The zero-effect in food attributes The impact of zero-value attributes in multicomponent food products, on consumer choice.

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Final Master Thesis

MSc. in Business Administration – Consumer Marketing specialization

The zero-effect in food attributes

The impact of zero-value attributes in multicomponent food products, on consumer choice.

Student : Christina Tzani Student Number : 13650904 Date: 24/06/2022

Supervisor : Dr. Sadaf Mokarram Dorri EBEC approval number : 20220409120414

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STATEMENT OF ORIGINALITY

This document is written by Christina Tzani who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

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Abstract

Zero as a special number has been researched in psychology over the years, alongside its implications in domains such as price, rewards, and probability. This research takes this investigation into a specific scenario of the food domain, regarding product attributes.

Given the increase of health consciousness among consumers, when faced with a choice of selecting several products (or nothing), people tend to choose the option that gives them the most health benefits. This research argues that products with zero-value attributes increase the perceived healthiness of a product and hence increase its choice share on the choice set.

This proposal is tested by comparing consumer purchase choice of granola bars across conditions, by maintaining the attribute value differences between the products, but varying the values of two undesirable attributes such that they are set either at zero or a low positive.

It resulted that having zero-value attributes does not lead to a higher perceived healthiness of a product. Alternative explanations are provided. It is however proven that the product’s choice share is increased when the perceived healthiness is higher.

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Introduction

Zero as a special number has been researched in psychology extensively over the years, however, its relevance, in the marketing and business field, was not so clear. As Alfred North Whitehead mentioned; “No one goes and buys zero fish in real life”.

Zero can be incorporated across various components of a product, such as price, quantity, the value of certain ingredients, and more. All of which, lead towards an implication in consumer decision-making. This research will focus to influence decision-making in the food domain, by using zero as a special number.

Poor food choices can have an impact not only on an individual level but also on society. They increase the probability of obesity, heart diseases, and other blood-related problems which in turn carry economic costs (Guthrie et al., 2015). WHO states that: “Spending on health has grown faster than the economy in most countries in the European Region ” (WHO, 2021). This context clearly states that it is time to nudge people’s buying behavior towards healthier food choices.

The work of Kahneman and Tversky (1979), which is one of the most influential ones in the psychology of zero, showed that zero as a special number applies to probability, reward, and price. Cognitive dissonance theory states that people will like a task more when they receive a zero reward, compared to a small positive reward (Festinger and Carlsmith 1959).

Theoretical perspectives say that consumers base their decision when purchasing products on a cost-benefit analysis (Ratchford 1982). However, this is not always true. It has been shown in previous research that when prices are mentioned, people apply market norms, but when the prices are absent (i.e., zero), they apply social norms to decide between alternatives (Heyman

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and Ariely 2004). Thus, this led to vast literature research on how the concept of zero affects people’s buying behavior and purchase decision-making.

Shampanier et al., (2007) investigated and extended the research on the psychology of zero, especially in the price domain. They demonstrated that zero price makes consumers perceive the benefits associated with free products as higher. When faced with a decision between two products, one of which is free, consumers will overestimate the free product as not only with the lowest costs but also with the highest value. They proved that people select the free product even when this means letting go of an objectively better option. Shampanier et al., (2007) study focused on the impact of zero-price on the product and researched three potential explanations for the effect; social norms, affect, and mapping difficulties. Out of the three, affect resulted to be the main cause of the zero effect. Affect is defined as the psychological mechanism that explains how the free option evokes a positive response, which in turn is used as a cue in decision making (Finucane et al., 2000, Slovic et al., 2002, Gourville and Soman 2005).

Shampanier et al., (2007) study laid the groundwork for other researchers to extend the work of zero effect, beyond probability, reward, and price. If the affective response could be replicated in other domains, this means that one can use the zero effect to increase the choice share of a product with an attribute value equal to zero. This research will focus specifically on the food domain and will try to test the zero effect among unfavorable product attributes.

Unfavorable attributes are those that when in high quantity, they diminish the healthiness of the product, such as saturated fat and sodium (Plasek et al., 2020).

Palmeira (2011) was the first study to explore the zero-price effect in the food domain with products such as yogurt, regarding attributes such as fat. The study argued that a zero attribute removes a reference point that consumers use to evaluate the size of attribute differences and that’s what is called the “Zero Comparison” effect. According to this effect, an option can

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increase its choice share by changing the value of an undesirable attribute from zero to a non- zero number.

This study will extend and cross-reference the findings from the study by Palmeira (2011), by altering the context where the consumer will be making decisions, the number of manipulated attributes, and the choice set size. Specifically, it will examine how multi-component food products’ evaluation is impacted by the zero-value effect on more than one unfavorable attribute such as saturated fat, and sodium. The study aims to investigate this effect on consumer choice and explore the role of perceived healthiness as the underlying mechanism.

Wen Mao (2020) demonstrates that the ‘’Zero Comparison” can be reduced by choice set size while holding attribute dimensions constant. In real life, products are not compared in a binary set, therefore it is interesting to check if the larger choice sets will diminish and or even reverse the effect.

Hence, this research aims to expand the existing literature body on the zero-comparison effect, in the food domain, by looking at the moderating effect of consideration choice. We would gain more insights on how zero as a special number portrays itself in a product attribute other than price. This paper aims to expand on understating how consumers' decision-making is influenced by heuristics such as multiple ingredient effect & context effects. The following research questions will be of focus:

RQ1a: How does having zero-value attributes (saturated fat, sodium) in a multi-component food product, affect the consumer choice of these products?

RQ1b: Is the relationship between the zero-value effect in product attributes (saturated fat, sodium) and its purchase choice mediated by the perceived healthiness (high or low) of the product?

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RQ1c: Is the relationship between the zero-value effect in product attributes (saturated fat, sodium) and perceived healthiness (high, low) moderated by choice set size (binary vs quartet)?

By manipulating such undesirable attributes and having them reduced to zero, this study will investigate if the zero-effect holds. Making people alter their eating behaviors starts with tampering with factors that affect their decision-making.

This research has several managerial implications. Companies have an opportunity to reduce the consumption of unhealthy foods by manipulating the way the products are presented in the market, may that be shelf space, nutrient information, label, packaging, etc.

With this context in mind, the main managerial relevance of this study would pertain to the increased evaluation of options that have zero-value undesirable attributes. If consumers evaluate these products as healthier, it can be used as a nudge towards the consumption of healthier products. Companies can put effort into making this information visible to the consumers, by altering how nutrition labels are presented, or the title of products.

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Theoretical background

Context Effects

Hsee and Leclerc (1998) state that one of the most influential factors that affect decision- making is context, specifically making a distinction whether the focal product is compared in isolation or in the presence of another alternative. Simon and Tversky (1992) describe the two main components of context effects which are trade-off contrast and extremeness aversion. The extreme eversion principle states that when everything else is equal, the option with the most extreme values is less attractive than the other option with moderate values. The trade-off contrast, on the other hand, argues that consumers build preferences for an alternative as a function of the other alternative in the choice set.

Both these components explain the idea that an alternative in a choice set is evaluated based on its relative performance to the other options in the choice set, thus to a reference point.

The study of Chernev (2005) expands this research beyond the traditional context effects, to include the role of attribute balance as a moderator for both trade-off contrast and extremeness aversion. When in a choice set with balanced alternatives, the reference point is built from the relationship between the attribute’s values (e.g. 45,45), which makes this option as the one with the lowest dispersion. In this case, the balanced alternative is viewed as the least extreme, even when is it not in the middle of the choice set.

Context effects have been proven to hold in situations where consumers make decisions in separate as well as joint evaluations. Circling back, the work of Hsee (1996) proved that when a product is already (un)attractive in a separate evaluation, then putting this product in a joint evaluation will hurt(enhance) the attractiveness. For example, he explains that when considered separately, a dictionary with 10,000 entries without remarks, is evaluated higher than one with 20,000 entries with a torn cover. However, in a joint evaluation people tend to go for the 20,000 slightly damaged one.

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Considering the scenario where one is comparing a product with zero-value attributes against a product with any number of value, it can be argued that it is an analogy to a separate evaluation (Hsee, 1996). Comparing any number to zero removes the reference point, which brings in the need to compare each attribute as an absolute number (Palmeira, 2011). Thus, for this research zero-value attributes will be considered as part of the context effects, influencing decision-making in a separate evaluation scenario. It will be of interest to see if the separate evaluation effect will hold, even though the focal product will be shown and compared in a set size of several choices.

Zero as a special number

Shampanier et al., (2007) studied how people overreact to the price of zero. They did this by proving that consumers will buy much more of a product just because it is free, as compared to the quantity they would get when this product has a very low price. The study demonstrated in a series of experiments, that zero meant not only low cost of purchasing but also higher intrinsic values and benefits associated with it, even when they had to let go of an option that they “should” find superior. Moreover, they validated the findings in real-life choices and rejected the other potential explanations such as ratio and transaction costs. When exploring the psychology behind the zero-price effect, the authors considered 3 potential explanations labeled “social norms” “mapping difficulty” and “affect”. Out of which, affect proved to be the most likely account. Affect is defined as the psychological mechanism that explains how the free option evokes a positive response, which in turn is used as a cue in decision making (Finucane et al., 2000, Slovic et al., 2002, Gourville and Soman 2005).

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Zero Comparison effect

Circling back to the context of decision-making in the food domain, the study of Palmeira (2011) argued that the zero-price effect is in fact, a special occasion and it does not transfer to other domains and to other product attributes. An important implication of this study was that consumers will not experience the same strong affective reactions towards other zero-value attributes, in the same way, they did to price, in fact, they follow a trade-off cognitive approach to make decisions. It is proven however that having products with zero-value attributes will alter the way comparison is made on the choice set.

At first sight, chips with 5 grams of fat are unattractive when compared to those with 1 gram, but clearly better than those with 20 grams. In this scenario, 1 gram of fat vs 5 grams in a pack of chips, states a clear advantage for the pack with 1 gram. However, comparing 5 grams to 0 grams, the advantage may be harder to understand and interpret, diminishing thus its superiority (Palmeira, 2011). If one was to apply the same findings as to the ones from Shampanier et. al (2007) the assumption would be that the product with a zero-value attribute, would be overestimated, the same way as the one with zero price was. Thus, the zero-value attribute product’s choice share on the set would increase.

Palmeira (2011) argued that a zero attribute removes a reference point that consumers use to evaluate the size of attribute differences. That’s what they called the ‘Zero Comparison” effect.

Compared to zero, any number is infinitely larger, thus consumers have to make decisions based on absolute attributes.

One of the products used in that study was yogurt. Palemeira (2011) refers to attributes where lower numbers are better (and zero is the best you can get) as favorable zero attributes such as grams of fat and sodium on a yogurt. On the other hand, attributes, where higher values are better, are referred to as unfavorable zero attributes (where 0 is the worst you can get), for

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example, the number of free pictures you get when you buy a new digital camera. Favorable zero-attributes followed a different pattern from the unfavorable ones. In the yogurt example, the choice share of yogurt A shifted from 30% to 66% as its fat content increases from 0 to 1 gram, but then it drops to 53% at 5 grams of fat and finally to 35% at 8 grams. Consumers made selections in a binary choice set, where option A was manipulated & option B had a constant fat attribute value of 10 grams. The explanation behind the zero consequence is that it is easy to see the difference when comparing a product of 1gram vs one with 5 grams of fat.

However, compared to 0 grams of fat, the difference is still clear, but harder to interpret its advantages (Palmeira, 2011). Thus one of the main conclusions of this study was that an option can improve its choice share by becoming objectively inferior; increasing the level of an undesirable attribute such as fat from zero or decreasing the level of a desirable attribute (such as free pictures) to zero.

These findings were replicated by the study of Graham et al., (2014), which makes an important contribution to the literature on zero comparison effect in the food domain. Across several experiments, consumers were faced with making decisions based on FOP nutrition labels, in which each product is rated with a star (0-3) where 3 is the healthiest. The study showed that the zero-comparison effect made products with 0 stars to be perceived as healthier, than those with 1 star, and chosen more. Thus, when an option became objectively inferior, its choice share increased. Different from Palmeira (2011) study, in this case, consumers were pre- disposed to choosing products based on healthiness. Following this link, we can argue that in consumer’s minds a connotation of “Zero= Healthy” was formed and this effect was more pronounced for consumers with higher health concerns (Graham et al. 2014).

In this sense, I argue that the same link can be formed in a consumer’s mind when considering products with zero-value attributes against those with non-zero value attributes. This is the

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effect that this study will try to test in a multicomponent food domain, regarding attributes that affect the perceived healthiness of the product.

On the contrary to Palmeira (2011) and in accordance with the zero-price effect (Shampanier et al., 2007), I expect that reducing undesirable attributes to zero will generate the same type of emotional reaction and affective reaction to consumers.

In the following sections, the logic behind my assumption will be discussed, as well as the context in which the study will take place will be elaborated.

Multicomponent products

First, the research will be done with a multi-component product. Nicolau and Sellers (2012) and Baumbach (2016) were the first ones to present proof of zero price effect in the tourism industry, where products are multi-component in nature. A multi-component product is one where its components are interrelated, can take place separately, but are consumed together.

Their findings showed evidence in favor of what they called the “free breakfast” effect. The demand for the better preferred alternative decreases when the cheaper option offers free breakfast. The results show that there was an increased positive affect translating into the zero- price effect which holds for separate dimensions that are part of a complete product.

We can argue that the food domain is also made of products multi-component in nature, and the interrelations between the ingredients make the total product favorable or not. In the case of food products, components that are taken into consideration when making a purchase decision are fat, calories, amount of lead, protein, etc, varying by each person’s goals (Graham et al., (2014). For example, ingredients that are important when buying a pack of chips are;

saturated fat, calories, and carbs (Plasek et al., 2020). This assumption is in accordance with the literature on the multi-ingredient effect being used as a heuristic in decision-making (Jeong et al., 2020).

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Thus, this study will check whether having two unattractive attributes reduced to zero, while holding other relevant components to a certain standard, will replicate the desired effect, same as zero price. This will make consumers not have to “give up” on any other attribute that they find important, thus the trade-offs will be solely on the unfavorable attributes reduced to zero.

Following “the more the better” intuition, (Petty and Cacioppo, 1984) I expect this logic to hold for zero-value attributes, leading to the following hypothesis.

H1: Compared to non-zero values, reducing the value of undesirable attributes (such as fat and sodium) to zero, while holding desirable attributes constant, will lead to a higher choice share.

Consumer choice in the food domain

An extensive body of literature review has been focused on consumer decision making specifically in the food domain. Consumers are biased by several contextual and cognitive cues when making food-related decisions. Perceived healthiness of food has been one of the key factors of consumer food consumption (Jeong et al., 2020). Plasek et al., (2020) review of factors that influence the perceived healthiness of food, yielded 6 categories that have a direct effect on the consumer perception in this context; the communicated information—such as FoP labels and health claims, the product category, the shape and color of the product packaging, the ingredients of the product, the organic origin of the product, and the taste and other sensory features of the product. All the food labels in the present market, found on the packaging must include certain nutrition information to comply with a standard level of governmental policies.

Even though having the nutritional content present has been found to have a positive effect on the quality of food choices(Kim Sy,2000; Rebollar ,2016; Variyam, 2008), often consumers’

heuristics intervenes with their systematic processing of the nutrient information, leading them to make irrational choices. Jeong et al., (2020) study makes an important contribution to the

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relationship between the presence of certain ingredients and healthiness perception, stating that also the number of ingredients can affect this perception. They call this effect the multiple ingredient effect.

Previous studies support the idea that multiple components serve as a heuristic cue, in several domains of consumer decision-making. Petty and Cacioppo (1984) introduce the “the more the better” intuition that is also found in Berger, Draganska, and Simonson (2007) study that uses diversity to assess a brand’s perceived quality. Their research proves that increasing the number of brand offerings leads to an increase in the perceived quality of a brand. Following this vein of logic, in the case of food healthiness, one would perceive a higher food (un)healthiness when the number of (un)healthy ingredients is greater (Jeong et al., 2020). The study proved that consumers find a pizza with 3 toppings (each of 5gr of saturated fat) as unhealthier, than a pizza with 1 topping of 15gr saturated fat. These results were demonstrated in the virtue food domain using granola bar as a product and healthy ingredients as focal attributes (Jeong et al., 2020). Their work lays important groundwork for further research on how context effects could enhance or decay the multiple ingredient effect, especially if one or more of the components is altered and reduced to zero.

Thus, drawing from Jeong et al., (2020) setting, granola bars will be used in this research as well. I will aim to use the multi-ingredient effect (in this case by altering two unfavorable attributes) to affect the perceived healthiness and influence consumer decision-making.

Perceived Healthiness

Graham, D. J., and Mohr, G. S. (2014) found that perceived healthiness was positively related to purchase intention. Thus, finding out if zero effect can be used to increase the perceived healthiness of products, can be of interest, to influence demand for such items, and nudge consumers towards healthier choices. Thus, perceived healthiness will be used as a mediator in

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this study. Plasek et al. (2020) review that gathered results from 59 articles, shows that among other factors, product category and the following ingredients: omega-3, saturated fat, sodium, vitamins and minerals, salt, and additives are crucial in influencing perceived healthiness.

Thus, the study will incorporate saturated fat, and sodium as undesirable attributes to be manipulated and reduced to zero. These will be seen in the product category of snacks. The following hypotheses are presented.

H2: Perceived Healthiness, mediates the effect of product attribute value (zero vs non-zero) on

product choice share.

Specifically, H2 will be checked in two parts ;

H2a: Compared to non-zero values, reducing the value of undesirable attributes (fat and sodium) to zero, will lead to an increase in the target’s option perceived healthiness.

H2b: Higher perceived healthiness will lead to a higher target option’s choice share for consumers with a high health concern.

Choice set size

Choice set size has been proven to have a significant impact on consumers’ psychology and hence their decision-making (Levav et al. 2012). A noticeable contribution to this realm has been the research of Iyengar and Lepper (2000) which showed that larger choice sets demotivate the purchase while Chernev (2003) states that the choice set size can alter decision confidence and the preference strength. In real-life settings, consumers are exposed to several choices when engaging in a purchase decision, therefore it is reasonable to account for this factor, in the realm of this research as well. It is of interest to examine how will set size interact with the Zero Comparison effect and how this interaction will influence the consumer choice of product.

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Wen Mao (2020) demonstrates that the “Zero Comparison” can be reduced, by choice set size while holding attribute dimensions constant. Furthermore, the study showed that this pattern was valid no matter the actual size of the set as long as it was larger than 2. It also tested whether the compromise effect and the decoy effect would be a boundary condition. The conclusion was significant regardless of whether the options were all similarly attractive, or when the middle one was superior (inferior) to the existing options.

On the contrary to the study of Palmeira (2011) which uses binary sets consisting of a focal option and a competing option, this study will use set sizes of 4 products. I expect that H3 to hold, in such bigger set sizes because, when faced with products with zero-value attributes in larger choice sets, consumers will make meaningful trade-offs, and the “Zero-Comparison”

effect can reverse (Mao, 2020).

H3: Compared to binary choice sets, larger choice set sizes will reduce the zero-comparison effect, leading to a higher perceived healthiness and hence a higher choice share of the product with zero-value attributes.

Conceptual model

Figure 1: Conceptual model

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Data and Methods

This research was conducted to explain whether and to what extent, the value that ingredients of a product can have, (in this case zero-value) affects the purchase intention of this product.

Purchase intention throughout the study is measured in terms of consumer choice, thus product choice share. If a causal relationship can be proven, practical implications can be drawn, with suggestions on how one entity can affect another.

“The best tool researchers have for determining causal relationships is experimental research.”

(Vargas et. al, 2017). Therefore, an experimental design was chosen as the most appropriate method to test the hypotheses of this study (Perugini et. al, 2018).

Study

This study aimed to show the effect that having zero-value attributes (saturated fat and sodium), has on consumers’ purchase choices. I expect to find a higher (lower) product choice share when these attributes are zero (non-zero). Secondly, the study aims to test for the mediation effect of perceived healthiness on purchase choice. Based on the theory that was discussed previously, it is expected that zero-value (non-zero) attributes will lead to a higher (lower) perceived healthiness, which itself leads to a higher (lower) choice share of the focal product.

Furthermore, the moderating effect of Choice set size on the level of perceived healthiness will be tested, such that; products with zero-value attributes in binary(quartet) choice sets will have a lower (higher) perceived healthiness. It is of interest to test this effect, as based on previously explained links, any factor that can affect perceived healthiness, affects purchase choice.

Sample

An a priori power analysis was conducted using Gpower for a sample estimation. A factorial analysis of variance showed that a minimum of 128 participants would provide 80% power

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(a= .05), to detect a medium effect (Cohen’s f= .25) in the dependent measure of interest Kang H. (2021).

A sample of 225 participants was recruited (Mage = 25.57, SD age = 6.815), via nonprobability, and convenience sampling. The data was gathered through social media platforms and communities. Out of these participants, 39.3% were male, 45.9% were female and .9% a third gender. As the study is interested in consumer purchase behavior, gender and age do not provide any constraints on the expected results, therefore all valid responses were chosen regardless of gender and age. The survey used an attention check, to ensure the quality of responses. Responses with an invalid answer to the attention check and those with missing items were excluded from the study. A total of 54 participants were excluded, reducing the final sample used for analysis to N= 171.

Procedure

This study used a 2 (product attributes value: zero-value vs one) x 2 (choice set size: binary vs quartet) between-subjects experimental design. Participants were randomly assigned to one of the 4 conditions of this study. In the Binary zero-value condition, participants saw a picture of two granola bars, each bar accompanied by a table of its ingredients below (Appendix A.1) For the focal product, the unfavorable attributes of Saturated fat and Sodium were reduced to 0 while favorable attributes of protein and fibber had a standard value. The comparing option had standard values for the unfavorable attributes and higher values for the favorable ones.

On the Binary one-value condition, the attributes of Saturated fat and Sodium were increased to 1, and the comparing option’s values were adjusted accordingly to keep the trade-offs between products, the same, within two conditions (zero-value vs one-value) (Appendix A.1).

To manipulate the choice set size participants saw either two or four options.

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The same manipulation of the attributes; Saturated fat and Sodium was replicated in the quartet conditions, where the focal granola bar (Zed) was compared against 3 other granola bars (Appendix A.2).

To measure consumers’ purchase choice, among all conditions, after seeing the images, participants were to indicate whether they would buy the products they just saw. After they made the choice, they were asked 2 questions about the Perceived healthiness and Perceived taste of these products. An attention check question was incorporated as well in the flow. For an overview of the questions asked refer to Appendix B. The order of these questions and the multiple-choice options within the answers were randomized.

The Perceived healthiness was measured on a 1 (Very Unhealthy – 5(Very Healthy) Likert scale, as adapted from previous studies by Lillis (2010), and Raghunathan (2006).

The Perceived taste was measured using a 1 (Terrible) to – 5(Excellent) Likert scale regarding the question: “On a scale from 1 (terrible) to 5 (excellent), please indicate your opinion about the taste of the products that you saw in this study”. Again, this scale was adapted from previous research by Raghunathan (2006) where participants were asked “ How tasty do you think these crackers would be? “

Perceived Taste will be used as a control variable. Raghunathan (2006) study demonstrates when consumers are faced with information concerning the evaluation of the healthiness of food items, a link is formed in their heads; the less healthy an item is, the better its taste would be. Therefore, this variable will be analyzed as a covariate to check its influence on the Perceived healthiness of the focal product.

The last block of the questionnaire presented to everyone was composed of 2 demographic questions regarding age, gender, and the respondent’s health concern.

Consumer health concern is defined as the behavior orientation that makes consumers avoid unhealthy foods (Graham, D. J., and Mohr, G. S., 2014). The study of Moorman and Matulich

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(1993), demonstrated that individuals who rate high on health concern (vs low), tend to focus on the nutrition attributes of the products and this affects the perceived healthiness of the product. Thus, this factor will be taken as a second control variable in the analysis.

Participants’ health concern was measured using the Likert Scale of 1(Does not Describe me at all) - 5 (Describes me extremely well) regarding the question: “Being healthy and choosing healthy food products is important to me in my daily life.” adapted from previous research of Tudoran, Olsen, and Dopico (2009).

Upon competition, a message thanking everyone for their participation was shown.

Descriptive Statistics

To test the hypotheses, new variables were created; Perceived Healthiness and Perceived Taste were merged across all products and conditions to get an average rating, noted under PH_mean and PT_mean. A dummy variable was created and noted under Purchase_decision to measure consumer choice of focal product. Next Attribute_value_condition and Choice_set_size condition were created as binary variables. The descriptive statistics of all variables used in the analysis can be found in table 1.

The compared means of ratings (Healthiness and Taste) per product, across all conditions can be seen in figure 2A. Next, the frequency of consumer choice was compared per product, across

Table 1: Mean, SD, Correlations

Variables Mean SD 1 2 3 4 5

1. PH _mean 2.4 0.9

2. PT _mean 2.4 1.0 .842**

3. Health concern 3.8 0.9 0.013 0.057

4. Purchase_decision - - .300** .278** -0.03

5. Attribute_value_condition - - 0.033 0.061 0.001 .199**

6. Choice_set_size - - .858** .865** -0.011 .395** 0.046

** Correlation is significant at the p< 0.01(2-tailed). N=171

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all conditions, in terms of product choice share. The comparison of focal product Zed and the rest can be seen in figure 2B.

Figure 2A: Mean ratings per product aggregated across conditions.

Figure 2B: Choice share per product

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Manipulation check

The study by Raghunathan (2006), describes how consumers associate good taste with unhealthy products and vice versa.

A one-way ANOVA was used to test whether there are any statistically significant differences between the means of Perceived taste rating, among the Attribute value conditions. Results show that the difference of ratings between the focal product in the zero-value condition (M=3.02, SD= .91) and in one-value condition (M=3.27, SD= .81), F(1,170)= 3,41, p=0.07, was not significant. This was expected, as the Perceived taste was not indented to depend on attribute value, the stimuli aimed to have equal levels of perceived taste rating, across conditions. Compared means can be seen in figure 3.

Figure 3: Perceived Taste mean score of the focal product

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Results

Correlation

To check for all possible correlations between the variables used in this study as well as the control variables (health concern and taste), a correlation matrix was used. Results can be found in table 1.

Choice set size had a significant relationship with Perceived healthiness rating, Perceived taste rating, and Purchase decision. Next, Purchase Decision had a significant relationship with Perceived healthiness rating, Perceived taste rating, and Attribute value condition, However, Health concern had no significant relationship with any of the variables.

Zero Value Attributes

To test hypothesis 1, a binary logistic regression was run, to determine the effect of the product Attribute value (zero vs one) on Purchase decision (Buy Zed or Else).

Regressing buying of product Zed onto product’s Attribute value (zero vs one) revealed a non- significant relationship B = -.073, SE = .315, Wald = .054, p = .817. There is a small difference in the choice share of product Zed among conditions (see figure 4). However, we can not conclude that having a zero-value attribute would lead to a higher choice share of the focal product Zed, due to the insignificance of the results. Implications will be discussed in the following chapter.

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Figure 4: Purchase of focal product in two conditions

Choice set size

Next, a moderated regression was performed by adding Choice set condition as a moderator in the model. The purpose was to check if there are any significant differences in the Purchase decision (Buy Zed or Else) of the focal product, among two choice set conditions (binary vs quartet). The main effect of Choice set size proved to be not significant (B = -.388, SE = .464, p = .403). The analysis revealed yet again, a not-significant relationship on the interaction effect (B= -.433, SE= .645, p = .502 , 95% CI[-1.698, .832] between Choice set condition and Attribute value condition.

This suggests that the moderation effect is not significant. The choice share of product Zed decreased going from binary (45.16%) to quartet (30.77%) condition (figure 5). The results go against the expectations, and the pattern that evolved is contrary to what was assumed.

Nevertheless, this difference cannot be accounted for, due to the results being insignificant.

Possible explanations of this pattern will be explored in the following chapter.

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Figure 5: Purchase of focal product in the choice set conditions

Perceived Taste and Health Concern

Next, the binary logistic regression was repeated, to model the relationship between the purchase of focal product (Zed) and Attribute value, but this time, the control variables of Perceived taste and Health concern, were also included in the analysis.

The results showed that for Attribute value (B = .140, Wald = .191, p = .662) , Perceived taste (B = .274, Wald = 2.101, p = .147) and Health concern (B = .116, Wald = .417, p = .518) the effect was not insignificant.

Comparing these results to the prior regression, we can see that the accounting for the control variables did not bring in any new results. Therefore, for future analysis, Health concern and Perceived taste will no longer be included.

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A sample paired t-test was run, to compare the Perceived healthiness rating of the focal product (Zed) with the other products. Prior to conducting the analysis, the assumption of normally distributed scores was assessed. The assumption was satisfied as the skew and kurtosis levels were estimated all below the maximum allowable values (i.e., skew < |2.0| and kurtosis < |9.0|;

Posten, 1984). Results show that focal product Zed (M=3.60, SD= .97) rating is higher than Lis (M=3.14, SD= .94). The difference of .46 was statistically significant (t170 = 4.4, p <.001).

The same pattern was found comparing the scores of Zed with Mel (M=3.22, SD= .90) and Das (M=3.10, SD= .95). The respective differences of .42 and .54 were statistically significant for Mel ((t77 = 3, p=.004) and Das ((t77 = 3.3, p=.002). These results show that the focal product did receive a higher rating and is considered healthier, compared to other products, aggregated across conditions (refer to picture 2A).

Mediator analysis

To test hypothesis 2, Process SPSS macro 4(Hayes, 2013, 2017) was used. The independent variable was Attribute value condition (coded as 0= Zero Condition, 1= One Condition), the mediator was Perceived healthiness of Zed, measured on a 5-point scale, and the dependent variable was Purchase Decision (coded as 0= Else, 1= Purchase of Zed). As Health concern did not have a significant effect on purchase decision, it was not included as a covariate in the following analysis. The perceived taste was also excluded from the results of the manipulation check and the second regression run previously. The model is visualized in figure 6.

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Figure 6: Process macro, model 4.

The effect of product Attribute value on Perceived healthiness of the product was not significant (ai): b= -.086, t = -.58 p = .56 , 95% CI[-.379, .207]. This suggests that zero-value attributes on a product, do not influence the perceived healthiness of this product. The effect (bi) of perceived healthiness on purchase decision is positive and significant b= .667, s.e = .188 , p< 0.05, 95% CI[.416, 1.587]. This indicates that the healthier consumers perceive a product, the more they chose it. A one-unit increase in perceived healthiness leads to a .67 unit increase in choice share.

The indirect effect means that two products that differ based on attribute value, are estimated to differ by -.058 units in choice share, via perceived healthiness. However, the indirect effect of -.058, tested using bootstrapping, proved to be insignificant as 0 falls within the CI[-.665, .623].

The direct effect cⁱ = -.021, is the estimated difference in the purchase decision for two products, which have the same level of perceived healthiness, but differ only on the unfavorable attributes (zero vs one) they contain. The direct effect was not statistically significant, z = - .065, p = .948. This result is reconfirming what we found in the first regression analysis, testing hypothesis 1. The result suggests that the perceived healthiness a product receives is more important than the value of attributes when it comes to the purchase decision of this product.

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Hypothesis 2 is, therefore, only partly accepted. We cannot conclude that Perceived healthiness mediates the relationship between product Attributes value (zero vs non-zero) and the Purchase decision of this product.

Figure 7: Simple mediation; perceived healthiness

Moderator analysis

A moderated mediation analysis was conducted using Process SPSS macro 7 Hayes, 2013, 2017), to test hypothesis 3. The independent variable used was the Attribute value condition (coded as 0= Zero Condition, 1= One Condition), the mediator was Perceived healthiness of Zed, measured on a 5-point scale, and the dependent variable was Purchase decision (coded as 0= Else 1= Purchase of Zed), and as a moderator Choice set size was used (coded as 0= binary, 1= quartet). 5000 bootstrap samples were used to interpret the effect as significant if the confidence intervals (CI) did not include 0. The model used can be seen in figure 8.

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Figure 8; Process macro, model 7

Choice set size

The interaction term reveals how much the effect of Attribute value on Perceived healthiness differs depending on the Choice set size (binary vs quartet). The analysis disclosed that there was no significant interaction between XW in the model of Y (c’4 = .017, p = .955, 95% CI[- .575,.609]). Moreover, bootstrap was used to interpret the effects as statistically significant when the confidence intervals (CI) did not include 0. This is not the case as the results showed the CI[-.626, .744]. Therefore, hypothesis 3 is rejected, and we cannot conclude that when a product is compared in a bigger than 2 choice set size, will lead to a higher perceived healthiness of the focal product. This was not expected, and the reasons behind these results will be discussed in the following chapter.

General Discussion

Zero-value attributes

First, we checked whether reducing the value of undesirable attributes (such as fat and sodium) to zero, while holding desirable attributes constant, would lead to a higher purchase intention for this product. The focal product’s choice share with zero-value attributes was compared against the choice share of the product when the undesirable attributes were increased to one.

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The results were in fact, as expected aligning with the assumptions made in this research earlier;

when attributes were increased to one, the product got bought less. Choice share of focal product Zed decreased from 39.51% to 37.78% (figure 4), moving from zero to one condition.

However, the analysis showed that the results were insignificant, therefore, hypothesis 1 is rejected.

In any case, the focal product was always compared to other option(s) which were technically objectively worse, if one was to make the trade-off, based solely on the unfavorable attributes (Appendix A).

These results go against the Zero-Comparison effect that Palmeira (2011) explains in his study, which says that a product can increase its choice share by becoming objectively inferior, increasing the level of an undesirable attribute such as fat from zero.

The main effect of attribute value on consumer choice proved insignificant, thus we will look at some alternative explanations why the focal product, did get chosen more in the zero-value condition.

A possible explanation could be based on consumer decision-making psychology research.

When the information available regarding choices increases, consumers tend to process less information and opt for a shortcut in decision making (Hauser & Wernerfelt, 1990). This trace of thought is also supported by the information overload theory which states that with the increase of available information, individuals and organizations become overwhelmed as their capacity and time to process it, is not sufficient (Schick, Gordon, & Haka, 1990).

In the one-value condition, the amount of information provided was more (every attribute had a value). This could have accounted for difficulties in making meaningful comparisons between products, thus the value of the focal product was not distinct, leading to it not being chosen.

Participants were provided with information regarding several nutritious ingredients, which may not necessarily have the same importance to the consumer’s mind as according to the

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review of 59 articles from Plasek et al. (2020). This could have caused a chain effect, where people were paying less attention to the values and more to the name of the ingredient.

In the second analysis, Perceived healthiness was tested, to check if it mediates the relationship between having zero-value attributes and the purchase decision for this product.

The results unfolded in two ways.

Firstly, the effect of Attribute value on Perceived healthiness was not significant, therefore hypothesis 2a was rejected. We cannot conclude that having zero-value attributes will make a product perceived as healthier. Participants did not follow the ‘’the more the better’’ intuition, previously introduced by Petty and Cacioppo (1984) and later on in Berger, Draganska, and Simonson (2007). According to this line of research, the multiple components serve as a heuristic cue, affecting the consumer’s perception and decision-making. Jeong et al., (2020) state that the number of ingredients affects health perceptions. Two un-favorable attributes were reduced to zero in this research to create this line of logic in consumers’ minds, however, this was not the case.

Two possible explanations can account for these results.

First, as explained in the previous research (Tverskyand Kahneman 1991; Wong and Kwong 2005), the number zero removes a reference point, and consumers cannot compare between options in relative terms. Even though 0 grams of fat and sodium is clearly better than 5 and 72, they have trouble understanding how much better this is. The only meaningful trade-off they can do is based on the other favorable attributes (protein and fiber), which the comparing products always scored higher than the focal product. Thus, the link of which product is healthier created in the participants' minds was based on favorable attributes instead.

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Second, even though the choice of unfavorable attributes to be used in this study was based on previous research by Plasek et al., (2020), participants could have had trouble understanding the meaning of these definitions such as saturated fat and sodium and their effect on healthiness.

However, an interesting note, in this case, is the result from the paired t-test. The focal product Zed did receive a higher rating overall compared to the other options. Since we already removed the explanation that zero value attributes caused this, an educated guess can be that low values (0,1) lead to a higher perceived healthiness. All the other products had higher values than 1, on the two manipulated attributes (Appendix 1). Further research can explore the validity and boundary conditions of this assumption.

The second part of the analysis, however, revealed that a higher Perceived healthiness does lead to a higher choice share, so hypothesis 2b was supported. Important to note that consumer's Health concern did not have an impact on this result, as assumed. Specifically, a difference by one unit in perceived healthiness will lead to a .677 unit increase in choice share for this product. The correlation between these two variables was significant. These results fall in line with the results from Graham, D. J., and Mohr, G. S. (2014) who state that perceived healthiness is positively related to purchase intention, which in our case is measured through consumer choice.

Choice set size

The effect of Attribute value on Perceived healthiness was not moderated by Choice set size, as the analysis discovered that there was no significant interaction between XW in the model of Y (c’4 = .017, p = .955). Therefore, hypothesis 3 was rejected. This was not as expected. It was predicted that bigger choice sets than 2, specifically 4 in this case, would lead to a higher perceived healthiness evaluation. These assumptions were based on the previous study of Levav et al. (2012) who demonstrated the significant impact that set size has on decision

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making. Specifically, this study aimed to replicate the findings of Wen Mao(2020) who demonstrated that as long as the set size was bigger than two, the Zero-Comparison effect would be reduced. The goal was to reduce the zero-comparison effect, as that went against our assumptions made in this research.

Based on the Iyengar and Lepper (2000) study, people are more likely to purchase gourmet or chocolates when they are offered a limited array of 6 choices. The set size used in our research falls under this scope, therefore the Choice set size cannot be the explanation for why the purchase frequency of the focal product was lower and did not follow the intended path.

A possible explanation could be the fact that, as the number of options the participants were facing increases, consumers’ ideal points shift in a way that makes it harder for them to attain (Chernev, 2003b; Schwartz et al., 2002). Therefore, consumers in this study could have reached the point where none of the products were fulfilling their expectations, making them engage in rushed decision-making, not fully evaluating the choice sets.

Conclusions

This study enriches the current understanding of zero as a special number in the food domain.

Shampanier et al. laid the basis for other researchers to explore the zero effect on other domains than probability, reward, and price. Palmeira (2011) on the other hand, was the first one to explore this effect on the food domain.

This research takes a different approach while keeping in mind previous work done, trying to shed more light on how zero effect can be used to nudge consumer decision-making, towards healthier choices.

This study found that perceived healthiness is indeed related to consumer choice and consumers are driven by this factor when making their purchases. Due to the current situation where spending on health has grown (WHO, 2020) it is of interest to see which aspect, one can

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influence to stabilize this spending spree. Companies can translate this into a marketing effort to highlight the health benefits a product has and wait for it to translate into purchase choice.

Second, it was found out that zero as a special number could influence choice when it portrays itself on the ingredients of a food product. Consumers did choose the product with zero attribute value more often, however, we cannot conclude if this choice happened solely because of the zero-value of other possible causes.

Practical Implications

The findings of this study can have important implications for food companies and marketing managers. The results revealed that products with zero values will draw attention and affect choice. However, given the fact that a zero-value removes a reference point, consumers will have difficulties truly grasping the nutrition information they are reading. Kahneman and Tversky (1979) explain the principle of diminishing sensitivity, according to which; the perceived difference between two quantities decreases as both quantities increase by the same amount. For example, the difference between 25 and 30 is seen as less than 5 and 10.

Consumers tend to focus more on relative differences, as they are more sensitive to that. From a practical point of view, it could help decision-making if, on the label next to values reduced to 0, there would be a comparison against a suggested intake/ benchmark, so that the benefits could be highlighted better.

Next, the findings on perceived taste revealed that the ingredients do not necessarily affect consumers’ perception of taste. The taste rating of the products was almost the same between products with zero-value attributes and those with non-zero. Previous studies had proven that a link of taste intuition exists, where unhealthy= tasty and vice versa. A possible implication for label formats can be the fact that highlighting a certain attribute in the frame of healthiness, will not affect the taste. However, future research is needed to clearly make this link and

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explore more on the way attributes are shown, as, in this study, they were not highlighted, but rather blended among others.

Limitations and further research

Several limitations need to be considered regarding this study. Firstly, the sampling technique of convenience sampling led to the sample being largely composed of students with high education levels, and relatively young (M= 25.57). This group of participants could be biased in choosing products that are healthier as this is an age group tending to be more self-conscious about their health and purchase decisions (Chang et. al 2020; Horvath et. al 2005). Convenience sampling is also said to have an impact on the external validity of the experiment, by lessening it. Further research could aim to draw a more inclusive sample.

A second limitation lies in the type of attributes chosen to be included in this test. These attributes were based on previous research by Plasek et al. (2020) which demonstrated that the chosen ones, were among the crucial factors influencing perceived healthiness. However, the settings between these two studies were not identical therefore we cannot fully be sure that the attributes chosen were the right ones for our sample. A possible explanation for our insignificant results could be the fact that the participants were not well informed on what each attribute means, nor the benefits/ harms associated with it. Further research could be repeated with a pilot study, to correctly select attributes that will influence consumer’s decision-making.

Furthermore, the category chosen for this test was snacks, specifically granola bars. To gain a more inclusive overview of the zero effect, several products which vary in the frequency of consumption and category (dairy, snacks, gourmets, etc) could be tested.

Fernan et al. (2018) discuss the Health Halo effects of product titles on perceived healthiness.

Their study focused on protein bar food items, and the results showed that having nutrition content on the title (e.g., protein) increased the perceived healthiness of the product. Further

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research could combine the findings of this study with Fernan et al.(2018) to test whether including zero-value attributes on the title of the product (Zero % fat chips, Zero% yogurt, and 0% sugar) can lead to an increased perceived healthiness and/or purchase intention.

Additionally, the current research measured participants' purchase intention categorically.

There are two things to note in this case. The measure validity could have been increased by measuring the purchase intention on a scale or purchase probability scales (Wright, M, 2007).

Next, academic literature has noted discrepancies between intentions to do and what one actually does (intention behavior gaps) (Ajzen, 1991). A field experiment could clear this gap, test for actual consumer behavior, and increase the external validity of these results.

Finally, this research does not take into consideration the price of the product, but in real-life settings, healthier products tend to be more expensive. Thus, it could be of interest to further explore whether presenting consumers information about zero-value attributes and the price per product could reveal different results.

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Appendices

Appendix A: Questionary Conditions A.1 Binary Conditions

Zero Condition

One Condition

A.2 Quartet Conditions Zero Condition

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One Condition

Appendix B

:

Questions regarding Perceived Healthiness, Perceived Taste, Attention check

Binary Condition

Quartet Condition

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Attention check

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References

Baumbach, E. (2016). The zero-price effect in a multicomponent product context. International Journal Of Research In Marketing, 33(3), 689-694.

https://doi.org/10.1016/j.ijresmar.2016.01.009

Berger, J., Draganska, M., & Simonson, I. (2007). The Influence of Product Variety on Brand Perception and Choice. Marketing Science, 26(4), 460-472.

https://doi.org/10.1287/mksc.1060.0253

Chang, H., Ma, C., & Chen, H. (2020). The Impacts of Young Consumers’ Health Values on Functional Beverages Purchase Intentions. Retrieved 23 June 2022, from.

Chernev, A. (2003). When More Is Less and Less Is More: The Role of Ideal Point Availability and Assortment in Consumer Choice. Journal Of Consumer Research, 30(2), 170-183. https://doi.org/10.1086/376808

Fernan, C., Schuldt, J., & Niederdeppe, J. (2017). Health Halo Effects from Product Titles and Nutrient Content Claims in the Context of “Protein” Bars. Health

Communication, 33(12), 1425-1433. https://doi.org/10.1080/10410236.2017.1358240

Festinger, L., & Carlsmith, J. (1959). Cognitive consequences of forced compliance. The Journal Of Abnormal And Social Psychology, 58(2), 203-210.

https://doi.org/10.1037/h0041593

Finucane, M., Alhakami, A., Slovic, P., & Johnson, S. (2000). The affect heuristic in judgments of risks and benefits. Journal Of Behavioral Decision Making, 13(1), 1-17.

https://doi.org/10.1002/(sici)1099-0771(200001/03)13:1<1::aid-bdm333>3.0.co;2-s

(42)

Gourville, J., & Soman, D. (2005). Overchoice and Assortment Type: When and Why Variety Backfires. Marketing Science, 24(3), 382-395.

Graham, D., & Mohr, G. (2014). When zero is greater than one: Consumer misinterpretations of nutrition labels. Health Psychology, 33(12), 1579-1587.

https://doi.org/10.1037/hea0000080

Guthrie, J., Mancino, L., & Lin, C. (2015). Nudging Consumers toward Better Food Choices:

Policy Approaches to Changing Food Consumption Behaviors. Psychology &Amp;

Marketing, 32(5), 501-511. https://doi.org/10.1002/mar.20795

Heyman, J., & Ariely, D. (2004). Effort for Payment: A Tale of Two Markets. Psychological Science, 15(11), 787-793. https://doi.org/10.1111/j.0956-7976.2004.00757.x

Hsee, C., & Leclerc, F. (1998). Will Products Look More Attractive When Presented Separately Or Together?. Journal Of Consumer Research, 25(2), 175-186.

https://doi.org/10.1086/209534

Horvath, Agnes & Lajos, Attila & Szira, Zoltan & Varga, Erika, 2005. "The Manifestation Of Health-Consciousness In Food Preference And Consumption Among Young People According To An Empiric Study," GAZDÁLKODÁS: Scientific Journal on Agricultural Economics, Karoly Robert University College, vol. 49(Special E), pages 1-14.

Iyengar, S., & Lepper, M. (2000). When choice is demotivating: Can one desire too much of a good thing?. Journal Of Personality And Social Psychology, 79(6), 995-1006.

https://doi.org/10.1037/0022-3514.79.6.995

(43)

Jeong, Y., Hwang, S., & Kwon, M. (2020). <p>The Multiple Ingredients Effect: The More Diverse (Un)Healthy Ingredients, the More (Un)Healthy

the Food</p>. Psychology Research And Behavior Management, Volume 13, 1165- 1180. https://doi.org/10.2147/prbm.s271979

Kahneman, D., & Tversky, A. (1979). On the interpretation of intuitive probability: A reply to Jonathan Cohen. Cognition, 7(4), 409-411. https://doi.org/10.1016/0010-

0277(79)90024-6

Kang, H. (2021). Sample size determination and power analysis using the G*Power software. Journal Of Educational Evaluation For Health Professions, 18, 17.

https://doi.org/10.3352/jeehp.2021.18.17

Kim, S., Nayga, R., & Capps, O. (2001). Health Knowledge and Consumer Use of Nutritional Labels: The Issue Revisited. Agricultural And Resource Economics Review, 30(1), 10-19. https://doi.org/10.1017/s1068280500000502

Levav, J., Reinholtz, N., & Lin, C. (2012). The Effect of Ordering Decisions by Choice-Set Size on Consumer Search. Journal Of Consumer Research, 39(3), 585-599.

https://doi.org/10.1086/664498

Lillis, J., Luoma, J., Levin, M., & Hayes, S. (2010). Measuring Weight Self-stigma: The Weight Self-stigma Questionnaire. Obesity, 18(5), 971-976.

https://doi.org/10.1038/oby.2009.353

Moorman, C., & Matulich, E. (1993). A Model of Consumers' Preventive Health Behaviors:

The Role of Health Motivation and Health Ability. Journal Of Consumer Research, 20(2), 208. https://doi.org/10.1086/209344

(44)

Nicolau, J., & Sellers, R. (2011). The Free Breakfast Effect. Journal Of Travel Research, 51(3), 243-249. https://doi.org/10.1177/0047287511418370

Palmeira, M. (2011). The Zero-Comparison Effect. Journal Of Consumer Research, 38(1), 16-26. https://doi.org/10.1086/657998

Perugini, M., Gallucci, M., & Costantini, G. (2018). A Practical Primer To Power Analysis for Simple Experimental Designs. International Review Of Social Psychology, 31(1).

https://doi.org/10.5334/irsp.181

Petty, R., & Cacioppo, J. (1984). The effects of involvement on responses to argument quantity and quality: Central and peripheral routes to persuasion. Journal Of Personality And Social Psychology, 46(1), 69-81. https://doi.org/10.1037/0022-3514.46.1.69

Plasek, B., Lakner, Z., & Temesi, Á. (2020). Factors that Influence the Perceived Healthiness of Food—Review. Nutrients, 12(6), 1881. https://doi.org/10.3390/nu12061881

Raghunathan, R., Naylor, R., & Hoyer, W. (2006). The Unhealthy = Tasty Intuition and Its Effects on Taste Inferences, Enjoyment, and Choice of Food Products. Journal Of Marketing, 70(4), 170-184. https://doi.org/10.1509/jmkg.70.4.170

Ratchford, B. (1982). Cost-Benefit Models for Explaining Consumer Choice and Information Seeking Behavior. Management Science, 28(2), 197-212.

https://doi.org/10.1287/mnsc.28.2.197

Rebollar, R., Lidón, I., Gil, I., Martín, J., Fernández, M., & Riveres, C. (2016). The influence the serving suggestion displayed on soft cheese packaging has on consumer expectations and willingness to buy. Food Quality And Preference, 52, 188-194.

https://doi.org/10.1016/j.foodqual.2016.04.015

(45)

Simonson, I., & Tversky, A. (1992). Choice in Context: Tradeoff Contrast and Extremeness Aversion. Journal Of Marketing Research, 29(3), 281. https://doi.org/10.2307/3172740

Simonson, I., & Tversky, A. (1992). Choice in Context: Tradeoff Contrast and Extremeness Aversion. Journal Of Marketing Research, 29(3), 281-295.

https://doi.org/10.1177/002224379202900301

Shampanier, K., Mazar, N., & Ariely, D. (2007). Zero as a Special Price: The True Value of Free Products. Marketing Science, 26(6), 742-757.

Slovic, P., Finucane, M., Peters, E., & MacGregor, D. (2002). Rational actors or rational fools: implications of the affect heuristic for behavioral economics. The Journal Of Socio-Economics, 31(4), 329-342. https://doi.org/10.1016/s1053-5357(02)00174-9

Tudoran, A., Olsen, S., & Dopico, D. (2009). The effect of health benefit information on consumers health value, attitudes and intentions. Appetite, 52(3), 568-579.

https://doi.org/10.1016/j.appet.2009.01.009

Vargas, P., Duff, B., & Faber, R. (2017). A Practical Guide to Experimental Advertising Research. Journal Of Advertising, 46(1), 101-114.

https://doi.org/10.1080/00913367.2017.1281779

Variyam, J. (2008). Do nutrition labels improve dietary outcomes?. Health Economics, 17(6), 695-708. https://doi.org/10.1002/hec.1287

Wright, M., & MacRae, M. (2007). Bias and variability in purchase intention scales. Journal Of The Academy Of Marketing Science, 35(4), 617-624. https://doi.org/10.1007/s11747- 007-0049-x

The zero-effect in food attributes The impact of zero-value attributes in multicomponent food products, on consumer choice.

MSc. in Business Administration – Consumer Marketing specialization