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environmental stewardship. The construct of product-specific attitude would be more

appropriate in order to empirically investigate factors influencing individual’s consumption of
organic food as they are deemed highly involved with the product and express their actual
attitude on the product towards consumption and not the perception that could result in
potential bias. For this reason, it is hypothesized that:

H1: Product-specific attitude positively influences consumption of organic food.

2.2 Sensory Appeal

Human food choice and consumption preferences are decided based on a variety and diverse
complex elements including disgusted (perceived negative sensory appeal) which can vary

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according to the context of food (Lee and Yun, 2015; Steptoe et al. 1995). Grunert et al.
(2004) characterized product by the distinction between credence, search, and experience
characteristics (sensory characteristics). The latter represent product attributes, such as taste
and smell which can only be ascertained during and or after experiencing the product. In the
food choice process model by Furst et al. (1996) and Steenkamp (1990), sensory appeal
identified as one of the products’ quality category, is a functional and psychological benefit
consequence provided by the product and exerts their effect on food choice through the
negotiation of values by the consumer. Particularly, sensory properties are intrinsic cues of
product quality related to appearance, smell and taste of food (Steptoe et al. 1995). Sensory is

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known as the added value of organic food production besides ethical properties
(Schleenbecker and Hamm, 2013). Bandura (2007) emphasized that consumers place great
value on hedonistic food benefits (e.g. sensory characteristic) and are looking for certain traits

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that involve all their senses and stimulate a deeper association with the product. Steptoe et al.
(1995) established that sensory characteristics to be the most important factor consumers take
into consideration when choosing their food. However, its satisfaction level with every type of

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food -organic as well as non-organic might not be the same (Paul and Rana, 2012). Mueller
and Szolnoki (2010) recommended for researchers and producers to understand the interplay
of sensory attributes as it has to be optimized for a food product to be successful in the
marketplace. Nonetheless, there has been a lack of effort, especially in Malaysia in

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reassessing the role of sensory appeal for particularly investigating factors influencing the
organic food consumption. This investigation is more valid and actual since the individuals
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have experienced all the senses involved compared to perception in purchase intention
situation which based on presumption or guesses solely. One must realize that organic and the
inorganic food is different where the latter is produced conventionally in which it has sensory
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characteristics of its own (ie, taste, texture, color and taste) and not assume they share equal
priority and influence in choosing both types of food, therefore, the following hypothesis
deduced:
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H2: Sensory appeal positively influences consumption of organic food.

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2.3 Health Orientation

Past studies have indicated that organic food perceived safer and healthier for the environment
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and human than conventionally produced food (Michaelidou and Hassan, 2008) and health is
among main factors inducing consumer’s organic food purchase intention (Hsu et al. 2016;).
This health motive is commonly referred as health conscious or health orientation. Health
orientation is defined as an individual's motivation to hold healthy beliefs, attitudes and
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behaviors (Dutta et al. 2008). Correspondingly, health orientation is referred to as an

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individual's awareness and inclination to gear towards good health with respect to lifestyle
and diet. For instance, it can be demonstrated in a sense when individuals consider safety and
health benefit of the food product in their consumption decision. Kahkonen and Tuorila
(1999) in their experiment revealed that the taste of less-fat yogurts, frankfurters, chocolates
and yogurts perceived as less tasty compared to regular ones by respondents with weak health
food values. Instead, less-fat margarine perceived as more tasty compared to regular
margarine by higher health-oriented individuals. The health-valuing individuals tend to like
more the taste of low-fat dairy products, fruits and vegetables, and cereals than the unhealthy-
valuing individuals who favor the taste of sweets, meat and fish (Wadolowska et al. 2008).

Moving beyond these food examples, health conscious consumers might demonstrate
similar behavior and reaction to organic food as pleasant and regularly engage in such

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sustainable consumption as they value the health benefit greater than sensory perception or
diminish the impact of sensory perception priority compared to health benefits. On the
contrary, several studies found that health is not a driver for the consumer to purchase organic
food (Voon et al. 201; Michaelidou and Hassan, 2008). Squires et al. (2001) suggest that
motive for consuming organic food may vary between individuals from different countries, in
which organic food exporters should investigate and treat their export destinations carefully as
not all recipients countries perceive or view organic food benefits similar as their current or
previous recipient country's consumer. It is relevant to add to the literature by investigating
and validate the relationship between health orientation and organic food consumption by

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those who are actually engaged with the product consumption to avoid bias result/effect. Jager
(2000) describes that people who are highly involved are motivated to invest cognitive effort
in decision-making, where health orientation is considered cognitive factor (Cho et al. 2014).

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Therefore, the following hypothesis is deduced:

H3: Health orientation positively influences consumption of organic food.

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2.4 Future Orientation

Time has been a central construct of consumer behavior (Graham, 1981) and decision making

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(Meyers-Levy and Maheswaran, 1992) and its perceptions are part of the foundation from
which cognitive thinking and behavior developed (Zimbardo, 1994). Zimbardo and Boyd
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(1999) highlighted that time orientation is a prevalent and potent yet unrecognized impact on
a great number of individual behavior. Bergadaa (1990) proclaim that construct of time has
often been positioned as a side in which completely ignored or tacitly addressed in consumer
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behavior studies. Time or temporal orientation is “the preferential direction in a subject’s

behavior and reflection in so far as it is predominantly oriented towards events and objects in
the future, past or present” (Nuttin, 1985) and involved cognitively by focusing mostly on
either one of the orientations (Nuttin, 1985; De Voider, 1979). Of the interest of this study,
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future orientation is examined as a moderator between product-specific attitude, health

orientation and consumption of organic food. Future orientation is defined as the extent to
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which individuals involve in future-oriented behavior such as delaying gratification, investing

and planning (Kluckhohn and Strodtbeck 1961).
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Previous studies and literature concerning temporal orientation backed the concept that
when damage and benefits take place in the future, it leads to more optimism (Mowen and
Mowen, 1991). This is rationale with the prevention state, its guidance’s towards fulfilling
responsibilities, its awareness of negative consequences and associated losses pain and,
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therefore, correspond to environmentally friendly behavior (Bertoli et al. 2013). For instance,
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temporal orientation has been employed to predict general pro-environmental behavior

(Miniero et al. 2014) energy saving (Tangari and Smith, 2012), recycling (Ebreo and Vining,
2001) and conservation behaviors (Corral-Verdugo et al. 2006). Jager (2000) points out that
values could be included in a sustainable behavior or other behavioral studies to explain and
or minimize the gap between the motivational factors and behavior/behavioral intention.
Vermeir and Verbeke (2008) adds that to gain a better insight into pro-environmental
behavior, values should be analyzed, for value orientation could yield different strengths and
considered to exert influence on the determinants of behavior or intention. Ajzen and Fishbein
(1980) seminal work earlier also noted that the person’s value priorities may determine the
relative influence of the personal attitude on the consumer’s intention and behavior formation.
Despite substantial literature that calls for further investigation on the impact of temporal
orientation on consumer behavior, Zhou et al. (2013) observed that the inclusion of values as

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a moderator in empirical research of consumer decision making is still rare. Consequently, it
is salient and pertinent to investigate the influence of future orientation in the context of
organic food consumption as asserted by past studies that pro-environmental behavior entails
temporal conflict (the past and future) (Milfont et al. 2012). As Jackson (2005) explains that
by understanding and incorporating a sense of temporal orientation, companies could
communicate their marketing messages effectively to alter or influence individuals.
Therefore, the following hypotheses will be examined:

H4: The positive relationship between product-specific attitude and organic food consumption

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will be stronger when future orientation is high.

H5: The positive relationship between health orientation and organic food consumption will

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be stronger when future orientation is high.

3. Research method

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3.1. Procedure for data collection and analysis

This study employed a quantitative research design, using a structured questionnaire. The

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questionnaire developed for this study consists of three sections with question-statements
adapted from previous studies. The first part of the questionnaire collected data on the
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frequency of consuming/eating organic food based on self-reported behavior. The second part
of the questionnaire focused on factors influencing respondents organic food consumption,
including product-specific attitude (five items) (Urena et al. 2008; Steptoe et al. 1995),
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sensory appeal (four items) (Steptoe et al. 1995), health orientation (four items) (Michaelidou
and Hassan, 2008; Moorman, 1990; Squires et al. 2001) and future orientation (four items)
(Usunier and Valette-Florence, 2007). The third section comprised of questions pertaining to
demographics characteristics (e.g., gender, age, education attainment, employment and yearly
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household income). The items for all investigated variables were anchored on a 5-point Likert
scale with 1 referring to strongly disagree and 5 referring to strongly agree.
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3.2 Sample and data collection

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As this study is concerned with organic food consumption, a purposive sampling technique
was employed to collect data from only individuals who consume organic food as this
required to determine the actual factor influencing organic food consumption and not those
who are aware of organic food or possess intention to purchase organic food which is not an
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ideal proxy for consumption for not every purchase means personal consumption, but every
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consumption is preceded by purchase and considered highly involved with the product as
discussed earlier. A total of 150 questionnaires were distributed and 133 questionnaires
(88.6%) were returned as completed. Respondents were sampled from several states in
Malaysia and the participation of respondents in the study was on a voluntary basis.

To decide on the sample size of the respondents for this study, G*Power software was
used to calculate the minimum sample size required based on statistical power (Faul et al.
2009). Since the model had a 3 exogenous (predictors of organic food consumption), the
effect size was set as medium (0.15) and power needed 0.95. The sample size required was
119. This study set out to collect data which was equal to or slightly larger than the required
number. Given that the sample size exceeded 119, the power value in this study also exceeded
0.95. The minimum power required in social and behavioral science research is typically 0.8.

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It is also recommended for PLS-SEM to use “10 times” rule of thumb as the guide for
estimating the minimum required sample size (Hair et al. 2011). This rule suggests that PLS-
SEM only requires a sample size of 10 times the largest number of path or relationship in the
structural model, which would call for a minimum sample size of 30 in our model (that is, 10
x 3 structural paths = 30 organic food eaters). In both cases, it can safely conclude that a
sample of 133 is deemed adequate/sufficient in this study.

3.3 Common method variance

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Common method variance was examined since the data about the predictor and criterion
variables were collected from a single source (respondent) (Podsakoff et al. 2003). Podsakoff
and Todor (1985) also noted that: “Invariably, when self-reported measures obtained from the

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same sample are utilized in research, concern over same-source bias or general method
variance arise” (p. 65). Several remedies have been employed to detect this problem including
Harman's one-factor test. A factorial analysis without rotation in SPSS carried out and the

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result revealed that the first factor accounted for only 24.914% of the variance, much lower
than the majority indicating method bias is not a serious issue in this study. As shown in
Table (3), the construct correlation matrix shows that each of the inter-construct correlations
was not more than 0.90 (Bagozzi et al. 1991), confirming that both tests indicate that common

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method bias is not a problem in this study.
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4. Analysis and results

4.1. Profile of respondents

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Table 1. Profile of respondents

Characteristics Frequency (N=133) Percentage (%)
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Gender Male 51 38.3

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Female 82 61.7
Age (in years) 20-29 30 12.8
30-39 49 36.8
40-49 41 30.8
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50 and above 13 19.6

Highest academic Secondary school or lower 12 9.0
qualification Diploma or equivalent 29 21.8
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Bachelor Degree 73 54.9

Master Degree 15 11.3
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PhD 4 3.0
Employment Government 37 27.8
Private 74 55.6
Self-employed 19 14.3
Unemployed 3 2.3
Yearly Income MYR 50,000 and below 5 3.8
MYR 50,001 – 100,000 41 30.8
MYR 100,001 – 150,000 56 42.1
MYR 150,001 – 200,000 19 14.3
MYR 200,001 and above 12 9.0

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Table 1 shows the profile of respondents. The results show that 61.7% of respondents were
female and 38.3% were male. The respondents were categorized into four age groups: 20–29
years (12.8%), 30–39 years (36.8%), 40–49 years (30.8%) and 50 years and above (19.6 %).
Regarding educational attainment; most of the respondents have a bachelor degree (54.9%)
and diploma/certificate (21.8%), while a smaller number had a master degree (11.3%), Ph.D.
(3%) and 9% had attained secondary level education or lower. As for employment, 97.7% of
respondents have a job in the government, private sector or self-employed. In terms of yearly
income, most of the respondents earned between MYR 100,001 and MYR 150,000 (42.1%),
MYR 50,001 and MYR 100,000 (30.8%), MYR 150,001 and MYR 200,000 (14.3%), MYR

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200,001 and above (9%), while 3.8% earned MYR50,000 and below.

4.2. Model assessment using PLS-SEM

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The proposed relationships (research model) were analyzed using the partial least squares
technique (PLS-SEM) using SmartPLS 3.0 software (Ringle et al. 2015). PLS-SEM is a

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statistical technique that allows for simultaneous analysis of a large number of relationship in
a conceptual model. Anderson and Gerbing (1988) recommended two-stage analytical
procedures, firstly assessing the measurement model and followed by the assessment of
structural model (testing the hypothesized relationship). A bootstrapping method (5,000

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resamples) was employed to test the significance of the path coefficients and the loadings
(Hair et al. 2014).
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4.3. Assessment of the measurement model
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The assessment of the measurement model involves an assessment of reliability and validity.
Validity comprises of convergent validity and discriminant validity. Convergent validity is
usually evaluated through examining indicator loadings, Average Variance Extracted (AVE)
and Composite Reliability (CR) (Hair et al. 2011). Table 2 indicates that all the loadings were
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higher than 0.7, the AVE values were also higher than 0.5 and the composite reliabilities were
all higher than 0.7 as recommended by Hair et al. (2014). Therefore, the measurement model's
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convergent validity is acceptable.

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Table 2: Convergent validity of measurement model

Construct Item Loadings CRa AVEb

Product-Specific Attitude PSA1 0.946 0.961 0.830
PSA2 0.922
PSA3 0.905
PSA4 0.934
PSA5 0.845
Health Orientation HO1 0.907 0.936 0.786

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HO2 0.917
HO3 0.873
HO4 0.847

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Sensory Appeal SA1 0.778 0.855 0.597
SA2 0.806

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SA3 0.771
SA4 0.734
Future Orientation FO1 0.906 0.935 0.783
FO2 0.865

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FO3 0.864
FO4 0.905
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Organic Food Consumption OFC 1 1 1
a
AVE = (summation of squared factor loadings)/(summation of squared factor loadings) (summation of error
variances)
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b
Composite reliability = (square of the summation of the factor loadings)/ [(square of the summation of the
factor loadings) + (square of the summation of the error variances)]
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Table 3 shows the results of the discriminant validity. The discriminant validity of the
measures (the degree to which items differentiate among constructs or measure distinct
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concepts) (Chin, 2010) was examined by following the Fornell and Larcker (1981) criterion
of comparing the correlations between constructs and the square root of the AVE for that
construct. The square root of the AVE for each construct should be greater than all of the
correlations among the construct and the other constructs in the model (Hair et al. 2011).
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Table 3 shows the square roots of the AVEs for the constructs along the diagonal and the
correlations among the constructs, indicating that discriminant validity of the measures in this
study was established.
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Table 3 Discriminant validity of measurement model

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FO HO OFC PSA SA
FO 0.885
HO 0.253 0.887
OFC 0.844 0.224 1
PSA 0.843 0.190 0.855 0.911
SA 0.757 0.031 0.815 0.631 0.773
Diagonals (bolded) represent the square root of the average variance extracted while the off-diagonals are correlations among
constructs. Diagonal elements should be larger than off-diagonal elements in order to establish discriminant validity
PSA Product-Specific Attitude, HO Health Orientation, SA Sensory Appeal, FO Future Orientation, OFC Organic Food
Consumption

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4.4. Assessment of the structural model

In the second stage of analysis, the structural models for organic food consumption were
assessed. To assess the structural model, Hair et al. (2014) recommended looking at the beta,
R2 and the corresponding t-values through a bootstrapping with a resample of 5,000. Besides
these regular indicators, Soto-Acosta et al. (2015) and Hair et al. (2014) suggested that
researchers should also report effect sizes (f2) and predictive relevance (Q2).

Table 4 summarizes the results of the structural model analysis (hypotheses testing).

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Product-specific attitude (β = 0.513, p < 0.01), health orientation (β = 0.115, p < 0.01) and
sensory appeal (β = 0.385, p < 0.01) were positively related to organic food consumption
explaining 45.5% of the variance in consumption of organic food.Thus, H1, H2 and H3 were
supported.

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Next, the moderating effect or interaction of future orientation is assessed. The
interaction term between product-specific attitude and future orientation and also between

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health orientation and future orientation were created by mean centering the variables on
reducing multicollinearity. When the interaction effect was entered into the model, the R2
increased to 0.508, results in an R2 change of 5.4%. The result indicates that the interaction

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effect between the product-specific attitude and future orientation (H4) (β = 0.173, p< 0.05)
and between health orientation and future orientation (H5) (β = 0.198, p< 0.05) towards
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organic food consumption were significant. Thus, H4 and H5 were also supported. The effect
size f2 between the models as suggested by Cohen (1988) was 0.109, which is considered
small. Dawson (2014) suggested plotting of the interaction effect to see how the moderator
changes the relationship between product-specific attitude and organic food consumption and
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health orientation and organic food consumption. The result is presented in Fig.3 and Fig. 4.
the relationship between product-specific attitude and consumption of organic food is stronger
when individual’s future orientation is high. Similarly, health orientation towards organic
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food consumption is also stronger when future orientation is high. The R2 values are above
the 0.35 value as suggested by Cohen (1988) indicating a substantial model and considered
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significant and meaningful for the purposes of interpretation (Hair et al. 2014).

Table 4 Results of the Structural Model Analysis (Hypotheses Testing)

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Hypothesis Relationship Std Beta Std error t-value Decision f2 Q2 R2

H1 PSA → OFC 0.513 0.075 6.831** Supported 0.364 0.714 0.454
H2 HO → OFC 0.115 0.04 2.901** Supported 0.174
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H3 SA → OFC 0.385 0.079 4.878** Supported 0.155

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H4 PSA*FO → OFC 0.173 0.075 2.311* Supported 0.072 0.508

H5 HO*FO → OFC 0.198 0.091 2.175* Supported 0.038
**p < 0.01, *p < 0.05
PSA Product-Specific Attitude, HO Health Orientation, SA Sensory Appeal, FO Future Orientation,
OFC Organic Food Consumption

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Fig. 2. Interaction plot of future orientation on the relationship between PSA and OFC

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Fig. 3. Interaction plot of future orientation on the relationship between HO and OFC

As asserted by Sullivan and Feinn (2012), in assessing effect sizes (f2), Hair et al.
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(2014) suggested that the change in the R2 value should also be examined. The recommended
method is to examine the R2 change when a specified exogenous construct (predictor) is
removed from the model and assess whether the omitted variable has a substantive impact on
the endogenous construct. To evaluate the effect size, Cohen’s (1988) guideline which is
0.02, 0.15, and 0.35, representing small, medium, and large effects. From table 4, it can be
observed that all the relationships showed substantive impact with one large effect (PSA), two
medium effects (HO and SA) and two small effects (moderating effect).

The predictive relevance of the model by using the blindfolding procedure was also
assessed. If the Q2 value is larger than 0 the model has predictive relevance for a certain
endogenous construct and otherwise if the value is less than 0 (Hair et al. 2014; Fornell and

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Cha 1994). Hair et al. (2014) also stated that as a relative measure of predictive relevance,
values of 0.02, 0.15, and 0.35 indicate that an exogenous construct has a small, medium, or
large predictive relevance for a certain endogenous construct. Referring to table 4, the Q2
value is 0.714 suggesting that the model has large predictive relevance.

4.5 Importance performance matrix analysis

Further to this, the importance-performance map analysis (IPMA) also known as importance-
performance matrix or priority map analysis using consumption of organic food as the target

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variable(endogenous) is explained. It is a useful analysis approach in PLS-SEM that extends
the standard results reporting of path coefficient estimates by adding a dimension that
considers the average values of the latent variable scores (Ringle and Sarstedt, 2016). The

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IPMA contrasts the total effects, representing the predecessor constructs’ importance in
shaping a certain target construct, with their average latent variable scores indicating their
performance (Hair et al. 2014). The objective of assessing IPMA is to identify

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factor/exogenous variable that has a relatively high importance (strong total effect) for
explaining the variance of the endogenous target construct (Ringle and Sarstedt, 2016).
Following Ringle and Sarstedt’s (2016) recommendation against the inclusion of moderators,
construct of future orientation excluded from the IPMA analysis. This is due to the moderator

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interaction with predictor complicates any comparison of total effects that include moderating
effects with that predictor that lack a moderating effect (Hair et al. 2016; Ringle and Sarstedt,
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2016). Table 5 presents the results of total effects (importance) and index values
(performance) used for the IPMA while Fig 4. shows the plotted index values and total effects
scores out in a priority map.
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Table 5 Index values and total effects

Latent Variable Total effect of the latent variable Index Value (Performance)
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Organic Food Consumption

(Importance)
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Product-Specific Attitude 0.513 61.028

Health Orientation 0.115 49.994
Sensory Appeal 0.385 62.806
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Fig. 4. Importance-Performance Map Analysis (Priority Map) for Organic Food Consumption

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Based on Fig. 4, it can be seen that product-specific attitude and sensory appeal are
very important factors in influencing individual’s consumption of organic food as due to their
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relatively higher importance with values of 0.51 and 0.38 respectively. The performance of
these factors is also high in comparison to another construct. With respect to the construct of
health orientation, it lagged behind in performance and importance compared to both product-
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specific attitude and sensory appeal. Managerial activities aiming at increasing consumption
of organic food should give more focus or priority on improving the performance of product-
specific attitude and sensory appeal, as these constructs have high importance and
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performance. Health orientation follows as next priority.

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5. Discussion

This study has enhanced the understanding of what characterizes individuals' organic
food consumption practice in a developing country. It is among the earliest studies proposing
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and examining a conceptual model to understand how product-specific attitude, sensory

appeal and health orientation together with the moderating role of future orientation and to
explore the relative importance of these variables in explaining factors influencing
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individuals' actual consumption of organic food instead of being limited to intention to

purchase which is a less ideal of a proxy. The findings will be valuable for producers and
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marketers of organic food who are promoting sustainable consumption and are seeking to
know the underlying behavior of consumers/users of their products in their effort in increasing
the consumption of organic food. The study is of importance to the government in its attempt
to promote and instill sustainable consumption among its citizen holistically besides
introducing myOrganic certificate/label to create confidence among consumers regarding the
compliance of organic food in terms of production, preparation, storage and labeling
(Suhaimee et al. 2016). Foreign companies who intend to expand the market of their organic
products in developing countries may also consider the findings of this research while drafting
more effective strategies to better market and ensure more consumption of their product.

The findings of this study confirm that product-specific attitude and the sensory appeal
along with health orientation are salient factors affecting individuals' consumption of organic

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food. Future orientation also found to have a moderating effect on the relationship between
product-specific attitude, health orientation, and organic food consumption. This indicates that
future orientation raises the effect of product-specific attitude and health orientation, which
will boost individuals’ consumption of organic food. The result of the study confirms that
future orientation generally has a pervasive and powerful impact on people's behavior towards
long-term benefit like pro-environmental behavior, and specifically supporting its application
of organic food consumption (e.g. Miniero et al. 2014). This suggests that consumption of
organic food entail temporal conflict (future), which leads to more positive attitude and
stronger motivation. Producers and marketers should communicate organic food product by

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framing it to future gains for self and the environment making consumers who are consuming
or planning to consume organic food be more conscious of the long-term benefits while
making the instant commitment and confirming their loyalty to organic food.

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While past studies regularly employed and investigated the impact of environmental
attitude on various pro-environmental behaviors including organic and green purchase

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intention; this study, following the finding of Ajzen (2008) suggesting that general attitude is
commonly not decisively predictive of specific behavior, investigates attitude in a different
fashion by conceptualizing it in terms of attitude towards organic food. Our results provide
support for the relationship that product-specific attitude positively influences organic food

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consumption. Essoussi and Zahaf (2009) noted that typical organic food consumers tend to
gravitate towards labels such as “pesticide free”, “hormone-free”, “no chemicals”, “no
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pollutants”, “no antibiotics”, “no GMOs”; such foods are “natural” or purely organic. This
contributes to the formation of positive/favorable view/attitude towards organic food
perceiving it more nutritious, safer and healthier, and ultimately creating confidence to
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consume it. Based on the findings, individuals who consume organic food place a
considerable attention on the attributes of organic food such being pesticide free, more
nutritious, contains more vitamins and safer compared to normal food eventually forming a
favorable attitude. It is recommended that practitioners jointly with the government
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emphasize more on communicating these characteristics aggressively to the public at large.

This will stimulate the new ones as well as increase the current organic consumers' attitude to
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expand the number of organic food consumers which will lead to more demand and ensure
significant growth in organic agriculture. Practitioners could also communicate such attributes
through promotional campaign, a well-designed product packaging, credibly certified
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labeling, in-store digital and promotional advertisement that effectively highlights how
organic food is grown and processed, as well as its benefits. Similarly, this will stimulate and
form a favorable attitude towards the product ultimately leading to actual consumption as
individuals perceive it to fulfill their needs and want for food consumption.
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Apart from enhancing product-specific attitude, sensory appeal assumed a substantial

priority in determining organic food consumption. The finding underpins Steptoe's et al.
(1995) claim that sensory appeal is an essential underlying motive for choosing and
consuming food. This indicates that individuals’ consumption experience/functional effect is
stimulated by the sensory characteristics of organic food. This is consistent with the findings
by Furst et al. (1996) and Steenkamp (1990) explaining that sensory appeal is identified as
one of the products’ quality categories that represent a functional and psychological benefit
consequence provided by the product and exerts their effect on food choice through the
negotiation of values by the consumer. This suggests that producers should further develop
and improve the aspects of quality cue for organic food in term of the look, smell and taste.
This can be further developed by setting an appealing/attractive standard in presenting,
packaging and displaying organic food so that it may have a pleasant visual that can stimulate

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and boost individuals' sensory emotions. The taste also needs to be given attention by
ensuring usage of the best seeds and close monitoring and care during growth of the plants as
well as a proper and best practice of storage that ensures good taste and preserves its freshness
to be more appealing to consumers. Producers should not neglect these factors as they have a
significant effect in an engaging individual with consumption of organic food.

Health orientation yields a positive effect in influencing organic food consumption. A

possible explanation is that organic food can serve the needs of health oriented individuals
due to it natural and free of pesticides and chemicals making organic food safer to consume

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compared to conventional food. In other words, health oriented individuals typically weigh
healthfulness of the food they consume and organic food meets this consideration. This
finding is inconsistent with past studies of purchasing intention findings that health

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orientation is not a driver for the consumer to purchase organic food (e.g. Voon et al. 2011;
Michaelidou and Hassan, 2008). It is important to recall that these findings are based on
perception (purchase intention) and also indicated by convenient respondents who are not

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necessarily engaged in the act of consumption where they will help to provide a valid or
unbias response, whereby this study is based on actual consumption and participated by only
those who are actively consuming organic food providing more accurate and actual response
on the motivation or driver. Practitioners again should highlight the health benefits and gains

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of consuming organic food so they may increase loyalty among current consumers and attract
new ones. The government should also put effort to create public awareness campaign
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promoting healthy diets by observing organic food consumption practice.

6. Limitations and future research directions

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Despite the contributions of this study, some limitations are worth mentioning for future
research. First, since the research data were collected in Malaysia, future studies focused on
samples from other countries could be valuable in conducting a cross-cultural comparison to
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substantiate current research finding.

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Second, future research could compare the same framework or different ones of
different variables in consumption setting (comprise of those who are actively consuming
organic food) and in purchasing intention context. Such similarities or differences will help
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draw more justification on the dilemma of investigating purchase intention to understand and
identify organic food consumption factors and closing the gap between attitude-behavior and
intention-behavior.
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Third, different psychological or socio-demographic moderator could be explored in

improving the understanding of sustainable consumption determinants with special attention
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to organic food consumption behavior. It also seems necessary to analyze the influence of the
cultural and economic formation of the population as well as governmental/legislative
measures.

References

Ajzen, I., 1985. From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckman
(Eds.), Action-control: From cognition to behavior. Heidelberg, Germany: Springer.
Ajzen, I., 2008. Consumer attitudes and behavior. In C. P. Haugtvedt, P. M. Herr & F. R. Cardes
(Eds.), Handbook of Consumer Psychology. New York: Lawrence Erlbaum Associates.
Ajzen, I., Fishbein, M., 1980. Understanding attitudes and predicting social behavior. Englewood
Cliffs, NJ: Prentice-Hall.

16
 ACCEPTED MANUSCRIPT
Anderson, J.C., & Gerbing, D. W. 1988. Structural Equation Modeling in Practice: A Review and
Recommended Two-Step Approach. Psychological Bulletin, 103 (May). 411-423.
Bagozzi, R.P., Yi, Y., Phillips, L.W., 1991. Assessing construct validity in organizational research.
Administrative Science Quarterly. 36(3), 421–458.
Balderjahn, I., 1988. Personality variables and environmental attitudes as predictors of ecologically
responsible consumption patterns. Journal of Business Research. 17, 51–56.
Bamberg, S., 2003. How does environmental concern influence specific environmentally related
behaviors? a new answer to an old question. Journal of Environmental Psychology. 23, 21-32.
Bandura, A., 2007. Impending ecological sustainability through selective moral disengagement.
Internal Journal of Innovation and Sustainable Development. 2 (1), 8 – 35

PT
Beharrell, B., Denison, T.J., 1995. Involvement in a routine food shopping context. British Food
Journal. 97(4), 24 – 29.
Bertoli, G., Bonera, M., Codini, A., Corvi, E., Miniero, G., 2013. Green behavior: how to
encourage it. Proceedings of the 42nd Annual European Marketing Academy Conference

RI
(EMAC), 4–7 June 2013, Istanbul.
Carrigan, M. & Attalla., A. 2001. The Myth of the Ethical Consumer – Do Ethics Matter in Purchase
Behaviour?. Journal of Consumer Marketing. 18(7), 560–577.

SC
Carrington, M., Neville, B.A., Whitwell, G.J., 2010. Why ethical consumers dot’s walk their talk:
towards a framework for understanding the gap between the ethical purchase intentions and
actual buying behaviour of ethically-minded consumers. Journal of Business Ethics. 97, 139–
158.

U
Chatzidakis, A., Hibbert, S., Smith, A. P., 2007. Why People Don't Take their Concerns about Fair
Trade to the Supermarket: The Role of Neutralisation. Journal of Business Ethics. 74, 89-100.
AN
Chin, W.W. 2010. How to write up and report PLS analyses. In V.E. Vinzi, W.W. Chin, J. Henseler,
H. Wang (Eds.), Handbook of partial least squares. Berlin, Germany: Springer.
Cho, J., Park, D., Lee, H. E., 2014. Cognitive Factors of Using Health Apps: Systematic Analysis
of Relationships Among Health Consciousness, Health Information Orientation, eHealth
M

Literacy, and Health App Use Efficacy. Journal of Medical Internet Research. 16(5), 125.
Cohen, J. 1988. Statistical power analysis for the behavioral sciences. Mahwah, New Jersey:
Lawrence Erlbaum.
D

Corral-Verdugo, V., Fraijo-Sing, B., Pinheiro, J.Q., 2006. Sustainable behavior and time
perspective: Present, past, and future orientations and their relationship with water
TE

conservation behavior. Interamerican Journal of Psychology. 40(2), 139-147.

Dawson, J.F. 2014. Moderation in Management Research: What, Why, When, and How. Journal of
Business and Psychology. 29(1), 1-19.
De Pelsmacker, P., Driesen, L., Rayp, G., 2005. Do Consumers Care About Ethics? Willingness to
EP

Pay for Fair-Trade Coffee. The Journal of Consumer Affairs. 39(2), 363–385.
De Voider, M.L. 1979. Time orientation: A review. Psychologica Belgica. 19, 61-79.
DOA, 2015. Reports on the Progress Production for Organic Certified Farm. Jabatan Pertanian
Malaysia. Putrajaya
C

Dutta, M.J., Bodie, G.D., Basu, A., 2008. Health disparity and the racial divide among the nation's
youth: Internet as a site for change? In A. Everett (Ed.), Learning race and ethnicity: Youth
AC

and digital media (pp. 175-198). The John D. and Catherine T. MacArthur Foundation Series
on Digital Media and Learning. Cambridge, MA: The MIT Press.
Ebreo, A., Vining, J., 2001. How similar are recycling and waste reduction? Future orientation and
reasons for reducing waste as predictors of self-reported behavior. Environment and Behavior.
33(3), 424-448.
Essoussi, H., M. Zahaf., 2009. Exploring the decision-making process of Canadian organic food
consumers. Motivations and trust issues. Quantitative market research: International Journal.
12(4), 443-459.
Faul, F., Erdfelder, E., Buchner, A., Lang, A.-G., 2009. Statistical power analyses using
G*Power 3.1: tests for correlation and regression analyses. Behav. Res. Methods. 41, 1149–
1160.

17
 ACCEPTED MANUSCRIPT
Fisher, K., James, K., Sheane, R., Nippress, J., Allen, S., Cherruault, J.Y., Fishwick, M., Lillywhite, R.
Sarrouy, C., 2013. An initial assessment of the environmental impact of grocery products:
final report – March. Product Sustainability Forum (Ed.)
Fornell, C., Cha, J., 1994. Partial least squares. In R. P. Bagozzi (Ed.), Advanced methods in
marketing research (pp. 52–78). Cambridge: Blackwell.
Fornell, C., & Larcker, D. F. 1981. Evaluating Structural Equation Models with Unobservable
Variables and Measurement Error. Journal of Marketing Research. 18(1), 39–50.
Furst, T., Connors, M., Bisogni, C., Sobal, J., Falk, L. 1996. Food choice: a conceptual model of the
process. Appetite. 26, 247-265.
Gefen, D., Straub, D.W., Boudreau, M. C., 2000. Structural Equation Modeling and Regression:

PT
Guidelines for Research Practice. Communications of the Association for Information
Systems. 4(1), 1-70.
Gleim. M., Lawson. S., 2014. Spanning the gap: an examination of the factors leading to the green
gap", Journal of Consumer Marketing, 31(6/7), 503 – 514

RI
Gold, M.V., 2007. Sustainable Agriculture: Definitions and Terms. Related Terms. Retrieved May 3,
2016, from https://www.nal.usda.gov/afsic/sustainable-agriculture-definitions-and-terms-.
Graham, R.J. 1981. The role of perception of time in consumer research. The Journal of Consumer

SC
Research. 7, 335–342.
Grunert, K., Bredahl, L., Brunsø, K., 2004. Consumer perception of meat quality and implications
for product development in the meat sector. A review. Meat Science. 66, 259–272.
Gupta, S., Ogden, D.T., 2009. To buy or not to buy? A social dilemma perspective on green buying.

U
Journal of Consumer Marketing. 26(6), 376-391.
Hair, J.F., Hult, G.T.M., Ringle, C.M., Sarstedt, M., 2014. A Primer on Partial Least Squares
AN
Structural Equation Modeling (PLS-SEM). Sage Publication: Los Angeles.
Hair, J.F., Ringle, C.M., Sarstedt, M., 2011. PLS-SEM: Indeed a silver bullet. Journal of
Marketing Theory and Practice. 19(2), 139–151.
Heberlein, T.A., Black, J.S., 1976. Attitudinal specificity and the prediction of behavior in a field
M

setting. Journal of Personality and Social Psychology. 33, 474-479.

Hsu, S. Y., Chang, C. C., Lin, T.T., 2016. An analysis of purchase intentions toward organic food on
health consciousness and food safety with/under structural equation modeling. British Food
D

Journal. 118(1), 200-216.

Jackson, T., 2005. Motivating Sustainable Consumption: A Review of Evidence on Consumer
TE

Behavior and Behavior Change. A Report to the Sustainable Development Research Network.
Policy Studies Institute, London. http://hiveideas.com/attachments/044_
motivatingscfinal_000.pdf (accessed on January 23rd 2016).
Jager, W., 2000. Modelling Consumer Behavior, Groningen: University of Groningen.
EP

James, W., 1890. The principles of psychology. New York: Holt.

Kahkönen, P., Tuorila, H., 1999. Consumer responses to reduced and regular fat content in different
products: effects of gender, involvement and health concern. Food Quality and Preference. 10,
83-91.
C

Kluckhohn, F.R., Strodtbeck, F.L., 1961. Variations in value orientations. Evanston, IL:
Lee, H.J., Yun, Z.S., 2015. Consumers’ perceptions of organic food attributes and cognitive and
AC

affective attitudes as determinants of their purchase intentions toward organic food. Food
Quality and Preference. 39, 259–267.
Lin, P.C., Huang, Y.H., 2012. The influence factors on choice behaviour regarding green
products based on the theory of consumption values. Journal of Cleaner Production. 22(1),.
11-18.
Mahoney, S., 2011. Study: green gap is bigger than ever. Available at:
www.mediapost.com/publications/article/148938/study-green-gap-is-bigger-than-ever.html
(accessed 28 March 2016).
Marchand, A., Walker, S., 2008. Product development and responsible consumption: designing
alternatives for sustainable lifestyles. Journal of Cleaner Production. 16(11), 1163-1169.
Meyers-Levy, J., Maheswaran, D., 1992. When Timing Matters: The Influence of Temporal Distance
on Consumers’ Affective and Persuasive Responses. Journal of Consumer Research. 19, 424–
433.

18
 ACCEPTED MANUSCRIPT
Michaelidou, N., Hassan, L.M., 2008. The role of health consciousness, food safety concern and
ethical identity on attitudes and intentions towards organic food. International Journal of
Consumer Studies. 32(1), 163-170.
Milfont, T.L., Wilson, J., Diniz, P., 2012. Time perspective and environmental engagement: A meta-
analysis. International Journal of Psychology. 47, 325-334.
Miniero, G., Codini, A., Bonera, M., Corvi, E., Bertoli, G., 2014. Being green: from attitude to actual
consumption. Int. J. Consum. Stud. 38, 521–528.
Moisander, J., 2007. Motivational complexity of green consumerism. International Journal of
Consumer Studies. 31, 404–409.
Moisander, J., Markkula, A., Eräranta, K., 2010. Construction of consumer choice in the market:

PT
challenges for environmental policy. International Journal of Consumer Studies, 34, 73–79.
Moser, A.K., 2015. Thinking green, buying green? Drivers of pro-environmental purchasing behavior.
Journal of Consumer Marketing. 32(3), 167-175.
Mowen, J.C., Mowen, M.M., 1991. Time and outcome valuation: implications for marketing

RI
decision making. Journal of Marketing. 55, 54–62.
Mueller, S., Szolnoki, G., 2010. The relative influence of packaging, labelling, branding and sensory
attributes on liking and purchase intent: consumers differ in their responsiveness. Food Qual.

SC
Preference. 21(7), 774–783.
Nuttin, J., 1985. Future time perspective and motivation: Theory and research method. Hillsdale, NJ:
Erlbaum.
Oreg, S., Katz-Gerro, T., 2006. Predicting Proenvironmental Behavior Cross-Nationally: Values, the

U
Theory of Planned Behavior, and Value-Belief-Norm Theory. Environment and Behavior. 38,
462-483.
AN
Paul, J., Rana. J., 2012. Consumer behavior and purchase intention for organic food. Journal of
Consumer Marketing. 29(6), 412 – 422.
Podsakoff, P.M., Todor, W.D., 1985. Relationships between leader reward and punishment
behavior and group processes and productivity. Journal of Management. 11(1), 55-73.
M

Podsakoff, P.M., MacKenzie, S.B., Lee, J.Y., Podsakoff, N.P., 2003. Common method biases in
behavioral research: A critical review of the literature and recommended remedies. J. Appl.
Psychology. 88(5), 879–903.
D

Polonsky, M.J., Vocino, A., Grau, S.L., Garma, R., Ferdous, A.S., 2012. The impact of general
and carbon-related environmental knowledge on attitudes and behaviour of US consumers.
TE

Journal of marketing management. 28(3-4), 238- 263.

Richard, M., Habibi, M.R., Laroche, M., Paulin, M., 2016. Recent Advances in Online Consumer
Behavior. In I. Lee (Ed.), Encyclopedia of E-Commerce Development, Implementation, and
Management. Hershey, PA: Business Science Reference.
EP

Ringle, C.M., Sarstedt, R.M., 2016. Gain more insight from your PLS-SEM results: the importance-
performance map analysis. Industrial Management & Data Systems, 116(9).
Ringle, C.M., Wende, S., Becker, J.-M., 2015. SmartPLS 3, www.smartpls.com
Rodríguez-Barreiro, L.M., Fernandez-Manzanal, R., Serra, L.M., Carrasquer, J., Murillo, M.B.,
C

Morales, M.J., Calvo, J.M., Valle, J.D., 2013. Approach to a causal model between attitudes
and environmental behaviour. A graduate case study. Journal of Cleaner Production. 48, 116-
AC

125.
Schleenbecker, R., Hamm, U., 2013. Consumers’ perception of organic product characteristics. A
review. Appetite. 71, 420-429.
Smith, S., Paladino, A., 2010. Eating clean and gren? Investigating consumer motivations towards
the purchase of organic food. Australasian Marketing Journal. 18, 93-104.
Soto-Acosta, P., Popa, S., Palacios-Marqués, D., 2015. E-business, organizational innovation and
firm performance in manufacturing SMEs: an empirical study in Spain. Technological and
Economic Development of Economy, doi:10.3846/20294913.2015.1074126.
Squires, L., Juric, B., Cornwell, T., 2001. Level of market development and intensity of organic
food consumption: cross-cultural study of Danish and New Zealand consumers. Journal of
Consumer Marketing. 18(5), 392-409.
Steenkamp, J. B. E. M. 1990. Conceptual model of the quality perception process. Journal of Business
Research. 21, 309-333.

19
 ACCEPTED MANUSCRIPT
Steptoe, A., Polland, T.M. & Wardle, J. 1995. Development of a measure of the motives underlying
the selection of food: the food choice questionnaire. Appetite. 25(3), 267-284.
Suhaimee, S., Ibrahim, I.Z., Abd Wahab, M.A.M., 2016. Organic Agriculture in Malaysia. FFTC
Agricultural Policy Articles.
Sullivan, G. M., Feinn, R., 2012. Using Effect Size - or why the p Value is not Enough. Journal of
Graduate Medical Education. 4(3), 279–282.
Tangari, A.H., Smith, R.J., 2012. How the temporal framing of energy savings influences consumer
product evaluations and choice. Marketing Letters. 29, 198–208.
Thogersen, J., Olander, F., 2003. Spillover of environment-friendly consumer behavior. Journal of
Environmental Psychology. 23(3), 225-236.

PT
Urena, F., Bernabeu, R., Olmeda, M., 2008. Women, men and organic food: differences in their
attitudes and willingness to pay. A Spanish case study, International Journal of Consumer
Studies. 32, 18-26.
Usunier, J.C., Valletta-Florence, P. 2007. The Time Styles Scale. A review of developments and

RI
replications over 15 years. Time and Society. 16(2/3), 333–366.
Verbeke, W., Vackier, I., 2004. Profile and effects of consumer involvement in fresh meat. Meat
Science, 67, 159-168.

SC
Vermeir, I., Verbeke, W., 2008. Sustainable food consumption among young adults in Belgium:
Theory of planned behaviour and the role of confidence and values. Ecological Economics.
64(3), 542–553.
Voon, J.P., Ngui, K.S., Anand, A., 2011. Determinants of Willingness to Purchase Organic Food:

U
An Exploratory Study Using Structural Equation Modeling. International Food and
Agribusiness Management Review. 14(2), 103- 120.
AN
Wadolowska, L., Babicz-Zielinska, E., Czarnocinska, J. 2008. Food choice models and their relation
with food preferences and eating frequency in the Polish population. Food Policy. 33, 122–
134.
Willer, H., Lernoud, J., (Eds.) 2016. The World of Organic Agriculture. Statistics and Emerging
M

Trends 2016. Research Institute of Organic Agriculture (FiBL), Frick and IFOAM – Organics
International, Bonn.
Xie, B., Wang, L., Yang, H., Wang, Y., Zhang, M., 2015. Consumer perceptions and attitudes of
D

organic food products in Eastern China. British Food Journal. 117(3), 1105-1121.
Zhou, Y., Thogersen, J., Ruan, Y., Huang, G., 2013. The moderating role of human values in
TE

planned behavior: the case of Chinese consumers' intention to buy organic food. Journal of
Consumer Marketing. 30(4), 335-344.
Zimbardo, P.G., Boyd, J.N., 1999. Putting temporal in perspective: a valid, reliable individual-
differences metric. Journal of Personality and Social Psychology. 77, 1271–1288.
EP

Zimbardo, P.G. 1994. Foreword. In Z. Zaleski (Ed.). Psychology of Future Orientation. Lublin,
Poland: Scientific Society of KUL.
C
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Highlights

• Examined factors influencing actual consumption of organic food to narrow the attitude-
behavior gap

• Product-specific attitude, health orientation, and sensory appeal are important

motivational drivers of organic food actual consumption

• High future orientation found to strengthen the significant positive relationships

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• Provides a significant insight and better understanding of actual organic food
consumption behavior

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• Aid producers and retailers to communicate and promote organic food effectively

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