Every Calorie Counts

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Every Calorie Counts

A retail study about the effects of real-time calorie feedback

Kristine Grude Nesvik

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Every Calorie Counts

A retail study about the effects of real-time calorie feedback

Master Thesis

Kristine Grude Nesvik

Address: Damsterkade 1, 9711 SE, Groningen Phone: +31-639-784-747

Student Number: S2507684

Email: Kristine.g.nesvik@gmail.com Supervisor: Dr. Koert van Ittersum

External supervisor: Sebastian Sadowski

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ABSTRACT

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PREFACE

As I am about to close another chapter in my life, this thesis represents my last work as a marketing student at the University of Groningen. Initially, after years of studying, this completion feels like a relief, but in the greater sense of things, I realized that I am deeply grateful for this opportunity to create, conduct and understand the means needed to transform a novel idea into a firm concept, also known as my thesis.

I chose the topic of consumer wellbeing due to my keen interest in how to build a sustainable society where we as consumers have the right to know what we put into our body, as well as being provided with the tools necessary to limit harmful consumption, and thus prevent serious and highly meaningless diseases. I wanted to approach this study in a future oriented manner, and after reading van Ittersum’s (2013) article that explores prospects of smart shopping carts, I was intrigued to follow in this path, and I am therefore building on the fairly unexplored but very timely topic of in-store customer tracking.

First, I would like to acknowledge and express my gratitude to my thesis supervisor Dr. Koert van Ittersum, who has been of enormous help throughout the process, with clever advices, creative solutions and a great amount of support, daytime or evening, weekday or weekend. Thank you for being as enthusiastic and inspiring as you are. During the process I have also worked in a student group, to support and be supported by fellow peers. Therefore I would like to give recognitions to my group members for comprehensive discussions and a sincerely needed pat on the back when needed. Also of great importance is my family and boyfriend, thank you for your continuous support and limitless love.

I have truly enjoyed this process, and I hope this shines through when reading my text.

With best regards,

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TABLE OF CONTENT

1.0 Introduction

1.1 Background ……….... 1 1.2 Research Question ………... 2 1.3 Contribution ……….. 2 1.4 Structure of Research ……… 3

2.0 Theoretical Framework

2.1 Mindless Consumption ………. 4

2.2 Nutritional Labeling Systems ………...… 5

2.3 Real-time Feedback ……….. 7 2.4 Hypothesis ……… 8 2.5 Conceptual Model ……… 9

3.0 Research Design

3.1 Data Collection ……….………... 11 3.2 Respondents ……… 11 3.3 Measures ………. 12

4.0 Results

4.1 Data Analysis ...……… 13 4.2 Further Analysis ………... 15

5.0 Discussion

5.1 The effect of Real-Time Calorie Feedback ……….... 21

5.2 Calorie Information Format ………... 21

5.3 Individual Differences ……… 22

6.0 Contributions

6.1 Contribution to Current Literature ……….……… 25

6.2 Contribution to Marketing Practice ……… 25

7.0 Limitations & Further Research

27

8.0 Conclusion

29

9.0 References

30

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INDEX OF TABLES AND FIGURES

Table Nr. Table name Chapter Page Table 1 Results Factor Analysis 3 12 Table 2 Mean values total calories purchased 4 14 Table 3 Summary table hypothesis and results 4 15

Table 4 Paired Samples Test - Aggregate level 4 17

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1.0 INTRODUCTION

1.1 Background

The world’s obesity is growing steadily, and as a consequence, so has the attention to consumer-wellbeing and nutrition (Soedberg, Miller & Cassady, 2012). The topics are put on the agenda both in public discussions and in research as obesity can contribute to serious diseases, skyrocketing public health costs, and as it is also one of the leading preventable causes of death (Brisette et al., 2013). Various measures have been implemented in order to offset the rise in obesity. One of these tools is nutritional labeling of products, which is intended to inform consumers about the healthiness of the foods that they can choose from, in order to decrease unhealthy food choices (Aschemann-Witzel et al., 2013).

Studies have shown that people generally have a positive attitude towards nutritional food labeling (Aschemann-Witzel et al., 2013), nevertheless, Jones & Miles (2007) states that consumers find it difficult to understand the information presented, and there is a lack of knowledge on which nutrition’s are important to examine. In light of this insight, there has emerged a wide range of labeling schemes designed to ease interpretation and comparison for the consumers, including traffic light colors (TLC) and the percentage of guideline daily amounts (GDA) (Aschemann-Witzel et al., 2013). Nevertheless, studies are still reporting mixed outcomes on the effects of the different nutritional labeling (Black and Rayner, 1992; Elbel, 2011).

As mentioned, people in general are supportive of nutritional labeling, and especially this holds true for front-of-pack (FOP) nutrition labels (Witzel et al., 2013). Aschemann-Witzel et al.’s (2013) logic behind this is that the FOP nutrition labels are more easily exposed and salient to the consumer, and because they provide information at the point where the majority of the decisions are made. This study aims to take this idea one step further by providing real-time calorie feedback to customers, through the use of smart carts, while in store, where the decisions are made. The smart shopping cart looks like a normal one except

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designed to track in store spending, as illustrated in figure 1. They can offer a range of different services such as product recommendation, special offers, recipes and nutritional information (van Ittersum et al., 2013). The smart shopping cart is not a new phenomenon, but largely due to the lack of knowledge in terms of influence on shopping, spending and satisfaction, the cart has not been embraced despite its usefulness and potential (van Ittersum et al., 2013).

1.2 Research Question

The purpose of this study is to find and measure the effect of real-time calorie feedback on healthiness of consumer choices and total calories purchased. Specifically, this research will investigate the following questions:

1) How real-time calorie feedback will influence total calories bought per basket.

2) How shoppers adjust their behavior when confronted with real-time calorie information.

3) How alternative types of real-time calorie information affects consumers differently.

1.3 Contribution

A range of studies has been devoted to find the effects of nutritional labeling. The research of Hersay et al (2013) reviewed 111 articles within the topic of FOP nutritional labeling effects, and concluded that in general, the findings of the assessment suggest that FOP and shelf nutrition labels can help consumers make better food choices. However, no known research has been devoted to see whether this also holds true in a retail setting where consumers are exposed to real-time calorie feedback. New for the current research is therefore how purchase behavior will alter with the introduction of different real-time calorie feedback.

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hopefully show that the implementation in general can promote healthy in-store food decisions, and the use and awareness of calorie information.

The findings are firstly believed to be important for health education purposes, by attempting to put a halt to the continuously growing obesity. Also, as the goal of the research is to support awareness for nutrition by suggesting potential dietary changes in the retail environment, the outcome of the study is also thought to be valuable for dieters and the general public who wants to make more healthy choices. The grocery store is a particularly important research setting for food decisions, as grocery shopping can affect the eating behavior of several people over several days (Papies et al, 2013).

1.4 Structure of Research

After clarifying the motivation concerning the theme of this research project, the remaining structure of this academic paper is as follows: First, in order to build a foundation for the study, theoretical background is given where the research topic is explored in more detail, whereby the framework is laid out in the following chapter. Based on the theories and concepts from relevant academic journals, a detailed conceptual model will be constructed to best describe the proposed relationship between the established variables. Hypotheses are then formulated and will be tested in the later chapters of this research.

In chapter 3, the design of the study will be organized to best test the specified assumptions. Here, a portrayal of the research methods conducted will be given, as well as an overview of the respondents and a description of the data collection procedure. Then in chapter 4, the results of the hypothesis tests are presented and clarified, followed by a discussion in chapter 5, where earlier presented theory will be measured in light of the new findings, by drawing conclusions from the results to answer the research questions offered in chapter 1.

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2. THEORETICAL FRAMEWORK

To limit the scope of the theory review, focus is given to the effects of consumer’s mindless consumption, nutritional labeling systems, real-time feedback information, and lastly how they relate to each other.

2.1 Mindless Consumption

It is often assumed that people are rational decision makers, able to make conscious choices about food and eating, however if this were the case the obesity epidemic would not be a fact (Cohen, 2008). There are two main forces that are driving obesity; we eat too much (input), and we move too little (output) (van Ittersum, 2014). These are originally factors that we believe to have control over, however, environmental factors such as food advertising, availability, package size & shape, variety, and the presence of others, stimulates reflexive behavior by enhancing our desire to eat and increase calorie intake (Cohen, 2008). This often happens without consumers being aware of it (Wansink et al., 2009), making it exceedingly difficult for individuals to resist (Cohen, 2008). As a consequence, people will tend to largely underestimate their calorie consumption (Chandon & Wansink, 2006).

Consumers exhibit different consumption tendencies for unhealthy and healthy food, namely an overconsumption impulse for unhealthy foods, and an under-consumption impulse for healthy foods (Talukdar & Lindsey, 2013). This phenomenon might be caused by consumer’s implicit intuition that unhealthy foods are tasty, while healthy foods are assumed to be bland (Wansink et al., 2004; Talukdar, & Lindsey, 2013; Raghunathan, et al., 2006) Primarily, what most people want is tasty, inexpensive, varied, convenient and healthy foods, and roughly in that order of goal importance (Wansink et al., 2009). Because people cannot sample a food before purchasing, they rely on cues that they think are related to their desired goal (Wansink et al., 2004), and under circumstances where a hedonic (e.g., enjoyment) goal is more salient, people will tend to choose options perceived as unhealthier, even if there is no information about their healthiness relative to other options available (Raghunathan, et al., 2006). Also, habitual behaviour and outcomes of limitation in self-control usually leads to choices motivated by immediate benefit (selecting tasty caloric foods), and less motivated by actions with long-term benefit (selecting healthy “less tasty” foods) (Thorndike et al., 2012).

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(Cohen, 2008), as they can impact product choice and food consumption volume via marketing tools such as price setting, marketing communication, product quality and quantity (shape and size), and also the eating environment (access, salience, convenience, and the atmospherics) (Chandon & Wansink, 2006). Consumers affected by these marketing strategies are motivated to mindless consumption due to several reasons such as inborn preferences for sugar and fat, inability to judge volume or calories (either through visual perception or internal signals of satiety), automatic responses to priming, and/or limited cognitive capacity and self-regulatory control (Cohen, 2008).

In sum, it is evident that the unhealthful mix between marketing mechanisms and irrational consumer behavior is fundamental drivers of obesity. Given that people have limited ability to shape the food environment independently and no power to control or ignore automatic responses to food-related cues that are unconsciously perceived, it has been argued that it is the marketers’ responsibility to regulate the food environment (Cohen, 2008). Nutritional labeling of products is society’s main tool to help consumer make more rational product choices, and accordingly, the next section will be devoted to a review of the labeling properties and effects.

2.2 Nutrition Labeling Systems

The main counter-act to offset the rise in obesity has been to enhance access of information to the consumers, and the most prominent example of such is the nutritional labeling systems (Downs et al., 2009). Nutrition labels provide information as a way of improving citizens’ diets on the basis of voluntary, informed and conscious consumer decision-making (Aschemann-Witzel et al., 2013). The labelling policies are created with the assumption that, given calorie information, consumers will make more rational choices by choosing lower calorie options (Thorndike et al., 2012).

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nutrition information, only a minority actively searches for and employ this information when making purchase and consumption decisions (Chandon & Wansink, 2010; Downs et al., 2009; USDA, 2000). Even though consumers are aware of their harming diet, people tend to perceive a change in nutrition as too complicated to do anything with (USDA, 2000), and as previously shown, calorie counting is proven difficult for even the most diligent, and becomes even more problematic when environmental cues bias one’s decisions (Wansink, et al., 2009). Also, people have limited capacity to process information, so providing more can often be distracting (Downs et al., 2009).

However, there is not only bad news for nutritional labeling. As with other marketing actions, effects are context-dependent and have different outcomes depending on how they are framed (Chandon & Wansink, 2010; Downs et al., 2009). Due to this fact, a wide range of labeling schemes has emerged to enhance the ease of interpretation a comparison for consumers. The labeling formats developed as an alternative to the traditional calorie text box include traffic light colors (TLC), text describing low, medium and high content of each nutrient, and the percentage of guideline daily amounts (GDA) (Aschemann-Witzel et al., 2013; Fuenekes et al., 2007). The latter, GDA, is the most commonly used scheme (Aschemann-Witzel et al., 2013), and is thus the scheme this study will focus on for further research. GDA shows the amount in grams and percentages for calories, sugar, fat, saturates and sodium per serving (Fuenekes et al., 2007) as shown in figure 2.

Figure 2: Example of % GDA symbol (U.S., UK, and other European Countries

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1994; Hutton et al., 1986), suggesting that individuals of high health risk are more likely to change their consumption pattern when exposed to nutrition labeling. Bowen and Hopp (1994) also discovered that those at greatest risk tend to consistently underestimate the amount of fat in their diet, and not surprisingly, the high-risk category is also most likely to indicate that they know the least about dietary fat. In line with these findings, it is suggested that people in high-risk groups with an initial low health orientation are both more motivated to use nutritional information because of their personal relevance to the issue and more eager to learn as they assumed to be less knowledgeable, but with the most to gain from an understanding. However, the issue is still that the high-risk group finds the use of nutritional labeling as too challenging, and consequently the counter act fails to reduce the vulnerable consumer’s threat (USDA, 2000). As Bowen & Hopp (1994) and Hutton et al. (1986) signifies, the motivation for nutrition labeling practice is in fact dependent on that the consumers’ knowledge on how to reduce their risk, and their confidences in the ability to actually do it. It is therefore apparent that marketers need to find a measure to increase the intuitive level of nutrition labeling and enhance self-efficacy. This research suggests the use of in-store smart shopping charts equipped with real-time calorie feedback as a possible solution, and the next section will give a review of the methods possible strengths and the expected outcomes of an implementation.

2.3 Real-time Feedback

Feedback is defined as information about reactions to a product or a person's performance of a task. It includes an educational dimension as the feedback is used as a basis for improvement and learning is an outcome (Hutton et al., 1986). Due to its educational value, real-time feedback has been shown to represent an important dimension in behavior modification, communication and motivation (Darby, S., 2001), and as consumers in general have been proven to possess little or no knowledge of the level and rate of their calorie consumption, real-time feedback may act as a bridge to fill this knowledge-gap (Hutton et al., 1986), and thus increase consumer’s self-efficacy in purchase decisions (Luszczynska et al., 2007).

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interpretation, resulting in further enhancement of the effectiveness of labeling, (Thorndike et al., 2012). Under the assumption that information, provided in the right form, can lead to desirable changes in behavior (Downs et al., 2009), this study proposes that real-time calorie feedback may improve consumer well-being by reducing choice uncertainty, and thus increase the salience of the total calories in basket (that is, the level of attention shoppers place on total calories added to the basket during their shopping trip). This idea is supported by the research of Luszczynska et al.’s (2007) who found that an enhancement in self-efficacy by means of tailored feedback and verbal persuasion resulted in a long-term behavior change, where participants of the research were consuming healthier food due to regularly presented feedback on health status. Also supporting the belief is Greene and Rossi (1998) who found

that the effect of feedback on food choice improved behavior change through allowing a more salient and personalized intervention.

2.4 Hypothesis

Drawing on the information presented in the former paragraphs, three hypotheses are designed. Firstly, it is anticipated that real-time feedback will increase the salience of

consumers health motivation and enhance individuals perceived capability of choosing healthful foods (Achemann-Witzel et al, 2013), which in turn will result in healthy choice behavior (Hutton et al., 1986; Downs et al., 2009; Achemann-Witzel et al, 2013).

The first hypothesis is consequently:

H1: Consumers presented with real-time calorie feedback will reduce their total calories purchased.

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When counting calories the general adult is advised to consume around 2000 calories per day, depending on age and levels of physical activity (UK Government NHS Choices, 2012).This means that for those who wants to track their calorie consumption, they can measure consumed calories against the 2000-calorie limit. When using GDA calculations on the other hand, all numbers are given in percentages (ex. 26 % out of 100 % versus 520 out of 2000 calories), as the numbers are lower in level in the GDA condition, it is assumed that it makes the calorie count easier, as opposed to total calories. Therefore, building on the pricing strategy literature, the current study predict that the more appropriate the calorie information format is for calculating recommended consumption, the more it will increase understanding, and in turn, reduce the total calories purchased:

H2: The effect of real-time calorie feedback on total calories purchased will depend on the informational format it is presented in, where % GDA framing will reduce amount of calories more than total amount of calories framing.

As earlier proposed, the effect of real-time calorie feedback is expected to vary between different consumer groups (Grunert et al. 2010; Bowen & Hopp 1994; Hutton et al., 1986; Chandon & Wansink, 2010). By making the calorie cues highly salient and consumer friendly, it is believed that those habituated to taking unhealthy choices will have most room for improvement, as well as to benefit most from using the in-store system. Consequently, it is assumed that those with a poor health consciousness (versus high) will show the most improvements when confronted with real-time feedback information:

H3: The effects of calorie feedback will differ between low - and highly health conscious consumers. Real-time calorie feedback will a) substantially reduce calories purchased for poor health conscious consumers, and b) have no (or a lower) significant effect on calorie purchased by highly health conscious consumers.

2.5 Conceptual Model

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the independent variable of feedback. Where the more appropriate the label format is for calculating calories, the less calories will be bought, and the less health oriented the consumers are, the more it will impact the negative effects of real-time calorie feedback.

Calorie Information Format

Real-Time Calorie Feedback Total Calories Purchased

Health Oriendation

Figure 3: Conceptual Model

To summarize the idea of the conceptual model: in order for calorie information to have any effect, consumers must be exposed to them and must be aware of them. Consumer understanding and the personal relevance to the issue (high vs. low risk consumers) will then moderate the effect, and based on their understanding, consumers may use the real-time calorie feedback information to make inferences about the healthiness of the product, which, together with other information (for example the price of the product) may affect the evaluation and eventually the purchase decision with regard to the product.

In order to test the presented hypotheses and the proposed conceptual model, the next chapter will be devoted to the research design, where the method of collecting data, a portrayal of the respondents, and a detailed description of the procedure will be given, including the preparation of the data.

H2

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3.0 RESEARCH DESIGN

3.1 Data Collection

To make the proposed concepts measurable, a survey is constructed containing 19 questions to assess possible effects of real-time calorie feedback. The study has a mixed design, as three different surveys was constructed and distributed between participants. The respondents are asked to make product choices across different food and beverage categories, where the presence (versus absence) of nutritional labeling and different label formats on the packaging is manipulated, (1) no calorie feedback, 2) %GDP feedback, 3) total calorie feedback), to measure the impact of each condition (see appendix 10.2 & 10.3 for comparison). For the participants in the real-time calorie feedback conditions, the calories purchased are continuously present, and updated for each product choice (condition 2 & 3). For shoppers who will not receive real-time calorie feedback, only the total calories per product and price will be presented, while everything else is constant.

The respondents are engaged for participation via MTurk (amazon.com), where the survey is launched. MTurk is a crowdsourcing marketplace for work that requires human intelligence, and is used in order to get a representative data set, with serious respondents. To further ensure quality answers for the sample, only respondents with a high level of previously approved surveys are selected for the questionnaire.

3.2 Respondents

The survey was taken by a total of 170 respondents, where 10 of the responses were unusable due to missing data, leaving 160 functional surveys for the analysis (94,11%). Of those functional answers, 54 of the respondents were randomly assigned to the no-feedback condition (1), 48 to the total calories feedback condition (2), and 58 to the GDA % calorie feedback condition (3). The sample consisted of both younger and older adults with a mean age of 31,6 years (range = 48, min = 19, max = 67), where 87 of the respondents (54,4%) were male, and 73 respondents (45,6%) were female. All respondents had their geo-location within the boarders of United States of America, and education level achieved within the sample had an average equal to a collage degree.

3.3 Procedure

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product category there is presented four item choices. For each choice, a picture of the product, its calories and price is presented, and the consumers are then to choose between a national brand, an organic brand, a store brand or a low calorie brand. Additionally, an option to not choose any of the presented items is included so that the answers will be more comparable to the consumer’s typical shopping trip. Once a choice is made, respondents continue to the next page to buy the second item, and so on (see appendix 1 for full survey).

After all the participant’s choices and total calories in basket are recorded, participants are asked to indicate on a scale from 1-5 how much they agree (disagree) with the given statements about health consciousness, as suggested by Dutta-Bergman’s (2004) in order to reveal the individual’s interest and motivation to search, attend and comprehend health related issues (see Table 1). With the assumption of a substantial amount of common variance in the statements, a factor analysis is conducted to develop a single factor to account for most of the variance (Malhotra, 2010, p. 636). Here, the rotated factor analysis and the scree plot indicated that all items could be placed into a single factor, suggesting a systematic common to the measures. A new dimension is therefore created and labeled “health consciousness”, which proved to be a reliable and internally valid variable with a Cronbach’s Alpha of 0,83 (see Table 1 & appendix 10.4).

Characteristics Items M (SD) Cronbach’s Alpha

Health Orientation

1. Eating right, exercising, and taking preventive measures will keep me healthy for life.

3.939 (0,99) 1

0.83

2. My health depends on how well I rake care of myself. 3. I actively try to prevent disease and illness.

4. I do everything I can to stay healthy. 5. Calorie information is important to me1.

Table 1: Results factor analysis, 1 = Scores did not differ significantly between the three conditions.

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4.0 RESULTS

After a satisfactory number of respondents filled in the survey, the raw data is transported into SPSS Statistics. Calorie choices are recoded post-hoc, in order to assess the choices relative to each other. Also, descriptive variables such as BMI and health consciousness are split by their median to enable comparison. In the next section of this study, a description of all relevant data and methods used will be given in order to obtain a clear picture of the possible effects.

Important to note before the results are presented is that the data is checked for normal distribution but is deviating from the assumption of normality (Shapiro-Wilk Sig. levels < 0.002) (Shao, 2002, p. 357). If the dependent variable is not normally distributed this may increase the chances of finding a false positive result (Harwall et al. 1992). Fortunately, the tools used to assess the dependent measure for this study, the analysis of variance (ANOVA), has been found to stand robust against moderate deviation from normality (Schmider et al., 2010; Harwall et al., 1992) (see appendix 10.5 for details). It is accordingly assumed that all upcoming data represents statistically reliable findings.

4.1 Data Analysis

To address the first and most crucial research question, whether or not real-time calorie feedback will influence total calories bought, a one-way ANOVA is performed. ANOVA is used as it can determine whether there are any significant differences between the means of the three independent and unrelated conditions of this study (Shao, 2002, p. 482). Also the ANOVA is preferred over a t-test due to its ability to compare more than two independent conditions at the same time, reducing the risk of Type I errors by not running multiple tests on the same data (Shao, 2002, p. 445). The ANOVA therefore helps examine whether or not the feedback condition has a significant impact on how many calories the respondents has in their baskets.

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Consequently, when organizing the total calorie scores from each independent condition, it is found that the difference in mean of total calories purchased can be overlooked (see figure 3 & Table 2). If anything, more calories are bought in the two conditions that provided the respondents with the real-time feedback, although not at a significantly higher level.

Figure 4:Mean values for total calories purchased in each condition

Feedback

Condition Mean

Difference from

total mean F Sig.

1. No Feedback 11.1774 - 0.3693

0.550 0.578

2. Total Feedback 11.6933 0.1466

3. % GDA Feedback 11.7693 0.2226

Table 2: Mean values for total calories purchased in each condition

The third research question was aimed to reveal how different feedback information would affect consumers’ differently, and further H2 stated that that the effect of the feedback would depend on the information format it was presented in. However, as the feedback did not have any influence on total calories per basket, also H2 is refuted. This is, categorizing the number of calories bought by consumers into the three different conditions did not produce any statistically significant changes in the mean values.

To check H3, stating that the effects of calorie feedback would differ between low - and highly health conscious consumers, an ANOVA is also done with total calories purchased as dependent variable and health consciousness as independent. The initial expectations was that the higher the individuals health consciousness, the less the real-time calorie feedback would

11 11,2 11,4 11,6 11,8

C1 - No Feedback C2 - Total Feedback C3 - %GDA Feedback

Mean Calorie Score Plots

Weighted Calorie Score Average C1

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have an effect on total calories in basket. However, the results show that this does not hold true as predictive power and significance level is determined to 0.330 (F = 0.953, df = 1). This finding therefore opposes to H3, and one can conclude that the ANOVA has failed to reveal statistically reliable evidence of health consciousness having an impact on the total number of calories purchased (see figure 5 for illustration of means).

Figure 5:Mean values for total calories purchased for low and highly health conscious consumers.

After looking for results for the different hypotheses, one can conclude on the rather disappointing finding that there was no change produced in the dependent variable, total calories purchased, from any of the projected variables, as summarized in Table 3.

Hypothesis Description Hypothesized relationship

Sig.-value

Reject/Accept H0 H1 Feedback moderates the

healthiness of choices

÷

0.550 Accept

H2 Feedback effect depends on

information format

÷

0.550 Accept

H3 Feedback effect differs between

customer groups

÷

0.330 Accept

Table 3: Summary table of hypothesis and results

4.2 Further Analysis

The second stated research question aimed to discover how consumers adjust their shopping behavior when confronted with real-time calorie feedback. It is thus interesting to see whether

10,5 11 11,5 12 12,5

Health Conscious Not Health Conscious

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the altered feedback conditions has an effect on the respondents shopping behavior in early versus late shopping decisions, and also how this in turn may have a consequence for the total calories purchased.

To do so, the 11 product choices are divided into two different sections; early and late product choices, where the total amount of calories is calculated as index scores for each part of the shopping trip. As the same respondents are analyzed in both sections, it is assumed that early and late choices are dependent on each other, and repeated measures ANOVA is therefore chosen as a method of analysis. This test enables mean comparison of responses where members of a random sample are measured under a number of different conditions (Shao, 2002, p. 475). As the sample is exposed to different conditions in turn, early versus late stages of the shopping trip, the measurement of the dependent variable is repeated.

- Same dependent variable

- Same respondents - Same condition

Figure 6: Illustration of respondents measured at early choices, and late choices.

Although the health consciousness of the respondents was formerly disclosed with a non-significant influence on the total amount of calories bought, a different proxy for health consciousness, the respondents BMI level, is also assessed to check for possible effects. Thus, in addition to different feedback conditions as independent variables, the variable BMI is included to explore possible interaction effects due to individual differences in the respondents.

First, a simple ANOVA is done on aggregate level to see if there is a general trend in the dataset. As the results indicate with a Sig. level of 0.031, there is support of a difference in the healthiness of choices between the two stages of the shopping trip (see table 4). Specifically, the respondents seems to make more healthy choices at the start of the trip, as the mean values in table 3 has a negative value for the difference in early choices.

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Prod Choice t Difference Mean

Sig (2-Tailed) 95% Confidence interval of the difference Lower Upper

Early Choice -

- 2.175 -0.058 0.031* -0.1124 -0.0054

Late Choice

Table 4: Paired Samples Test - Aggregate level, difference in caloric choices during shopping trip. *

= Sig. at 0.05 level

As it is wanted to investigate the impact of feedback condition and BMI on total calories purchased, the data is next divided into two groups where one contains respondents with a high BMI, and the other those with low BMI levels. Then further, the respondents are grouped according to their randomly assigned feedback condition (1, 2 or 3). This is done with the earlier mentioned repeated measures, again because it enables determination of any difference in the choices throughout the shopping trip, which can only be caused by condition specific reasons or personal variances. Thus, this design now has two between-subjects factors: BMI and feedback condition. Further, the single within-subjects factor is added, namely early versus late choices, in order to measure each subject’s total calories at the two different points.

The repeated measures analysis also shows a substantial influence of the main within-subjects effect, early and late decisions, with a significance of 0.035 (F = 4.530, df = 1), supporting the previous findings of a difference in total calories purchased at earlier versus later stages of the shopping trip. Then in the next step, the possible influence of the between-subjects variables is assessed. It is first found that the type of calorie feedback the respondent is exposed to does not significantly change the amount of calories bought in early versus late stages of the shopping trip (Sig. = 0.855, F = 0.156, df = 2), as illustrated in figure 7.

Figure 7: The within subject effect of early vs. late decisions on feedback condition & total calories

Feedback

Condition Calories Total Sig. 0.035 Sig. 0.578 X

Early / Late

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Sig. 0.008

Though feedback was not found to change early versus late choices, the influence of BMI does have a significant positive effect, indicating an upward change between calories purchased in early versus late stages, depending on whether the respondent has a high versus low BMI level (Sig. = 0.008, F = 7.322, df = 1).

Figure 8: The effect of BMI on the within subject variable early vs. late choices

These results inform about an overall significant difference in means when BMI is included as a moderator, but it is not yet known in what way these differences occur. To do so, the descriptive statistics from the repeated measures is reviewed, as they allow the discovery of which specific means differed (see appendix 10.7). By interpreting the numbers one can first see that those with a BMI below the median seems to buy a stable amount of calories at the early and later stages of the shopping trip (mean value early choices = 1.07, mean value late choices =1.06). When looking at the respondents with a higher BMI, one can conversely see a perverse effect across all conditions with a substantially larger amount of calories being bought at the later stages of the shopping trip (mean value early choices = 0.9611), mean value late choices =1.0940) as illustrated in figure 9.

Figure 9: Difference in mean for high vs. low BMI respondents, at t1 & t2

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Sig. 0.049 Sig. 0.578 X

When performing a repeated measure analysis, the variances among the between-subject variables are also given. This output reveals some noteworthy findings, particularly that although as a main effect, feedback condition is found to be non-significant, the combination of feedback condition and the continuous moderator BMI (the between-subjects interaction effect) is producing a significant change in the dependent variable, at a 0.05 Sig. level (0.049, F = 3.069, df = 2). One can subsequently conclude that the persons BMI level moderates the effect of the different types of feedback, as illustrated in figure 10 (see appendix 10.8 for detailed output).

Figure 10: The moderating effect of BMI on feedback condition & total calories

As with the within-subjects effect, the descriptive numbers of the output must be assessed to determine in what way these between-subject effects occur. Based on this assessment, figure 11 is constructed as a visualization of the estimated differences.

Figure 11: Estimated means showing difference in High/Low BMI & feedback condition

Surprisingly, from the descriptive statistics one can see that the respondents with a low BMI are buying the most total calories in the two conditions where feedback is continuously presented and updated (mean difference = C1 – ((C2+C3)/2) = - 1,66 = 7% average change).

9 9,5 10 10,5 11 11,5 12 12,5 13

Low BMI High BMI

Me an Ca lor ie Scor e

Estimated Means

C1 - No Feedback C2 - Total Feedback C3 - %GDA Feedback Feedback

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This trend is not found amongst the high BMI respondents, as for them, feedback specific condition is not producing any striking difference.

Figure 12: Estimated means showing difference in High/Low BMI, t1/t2 & feedback condition

To further visualize the effects found it total, the data is again divided into the early versus late decisions, as well as the between-subjects variables BMI and feedback condition (figure 12). In sum, it is found that the upward change in unhealthy choices during the shopping trip is dependent on the respondents BMI level. The change in calories is in fact unique for high BMI level respondents, as those with lower levels are seemingly unaffected of point in time. Thus, the respondents with a high BMI purchases fewer calories at the start of the shopping trip than the counterpart, but they also buy a higher amount of calories towards the end. Furthermore, it is found that the effect of different feedback conditions is visible for the low BMI level respondents, where those presented with updated calorie numbers is increasing their total amount of calories bought on a steady level from the start to the end, unlike those who are not given any feedback.

0,8 0,85 0,9 0,95 1 1,05 1,1 1,15

Low BMI t1 Low BMI t2 High BMI t1 High BMI t2

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5.0 DISCUSSION

5.1 The Effects of Real-Time Calorie Feedback

The findings of the previous section reveals that real-time calorie feedback does not influence shoppers in making healthier product decisions on a general level, as the shoppers who received continuous feedback on the total calories of their choices did not show a substantial difference in calories bought when compared to the no-feedback condition. This finding is conflicting with the fundamental idea of this research, namely that calorie information, would lead to less calories in basket due to increased salience of healthfulness of product, as well as a supposed reduction in choice uncertainty for the consumer. This basic conception, supported in much of the previous literature from the field (Downs et al., 2009; Greene and Rossi (1998); Achemann-Witzel et al, 2013), is now questioned in relation to real-time calorie feedback, as there is a lack of significant results for this study. However, as the research of Hutton et al (1986) states, one must be cautious against concluding that any type of feedback, under any conditions, directed at any population, will produce positive results’. As Gaskell et al. (1982) also points out, understanding how feedback does or does not work based on a single occasion may not be representative, as feedback is a learning process, where the effects are often not visible till after multiple interventions. This therefore suggests that the effects of real-time calorie feedback might be more evident with time, where an adaption to the system and a learning progression might have taken place, thus possibly explaining why the feedback intervention did not change how the consumers made their choices at this instance. Also supporting this view is the previously presented study of Luszczynska et al. (2007), who found that tailored feedback did enhance consumers’ self-efficacy, but only after multiple interferences of regularly presented feedback on health status.

Additionally, it is widely known that people have a limited capacity to process information, so when providing the respondents with more, this might simply have been too distracting to take notice of, an outcome which has been the case for many prior nutritional labelling efforts (Downs et al., 2009; Hieke and Wills, 2012).

5.2 Calorie Information Format

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reduce the total calories purchased. This assumption originated from the fact that simplified labeling is found to increase customer attention, understanding, use and purchase behavior (Hersey et al, 2013), and also as the experienced ease (or difficulty) of calorie calculation is

thought to affect people’s judgments of magnitude of numerical differences (Thomas & Morwitz, 2006). In contrast, this study demonstrates that the type of feedback the respondents are presented with did not produce any significant difference in the total calories bought, and

between the two feedback conditions, the variance was barely visible and therefore possible to neglect.

As earlier mentioned, one of the assumptions of this research is that information, provided in the right form, can lead to desirable changes in behavior. Still, as Chandon and Wansink, (2010) and Downs et al. (2009) signifies, the effect of calorie labeling is context dependent, and there are different outcomes depending on how the calorie information is framed. It is therefore further questioned whether the format of total calories per product or the % GDA presentation of calories can be said to truly be “the right form”. As there are many variations of calorie labeling schemes, one could argue that altered versions, such as color-coding or text referring to content, may be more appropriate for reducing total calories bought. This view is supported by Achemann-Witzel et al. (2013), who revealed that the various label elements do differ in influence; where color-coding is found to especially increase understanding and use, when compared to summary labeling systems such as %GDA and total calories per product.

5.3 Individual Differences

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effect on the different BMI level respondents, and the predictions prior to the research is therefore to some extent supported when looking at early versus late product decisions, by the fact that the low-risk respondents were seemingly unaffected by point of time in shopping, while the high-risk respondents demonstrated a careful shopping behavior in the early stages although this diminished towards the end.

One possible rationalization for the shift in calories displayed by high BMI respondents could be because they are experiencing source depletion. The idea of source depletion based on the fact that people have a finite pool of regulatory resources that allows them to overcome emerging urges and undesirable behavior, however, as the pool is finite, individuals who are forced to make multiple choices that require self-regulation may temporarily deplete this reservoir, and in turn prevent the individual to draw upon enough regulatory resources to sustain the intended goal (Vohs & Faber, 2007). Consequently, people tend to make unhealthy choices immediately after having exerted substantial self-control due to a loss in cognitive resources and willpower (Friese et al., 2008). This view is also supported by Scheibehenne et al. (2007) who states that human decision-making is adaptive, and that there is substantial evidence that people make use of a wide repertoire of heuristics in order to make simplified choices when lacking the ability to achieve well-reasoned decisions. Habitual behaviour and outcomes of limitation in self-control therefore usually leads to a shift in focus from intended goal to immediate gratification (Cohen, 2008). When translating this theory in relation to this particular research, it is claimed that the respondents who are not habituated to make healthy choices (high BMI level respondents) are assumed to exert more energy during the shopping trip. This may lead to an increase in impulse behavior towards the end, where less regulatory resources are available and decisions are increasingly based on simple heuristics, leading to poor self-regulation and a rise in unhealthy choices.

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intended goal as a justification to subsequently indulge in incongruent and unhealthy behavior. Specifically, participants close to completing their weight-loss goal were demonstrating self-licensing behavior by being more likely to select a calorific chocolate bar as a gift for themselves. So in the same manner, one could argue that the high-risk consumers in this research, who were confronted with typical self-regulation dilemmas, may have used their former healthy choices to license the subsequent indulgence.

Further the assessment of individual differences revealed that when combining the effect of feedback condition and the moderator BMI there is a significant change in the total calories purchased. More specifically, low risk respondents are purchasing more calories when provided with real-time calorie feedback. This perverse effect shown by the low BMI respondents can possibly be explained by study of Downs et al. (2009), who discovered that providing calorie information actually promoted higher calorie consumption among weightwatchers. They explain their findings based on the idea that people who want to achieve and sustain a certain goal may motivate themselves by exaggerating the magnitude of the threat. Thus, the low risk participants might try to encourage themselves to buy low-calorie products by overstating their low-calorie estimates, but when learning the true value of their choices this leads to a downward correction in evaluations, resulting in an increase in calorie intake (Downs et al, 2009).

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6.0 CONTRIBUTIONS

6.1 Contributions to Current Literature

The main theoretical contribution of this research is the findings of the different influence of calorie feedback between high and low risk consumers. This supplements existing theories on consumers’ mindless behavior, nutritional labeling effects and in-store decisions, as well as providing the novel and detailed evidence of how exactly the change between consumers unfold and adjusts during the shopping trip. This new finding specifically contributes to the body of literature on in-store decision-making and consumer wellbeing.

Previous literature within the field has advised future researchers to investigate new marketing tools, which can effectively interfere with the current food environment (Papies et al, 2013; Cohen, 2008; Downs et al., 2009). Such tools are needed as the existing practice of nutritional labeling has only been found to produce minor behavioral changes amongst customers, which therefore creates the requirements of further efforts in order to nudge consumers’ into making better food choices (Achemann-Witzel et al, 2013). The current research has proposed the means of smart shopping carts as a new device to attack the unhealthy and highly habitual behavior of the customer. However, as the results of this study were slim and somewhat perverse, the question is raised whether the real-time calorie feedback truly has the power to decrease the amount of harmful choices made in a retail setting. The findings can therefore best be seen as an introduction to further studies required to measure the total impact of real-time calorie feedback, and suggestions for future will therefore be given in the following chapter.

6.2 Contributions to Marketing Practice

Despite the fact that this current research failed to reveal significant effects for an implementation of real-time calorie feedback to reduce unhealthy buying, it is believed that the study can contribute with relevant insights for marketers and retailers.

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of such intervention tool is seen as a long-term investment, as it is recognized that promoting good nutrition may help supermarkets gain additional loyal customers, where profits may increase accordingly from an enlarged customer base (Winett et al., 1988). Moreover, as a customer’s use of smart shopping carts is voluntary, it is assumed that an implementation of the calorie feedback would not discourage current or future shoppers to shop at the specific retailers. In fact, consumers already use technology in various ways during their time in the store, and FMI (U.S Grocery Shopper Trends, 2012) found that during the grocery shopping more than 7% of the total shoppers are actually likely to look for nutritional information on their smartphones. This is a rising trend, and consequently, the ongoing shift is forcing retailers to rethink merchandising from items and into ideas (FMI - U.S Grocery Shopper Trends, 2012). So as consumers have already largely adapted to this new behavior, retailers will have to decide whether to be proactive and take advantage of it, in order to ensure future returns of the shoppers.

For the proactive retailers, an implementation of the smart charts may be seen as a store differentiation strategy to bolster individuals against the impact of hedonic cues, and could in turn lead to a shift in customer focus from prices, to the added value of extra services (Anderson & Narus, 1998). However, further research on implementation and the subsequent customer satisfaction is needed to provide truly dependable managerial implications on this subject of matter.

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7.0 LIMITATION & FUTURE RESEARCH

There are several limitations to this study and so this next chapter is devoted to address these specific restrictions in turn. After the limitations are clarified, suggestions for further research will be given.

7.1 Limitations to the Study

As earlier mentioned in the discussion chapter, one major disadvantage of this study is that it was conducted at only one specific point in time. This prevents the respondents to show any form of progress, and a longitudinal study is therefore believed to better contribute to more robust insights on behavioral changes and further understanding to the field of smart-carts.

It is also questioned whether the consumers had enough motivation to make mindful choices when participating in an online questionnaire and not making the choices for actual consumption. Participants who might have been distracted or did not take the survey seriously can therefore have biased the outcome and decreased the external validity of the test when compared to field or laboratory studies (Aronson et al. 1998).

As this research was conducted with only respondents within the U.S boarders, it is not generalizable across the general population or for different nations and cultures. Therefore, for future references, it would be interesting to investigate whether product preferences or consumption behaviors vary across different nationalities.

In this study, the number of calories per product was used as a proxy for overall healthfulness of item chosen. This could cause a drawback due to the fact that a product’s overall dietetic value does not necessarily depend on how many calories it contains, but rather on what type of calorie, and also it’s total of proteins, sugar and saturated fat, and similar. This might have biased the view of the products presented, and it is therefore suggested that providing total nutritional value may be better in determining overall healthfulness of product.

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7.2 Suggestions for Further Research

The limitations for this study can further be translated into worthwhile opportunities for further research. Firstly, subsequent studies are advised to recruit samples of shoppers directly from supermarkets instead of using online surveys, to limit possible confounding variables. This is believed to add more authenticity as consumers might take the task more truthful when presented with feedback in the actual retail environment, where respondents also actually take home their chosen products.

To tackle the concern of feedback as a learning progress, it is proposed researchers requests the involved participants to document their receipts over a given period in order to asses possible progresses in healthy choice behavior. Also as what the products consumers’ buy is only an indication of what they might later consume (Winett et al., 1988), additional monitoring of actual eating habits is required to fully assess overall health impact of a feedback intervention.

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8.0 CONCLUSION

The goal of this particular study was to uncover the possible effects of real-time calorie feedback on consumers’ purchase behavior, via the use of smart shopping charts in super markets and grocery stores. This was done as an effort to modify food purchases in accordance with healthful guidelines, in order to improve consumer wellbeing, and subsequently reduce the risk of chronic disease and related healthcare costs. Supermarkets and grocery stores are important environments where interventions may increase the availability of, and access to healthier food choices. Although the intervention tool proposed in this study failed to show significant changes in healthfulness of product choices, it is still believed that a long-term oriented research, where respondents are tracked over a certain point of time, can give significant effects of real-time feedback on calories purchased by allowing a learning progression. Therefore multiple suggestions for further research related to in-store technology have been proposed.

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10.0 APPENDIX

10.1 Survey condition 1: No calorie feedback

Dear respondent,

You are about to enter the store to purchase your groceries. For each item you will be presented with four choices. For each choice, a picture of the product, its price and calories per serving will be presented to you. Once you made your

choice, you continue to the next page to buy the second item, and so on.

Daily recommended calorie intake for adults is 2000 Cal.

Click " >> " when you are ready to go shopping.

Choose product from the list:

Great Value Pasta:

225 Cal / Serving Price $1,89

Barilla Whole Grain:

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Choose a product from the list:

Pure Daily 1% Fat Milk:

Yeo Valley Organic Whole Milk:

Home Brand Whole Milk:

Dale Farm Whole Milk:

139 Cal / Serving

Price $2,43 None of the items

Coca Cola:

Cola Home Brand:

Coca Cola Light:

0-3 Cal / Serving $1,39

Bio Organic Cola:

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Home Brand Cocoa Puffs:

Cocoa Chrispy Organic Brown Rice:

Fiber One Chocolate:

80 Cal / Serving Price 3$,59

Nesquick Cereal:

125 Cal / Serving

Gutton Chocolate Chip:

121 Cal / Serving Price $2,55 Grandma's Chocolate Chip: 160 Cal / Serving Price $2,60 Earthbound Farm Organic Cookies: 178 Cal / Serving Price $2,75 Home Brand Chocolate Chip Cookies: 147 Cal / Serving

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Rudis Organic White:

Smart Buy White:

Weight Watchers White Toast:

Hovis Soft White:

59 Cal / Serving

Lean Ground Beef:

Beef Mince:

Home Brand Minced:

Organic Beef Minced:

221 Cal / Serving