Behavioral Learning: A Solution for the Global Obesity Pandemic

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Behavioral Learning: A Solution for

the Global Obesity Pandemic

A Visual Point-of-purchase Food Shopping Intervention

By Fernando Ontañón Pérez

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Master Thesis

Behavioral Learning: A Solution for

the Global Obesity Pandemic

A Visual Point-of-purchase Food Shopping Intervention

Fernando Ontañón Pérez

University of Groningen

Faculty of Economics and Business

MSc Marketing Management

15-01-2018

Boterdiep 9

9712 LH Groningen

+31 (0) 645851154

Fer_ontanon@hotmail.com

S3291057

First Supervisor:

Prof. dr. ir. Koert van Ittersum

Second Supervisor:

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ABSTRACT

The exponential rise of overweight and obesity rates during the last decades has drawn the attention of an unprecedented number of governments in order to find a solution to the negative and alarming consequences of this pandemic. Consequently, governments have started to pressure manufacturers and retailers as key stakeholders in this situation, claiming their support and intervention at the point-of-purchase regarding this issue. Food marketing literature has investigated different strategies and possibilities for the implementation of point-of-purchase interventions as well as their efficacy nonetheless with inconclusive results. In particular, information and availability strategies have led to mixed results which might be explained as a result of the limited effectiveness of a currently applied rational and conscious cognitive learning approach during the decision-making process, hinting an opposing unconscious and extra-rational behavioral approach, and related promotional and price strategies as promising alternatives. Both strategies were implemented in this research, revealing that a promotional strategy with a focus on a healthy visual food stimulus increased the probability of healthy food purchases while a price strategy with a price promotion did not show significant effects. It is worth highlighting that the effects of both strategies were stronger for respondents with overweight and obesity.

Keywords: obesity, behavioral learning, POP shopping intervention, promotional

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PREFACE

I would like to thank my supervisors, dr. Koert van Ittersum and Martine van der Heide, who supported and guided me throughout this research paper to get the best out of this final master thesis project. I truly appreciated the trust and confidence they placed in developing this topic of my own personal interest. As this thesis does also represent the completion of my studies here in Groningen, I would like to take this opportunity to thank my parents for their unconditional support in each of the decisions I have made in life. Although this year has been tough and challenging, it has also been satisfying and hope to make my family proud.

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Table of Contents

Abstract... ... 3

Preface... ... 4

1 Introduction ... 6

1.1 Background of the study... 6

1.2 Causes and consequences of obesity ... 8

1.3 The aims and objectives of the study ... 11

1.4 Research question ... 12

2 Theoretical framework ... 13

2.1 Point-of-purchase shopping interventions ... 13

2.2 Behavioral learning approach and recipe stimulus ... 16

2.3 Price promotion stimulus ... 20

2.4 Conceptual model ... 22

3 Methodology ... 23

3.1 Research Method ... 23

3.1.1 Study Design ... 23

3.1.2 Survey Design Description ... 25

3.2 Operationalization of Variables ... 26 3.2.1 Dependent Variable ... 26 3.2.2 Independent Variables ... 26 3.2.3 Moderator Variable ... 26 3.2.4 Control Variables ... 27 3.3 Plan of analysis ... 27 4 Analysis………..………28

4.3 Binary Logistic Regression ... 30

4.3.1 Control Variables ... 30 4.3.2 Assumptions ... 31 4.3.3 Final model ... 32 5 Conclusions ... 34 5.1 Discussion ... 34 5.2 Conclusion ... 37

5.3 Limitations and future research recommendations ... 39

Reference list ... 42

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I INTRODUCTION 1.1 Background of the Study

Over the past two decades, few health topics have been able to draw the amount of attention as much as overweight and obesity. Since the World Health Organization (WHO) identified the whole situation as a global epidemic also known as “globesity” at the beginning of the 1990s (WHO, 2003a), expert and technical consultations have taken place in order to understand this complex phenomenon, spread awareness to the public and private sector about its negative consequences, and find effective solutions through interventions (Chandon & Wansink, 2012; Glanz, Bader, & Iyer, 2012; Payne, Niculescu, Just, & Kelly, 2014; Seymour, Yaroch, Serdula, Blanck, & Kahn, 2004).

Overweight and obesity are defined as abnormal or excessive fat accumulation that may impair health (WHO, 2014a). Both are classified using the body mass index (BMI), a simple index of weight-for-height, in which the person’s weight in kilograms is divided by the square of his height in meters (kg/m2_{). The WHO defines overweight for BMI} values greater than or equal to 25 and obesity for BMI values greater than or equal to 30, although it should be taken into consideration as a guide, as fatness levels might differ between individuals (WHO, 2014a). Ideal BMI values should lie between 20 and 22 for adults regardless of their gender (Rashad & Grossman, 2004). Undoubtedly, this general definition of the BMI index has been accepted and implemented in related literature (Bleich, Cutler, Murray, & Adams, 2008; Hill, Wyatt, Reed, & Peters, 2003; Ng et al., 2014; Rashad & Grossman, 2004; Swinburn et al., 2011).

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overweight in children and adolescents of developed countries was also noticeable high with values between 22.6% and 23.8% for girls and boys respectively. To make the picture clearer, by 2016 more than 1.9 billion adults were overweight within which 650 million were obese (WHO, 2017) representing more than 20% of the world population. In addition, 41 million children under 5 and over 340 million children and adolescents aged 5 to 19 were overweight or obese as well. The WHO stated that obesity has nearly tripled since 1975 reinforcing its prior declaration about the importance of overweight as one of the five leading global risks for mortality worldwide (WHO, 2009).

Although it was originally believed that obesity and overweight were diseases mainly present in developed countries (WHO, 2002) as in the United States where nowadays approximately two thirds of woman are overweight or obese (Hruby et al., 2016) or West Europe in which overconsumption is feasible due to a wealthier society, obesity rates are increasing in all parts of our world without exceptions. Even in Japan, the country with the highest life expectancy (WHO, 2014c), which has one of the lowest rates worldwide regarding obesity (Tomer, 2011) is also experiencing this global burden. There are developing countries in which rates already exceed 50% of the population as in the case of Tonga, Kuwait, Qatar or Libya (Ng et al., 2014).

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1.2 Causes and Consequences of Obesity

It is nevertheless important, in order to understand the obesity epidemic as a health and economic phenomenon, to elucidate its multiple causes and consequences as well as implications for society.

The fundamental cause for overweight and obesity is the energy imbalance between calories consumed and calories expended (WHO, 2014a) mainly driven by changes in the global food system (Swinburn et al., 2011) that has experienced a negative nutrition transition (Glanz & Mullis, 1988; Hawkes, 2008; Payne, Niculescu, Just, & Kelly, 2014) over the past several decades. This nutrition shift promoting overconsumption of energy (Hill, Wyatt, Reed, & Peters, 2003) also known as an ‘eat more’ approach (Guy, 2006) has enabled an increase of a wide variety of affordable, packaged, and processed energy-dense food laden with salt, fat, and sugar in our environment (Guy, 2006; Hill, Wyatt, Reed, & Peters, 2003; Just & Payne, 2009; Milliron, Woolf, & Appelhans, 2012; Swinburn et al., 2011; Volpe & Okrent, 2012). Leading operators in the market as supermarkets have focused during the past 25 years on processed food categories (Hawkes, 2008) driving this diet shift from traditional to westernized (Charlton, Kähkönen, Sacks, & Cameron, 2002). The linkage of a parallel increase in food consumption and obesity rates has been supported by empirical evidence (Bleich, Cutler, Murray, & Adams, 2008). However, although the effects of this nutrition transition have become more visible in last few decades, consumption became an essential part of our lives long time before and the marketing environment has developed accordingly improving distribution and offering affordable products thanks to innovation and technology (Szmigin & Piacentini, 2015).

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Carroll, Ogden, et al. 2010; Hruby et al., 2016; Just & Payne, 1988; Lock, Pomerleau, Causer, Altmann, & McKee, 2004; Rashad & Grossmann, 2004; Swinburn et al., 2011; Tomer, 2011). Studies confirm that the mean recorded level of consumption in nearly half of the EU 15 member states does not fulfill the recommended level by the WHO (Joffe & Robertson, 2001) although a high percentage (75%) of the population believes that it is necessary to eat at least two pieces of fruit per day (Southon, 2000).

The fact that the food industry is the second largest advertiser in countries as the United States (Tomer, 2011) suggests a high impact of food and beverage marketing on consumers’ preferences and purchases (Grier & Kumanyika, 2008). Population exposure to unhealthy advertising is high designating only 2% to healthy food (Tomer, 2011), increasing the presence of junk food, increased portion sizes, and fast-food promotions (Grier & Kumanyika, 2008) molding consumers’ minds. These negative attributions implicate retailers marketing strategies, supermarkets in particular, as an active contributor to the global pandemic.

The negative and alarming consequences of obesity and overweight, can be classified as internal or in relation with the individual and external or related to society. The internal consequences are the proven significant health risks for multiple and severe diseases as hypertension, coronary heart disease, cardiovascular disease, diabetes, and certain types of cancer including colon, breast, or endometrial (Finkelstein, Ruhm, & Kosa, 2005; Flegal, Carroll, Ogden, et al., 2010; Hruby et al., 2016; Joffe & Robertson, 2001; Just & Payne, 2009; Liaukonyte, Rickard, Kaiser, Okrent, & Richards, 2012; Rashad & Grossman, 2004; Swinburn et al., 2004; Tomer, 2011) that are the most important causes for premature death in the European Union (Joffe & Robertson, 2011). Global premature death rate estimations were around 2.6 million in 2004 (Lock, Pomerleau, Causer, Altmann, & McKee, 2004) and 3.4 million in 2010 (Ng et al., 2014). In the United States premature death rates oscillate between 300.000 (Tomer, 2011) and 400.000 (Finkelstein, Ruhm, & Kosa, 2005; Rashad & Grossman, 2004) depending on studies and dates, only slightly surpassed by tobacco (Finkelstein, Ruhm, & Kosa, 2005) confirming a medical crisis (Liaukonyte, Rickard, Kaiser, Okrent, & Richards, 2012). In addition, there is also a clear association with psychological disorders such as depression (Hill, Wyatt, Reed, & Peters, 2003; Rashad & Grossman, 2002; Tomer, 2011).

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2006; Hill, Wyatt, Reed, & Peters, 2003; Rokholm, Baker, & Sorensen, 2010). Countries are spending between 2% and 6% of their total healthcare costs (Swinburn et al., 2011) to combat overweight and obesity related diseases that involve long and costly treatments due to its multiple complications (Rashad & Grossman, 2004). The related health care costs in the United States even surpass these values spending around $150 billion per year (Finkelstein et al., 2012; Liaukonyte, Rickard, Kaiser, Okrent, & Richards, 2012) accounting for 7% to 9% of the total annual cost (Finkelstein et al., 2012; Tomer, 2011). Other social consequences as social exclusion, discrimination, lower professional and private satisfaction (Tomer, 2011), higher levels of absenteeism, and related lower productivity for the private sector (McCormick & Stone, 2006) do also impact society and produce higher costs for governments. The unsustainable deterioration of the health system in most countries and other secondary negative consequences in other parts of society as at the workplace, has encouraged and forced a rapid and effective governmental action. Nonetheless, as the only solution for a reduction in obesity is at the individual level, the government has to be able to provide support, recommendations, and collaborate actively and committed with the different public and private stakeholders (WHO, 2014b). A rising number of countries have therefore developed public health policies aiming to enhance food systems, set dietary guidelines for citizens, improve understanding through public and social marketing campaigns, and promote physical activity as recommended by the WHO (WHO, 2014b).

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epidemic, development and effectiveness degree of food shopping interventions, and cooperation between government and retailers to find effective long-term solutions to the global obesity pandemic.

1.3 Aims and Objectives of the Study

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down processing way, is probably not the best solution to solve this issue. A focus on health education might be less relevant than price and availability of healthy food options at the point of purchase (Joffe & Robertson, 2001). Therefore, a change in the environment instead of a change in the individual might be a more effective mechanism. Environmental interventions should be prioritized (Seymour, Yaroch, Serdula, Blanck, & Kahn, 2004; Swinburn et al., 2011) addressing the current need for a more multifaceted intervention approach (Finkelstein, Ruhm, & Kosa, 2005). Several studies (American Dietetic Association, 2007; Moorman & Matulich, 1993) support the relevance of price and convenience compared to nutrition.

This research paper aims to understand the importance of environmental or point-of-purchase food interventions addressing the need to comprehend the relevance and effectiveness of pricing and advertising practices (Glanz, Bader, & Iyer, 2012) in food marketing tactics. The objective is to demonstrate that behavioral learning in contrast to a currently applied cognitive learning could be a comprehensive solution to promote the purchase of healthy food and tackle the alarming obesity burden in society.

1.4 Research Question

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II THEORETICAL FRAMEWORK 2.1 Point-of-purchase Shopping Interventions

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(Finkelstein, Ruhm, & Kosa, 2005; Glanz, Bader, & Iyer, 2012; Payne, Niculescu, Just, & Kelly, 2014; Seymour, Yaroch, Serdula, Blanck, & Kahn, 2004).

In order to provide an accurate and diverse review of POP food interventions, forty-two different interventions in current related literature, published between 1970 and 2014, will be revised in this paper. Thirty-eight of these interventions have been selected from a widely used and quoted literature review published in 2004 by Seymour, Yaroch, Serdula, Blanck, & Kahn, which includes interventions at different setting as worksites and university cafeterias, vending machines, supermarkets, and restaurants. In addition, four additional interventions in supermarket settings published between 2010 and 2014 (Gittelsohn et al., 2010; Milliron, Woolf, & Appelhans, 2012; Ni Mhurchu, Blakely, Jiang, Eyles, & Rodgers, 2010; Payne, Niculescu, Just, & Kelly, 2014) have been included to make the review more up-to-date.

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the studies. In addition, the most positive results have been found in interventions with vending machines for which all studies were positive. Results based on the strategy classification do not show more or less favorable outcomes for any strategy as all reach positive values in approximately 70% of the studies.

A first comparison of the different interventions show that interventions at the point of purchase have been less effective compared to interventions at the point of consumption regardless of the implemented strategy, although these results need to be evaluated more in depth as not all of them intended to increase the purchase of fruits and vegetables but did also aim to decrease the purchase of unhealthy food, which is not relevant and the main aim of the study. On one side, more than 70% of the positive interventions at the point of consumption aimed to decrease the energy intake by promoting in most cases a less unhealthy version of a non-produce product. This might be related to the fact that these interventions were more successful as it might be logic to think that it is for certain easier to make consumers purchase a substitute product with less energy intake (fat or sugar) than to purchase new and completely different products, as fruits or vegetables, which are for instance not even part of their consideration set. Regarding the different strategy types it is difficult to assess the efficacy as in two third of the interventions a strategic combination was present. Nevertheless, it is remarkable that all the successful supermarket interventions, aiming to increase the purchase of produce items, used a promotional strategy by introducing visual elements as labels, placards, advertisement, or signage (Curhan, 1974; Levy, Schucker, Tenney, & Mathews, 1985; Milliron, Woolf, & Appelhans, 2012; Rodgers et al., 1994; Payne, Niculescu, Just, & Kelly, 2014; Schucker, Levy, Tenney, & Mathews, 1992) hinting a possible higher effectiveness degree of this strategy for this setting. Although Seymour, Yaroch, Serdula, Blanck, & Kahn (2004) highlight the multiple difficulties for a valid assessment of the selected thirty-eight interventions in their study, as the lack of evaluation and information in some studies, methodology inconsistency, or different setting characteristics, their conclusion of a least effective intervention scenario for point-of-purchase settings is consistent with the priory presented results. Interestingly, the main conclusion of their review was that interventions were most effective in settings with limited choices as in the case of a vending machine or cafeteria and less effective in settings with a wide range of choices as in the case of supermarkets.

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regarding the limited effectiveness of availability strategies (Glanz, Bader, & Iyer, 2012) and information strategies (Payne, Niculescu, Just, & Kelly, 2014) at the point-of-purchase highlighting the need for a clear endorsement of the marketing mix, as promotional and price strategies, in order to rise effectiveness (Finkelstein, Ruhm, & Kosa, 2005). This corroborates the low consideration of in-store marketing applications that has taken place (Payne, Niculescu, Just, & Kelly, 2014) although settings as supermarkets represent a unique opportunity to influence eating patterns and increase healthiness (Glanz & Mullis, 1988). Mixed results, a low consumption rate of fruits and vegetables, and worldwide increasing obesity and overweight rates, addresses the need for further research on promotion strategies as well as systematic manipulation of healthier food options in experiments at the point-of-purchase (Glanz, Bader, & Iyer, 2012). Price strategies as for instance reducing prices for healthier product categories as in the case of produce or promotion strategies highlighting healthy options thanks to displays or labels have been identified as promising as they are more noticeable and attractive for consumers (Glanz, Bader, & Iyer, 2012). It is therefore interesting and relevant to further investigate the effect of both strategies in a POP food intervention context as both seem to be a prospective solution to the current obesity pandemic.

2.2 Behavioral Learning Approach and Recipe Stimulus

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consequences of obesity by Finkelstein, Ruhm, & Kosa (2005), explains the mismatch between information strategies and consumers’ decisions more precisely. The limited impact of information in the decision making process is related to the fact that a subset of consumers, in particular the obese and overweight population with self-control issues, will not make the election of a healthy lifestyle although they are aware of the information and related benefits and consequences. These interventions make an attempt to appeal deliberative thought (Payne, Niculescu, Just, & Kelly, 2014), implying a fully conscious, careful and time-consuming process of learning also known as cognitive learning (Szmigin & Piacentini, 2015). Cognitive strategies are unlikely to be population solutions being moderately effective only for certain individuals (Swinburn et al., 2011). Assumptions about food-related behavior as a function of rational decision making might not be lightly made as in some cases the marketing environment leverages the opposite processes (Just, Collin, & Payne, 2009) being this the most probable case for the current obesogenic environment at the point-of-purchase. If the rationality assumption would hold, obesity and overweight rates would have decreased over the past decades and in fact the complete opposite scenario is taking place.

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the pandemic. Nevertheless, other authors in related literature have approached this issue in the opposite way explaining that interventions, which aim to reduce obesity rates are realistic when they are directed to modify the environment (Glanz & Mullis, 1988; Just & Payne, 2008; Swinburn et al., 2011) as the global obesity pandemic is indeed the result of the response to this environment (Swinburn et al., 2011). This means that rather than focusing on the individuals trying to force and persuade them to make healthy choices in an environment that strongly promotes unhealthy choices, the focus should be shifted towards changing the environment stimulating consumers to purchase healthy products not only rationally and consciously but maximizing the potential of impulsive and extra-rational behavior.

Extra-rational decision making during food purchases are much more common than priory thought (Southon, 2000) being a reasonable explanation of the very high rate of unplanned purchases, from 43% to 93% (Payne, Niculescu, Just, & Kelly, 2014), during grocery shopping. These decisions are made with little cognitive involvement or effort (Southon, 2000) and represent the perfect opportunity to make consumers’ purchases healthier altering their behavior unconsciously (Glanz & Mullis, 1988). Marketing environments have the potential to leverage people’s consumption patterns and decisions (Just & Payne, 2009) and make them learn as a response to changes in the environment and not as a response to their internal mental processes (Szmigin & Piacentini, 2015). This kind of learning is known as behavioral learning and rejects the need to consider thoughts and feelings that have been processed internally to make decisions. Indeed we do not always need to develop a set of beliefs or thoughts or create an emotional connection before making a decision for all contexts as explained by the hierarchy of effects (Szmigin & Piacentini, 2015). This learning model has been used to understand how consumers respond to external stimuli (Szmigin & Piacentini, 2015) as consumers do not always follow a rational decision-making process and form attitudes after purchase.

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on an internal threshold but on the environment, which suggests what will be “pleasurable” for the consumer (Just & Payne, 2009). Under uncertainty consumers make the choice task easier relying sometimes on features not completely related with the product (Clement, Astrup, & Forsberg, 2014). The statement “what you see is what you choose” (Clement, 2007) is a brief but precise description of the decision-making process of most consumers. Capturing the attention is crucial (Zhang & Seo, 2014) and visual cues or elements can definitely contribute and play an active role as most important predictors of purchases (Gidlöf, Anikin, Lingonblad, & Wallin, 2017). Players in the competitive environment of supermarkets, in particular the ones of processed foods as packaging is one of the main visual elements, are aware and try to capture and gain consumers’ visual attention (Gidlöf, Anikin, Lingonblad, & Wallin, 2017) placing the produce category on a more than disadvantageous situation.

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H1: In a grocery shopping environment, a promotional strategy, which implements visual

stimuli in the form of a recipe image promoting produce products, leads to higher purchases of these products.

2.3 Price Promotion Stimulus

The importance of the price dimension for consumers at the point-of-purchase and everyday life, placing it as one of the three most important factors regarding preferences has remained unchanged since the eighties in the retail environment (Clarke et al., 2004). Prices have changed during the last decades differently depending on the product category. Generally, most products at the supermarket are nowadays affordable for a vast majority of households and some have decreased their prices as for instance products containing high amounts of sugars and fats, oils, sweets, and carbonated beverages (Tomer, 2011). However, the prices of other product categories as fresh fruits and vegetables, fish, and dairy products, which can be classified as healthy products, have increased (Tomer, 2011). This bidirectional phenomenon has led to a higher consumption of cheaper and unhealthy products, showing that price sensitivity is highly related with obesity rates (Gandal & Shabelansky, 2010).

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The food intervention review introduced at the beginning of part two, shows a positive scenario for the interventions in which price discount strategies were implemented as 71% reached positive outcomes. However, the small number of interventions, only seven out of forty-two, represents a clear limitation regarding the interpretation and validity of this positive result. Nevertheless, additional studies confirm that price discounts have a positive impact by increasing purchases of healthy food (Ball et al., 2015; Geliebter et al., 2013; Kinsey & Bowland, 1999; Ni Mhurchu, Blakely, Jiang, Eyles, & Rodgers, 2010; Waterlander, de Boer, Schuit, Seidell, & Steenhuis, 2013; Waterlander, Steenhuis, de Boer, Schuit, & Seidell, 2012; Waterlander, Steenhuis, de Boer, Schuit, & Seidell, 2013). All of the studies showed positive effects after some months compared to the control group, in which price discounts were not implemented, reaching in some cases an increase higher than 20% in six months (Waterlander, de Boer, Schuit, Seidell, & Steenhuis, 2013) or a 35% increase of fruit and 15% of vegetables purchases per household (Ball et al., 2015). However, some studies have also showed negative cross effects (Kinsey & Bowland, 1999; Waterlander, Steenhuis, de Boer, Schuit, & Seidell, 2013) as price discounts for healthy products can also lead to a rise of energy purchases. The promising positive results of price strategies, in particular price promotions for fruit and vegetables in a supermarket environment, makes it an interesting topic to be studied and examined in this research. It is therefore relevant to test on one side the positive effects that price promotions can directly have on sales, increasing fruits and vegetables but also in combination with promotion strategies which implement visual stimuli. Consequently, the following two hypothesis (H2 and H3) are formulated:

H2: In a grocery shopping environment, a price strategy, which implements price

promotions positively moderates the effect of a promotional strategy, which implements visual stimuli in the form of a recipe image promoting produce products.

H3: In a grocery shopping environment, a price strategy, which implements price

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2.4 Conceptual Model

The theory discussed in the previous sections including the different hypothesis are summarized in the following model. Ten control variables, which will be discussed in consecutive sections, have also been included in the model.

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III METHODOLOGY

In order to test the hypothesis, which were introduced in the literature review and illustrated in the conceptual model, an online study has been conducted. In this part, the research method, including study design and survey design description, operationalization of variables, and plan of analysis will be explained

3.1 Research Method 3.1.1. Study Design

In order to adequately address and answer the main research question of this study, which is to see whether a promotional strategy with a visual recipe stimulus and a price strategy with a price promotion increase the sales or choice of the produce products displayed, the following experiment set up has been developed.

As this study aims to understand the effect of different strategic elements at the point-of-purchase, the most suited setting to develop the required experiment is a location in which groceries take place as in the case of a supermarket. As a natural setting it enables to reflect the reality of the environment more precisely. However, due to the impossibility to cooperate with a physical local supermarket, the study has been conducted as an online grocery shopping experience thanks to the online survey platform Qualtrics. This platform is suited to easily recreate an online shopping experience in a simple manner. In addition, it is also possible to include several structured questions to capture all the required information for the subsequent analysis. The main advantage of this method is that the control over extraneous variables is higher, which is important as they represent a threat to the internal and external validity of an experiment (Malhotra, 2009, p. 225). In this case the extraneous variables are controlled through randomization, which means that participants are randomly assigned to one of the four experimental groups present in this study (appendix B).

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measure the effects of two or more independent variables at various levels but also the interactions between variables (Malhotra, 2009, p. 234).

Table 1. 2x2 between subjects design

Four different conditions were tested (appendix B):

1. The control condition – there will be a banner showing solely a tomato and a

cucumber and the sentence “Just add the following products to your basket”.

2. The recipe stimulus condition – there will be a banner showing a tomato and a

cucumber with the sentence “Just add the following products to your basket and enjoy the recipe”. In addition, an image of a Greek salad with a heading indicating the recipe name will also be included.

3. The price promotion condition - there will be a banner showing a tomato and a

cucumber and the sentence “Just add the following products to your basket”. In addition, two small “SALE” tags close to the products and one bigger “Special Discount” wobbler in one corner were included.

4. The recipe stimulus and price promotion condition - there will be a banner showing

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for the term “salad” that could therefore be used as the recipe stimulus and in particular a Greek salad, whose main ingredients are tomatoes and cucumber. Regarding price discounts, as the survey aims to reach respondents from different countries and socioeconomic backgrounds only sales tags and the word “special discount” were included instead of giving specific prices and exact discount percentages, which could lead to non-selection based on price-sensitivity reasons. The goal of both elements is to symbolize the general idea of a price discount or promotion.

3.1.2. Survey Design Description

The online survey has been structured into three different parts: introduction, online grocery shopping, and final questionnaire (appendix A). It took place during twelve days, from the 24th _{of November to the 5}th _{of December, including a pretest with approximately} 10 participants to ensure it worked flawlessly. Participants were mainly reached through the anonymous link generated by the platform, which was mostly sent via mobile phone (68.4%) but also thanks to posts on different profiles and groups on the social platform Facebook (31.6%). As overweight and obesity are present throughout the whole population, there were no restrictions regarding participation. The only restriction, which can be explicitly derived from the survey, is that participants have to speak English as the survey was not translated in order to be able to target a more diverse sample in terms of nationalities.

The survey starts with a short introduction screen welcoming participants and thanking them for their future feedback. In order to motivate respondents to participate, the short time which is required to complete the survey (approximately 3 minutes) is also mentioned. In a second screen, the scenario “a normal daily grocery shopping trip” is introduced and briefly explained in order to provide respondents a better understanding of the survey context.

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a high category neutrality and decrease possible result distortions due to related bias, five supermarkets were taken into account: Jumbo, Albert Heijn, Rewe, Tesco, and Carrefour. Although the number of categories displayed on the different retailers’ websites differed from eight to eighteen, in order to simplify the survey the following six food product categories, comprising 6 products per category, were created: bakery, dairy, produce, beverage, meat/fish, and dry/baking goods (appendix A). To further support the neutrality of the study, the product images displayed in the survey were also selected as neutral as possible avoiding brand specific elements to minimize possible brand bias.

Finally, a questionnaire with ten different questions has been included in the survey to get a clearer picture of the final respondent sample and be able to test them as control variables or possible covariates in the final model (appendix A). The questions collected information about: gender, age, BMI (height and weight), education level, country of origin, grocery shopping frequency, general healthiness degree, price sensitivity, cooking frequency, and degree of hunger.

3.2 Operationalization of Variables 3.2.1 Dependent Variable

The dependent variable of this study is the selection of the two products, tomatoes and cucumbers within the produce category, which can be interpreted as produce sales. The variable is operationalized as a binary variable that can take two values: 0 = when none of the products or one of them is selected and 1 = when both products are selected.

3.2.2 Independent Variables

The independent variables of this study are the different treatment conditions that are the recipe stimulus and the price discount. Both variables are operationalized as binary variables, which take the following values: 0 = when condition is absent and 1 = when condition is present.

3.2.3 Moderator Variable

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variable, which takes the following values 0 = when conditions are absent and 1 = when conditions are present.

3.2.4 Control Variables

Ten different control variables were included in the final questionnaire part, to test if they possibly explain certain variation that in other circumstances would have been captured by the independent variables in the final model (Leeflang, Wieringa, Bijmolt, & Pauwels 2015; Malhotra, 2009). On one side, more general control variables as age, gender, education, and nationality were included with simple open questions or multiple choice questions. On the other side, variables specifically related with the study as the frequency of grocery shopping, cooking frequency, and BMI (height and weight), were also included using multiple choice questions. Price sensitivity was also included with three different questions based on a 1-5 agree-disagree scale (Ailawadi, Pauwels, & Steenkamp, 2008) as it might influence the price promotion condition. General health interest with eight questions based on a 7-point Likert scale (Roininen, Lähteenmäki, & Tuorila, 1999) as it is possible that individuals with lower health interest are also less likely to choose healthy food options as produce, and degree of hunger based on a 7-point Likert scale as it might affect the number of products selected as well as the type.

3.3 Plan of Analysis

In order to test the different hypothesis, the statistical program SPSS has been used. As recommended (Malhotra, 2009) a dependence technique is more appropriate as there is one dependent variable and two independent variables (as well as a moderator). Due to the operationalization of the different variables, which are categorical (binary), a logit regression is the most suitable test. The model of a logistic regression is as follows:

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IV ANALYSIS AND RESULTS

In order to test the hypothesis and analyze the results of the proposed model, the following sections of this part will explain how the data was prepared to be valid, descriptive statistics of the sample, and the results of the binary logit regression model.

4.1 Data Preparation

The conducted survey, as described priory in part three, was imported to the statistical program SPSS. A total of 254 responses were exported although a two-step filtering and cleaning process took place in order to guarantee consistency and validity of the dataset that will be used on a next step for the analysis. On a first step, respondents who did not complete the survey until the end, i.e. their value in the column “progress” was not 100% and did not get until the last screen, were deleted.

On a second step, respondents with a 100% status in “progress” who had several missing questions were also deleted. After these two filters, thirty nine respondents were deleted from the initial dataset, reaching a final valid sample size of 215 respondents as the minimum size should be above 200 respondents in problem-solving research studies (Malhotra, 2009) in order to be able to achieve statistical reliable results. In addition, a consistency check for the open questions (age, country, height, and weight) was done correcting misspelling or small typos as for example writing their country of origin in their local language. Although most questions were correctly imported as variables with correct coding, some variables had to be computed. That was the case of the BMI, which was computed as the expression [weight / (height*height)], price sensitivity as the expression (∑ Price statements / 3), and health importance/interest as the expression (∑ Health statements / 8). In addition, the dependent variable “sales”, the two independent variables “recipe stimulus” and “price discount”, as well as the moderator of both had to be computed as binary variables.

4.2 Sample

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female respondents, who had 31 different nationalities, representing appropriately the intended diversity in the sample. Regarding the country of origin (nationality) more than half of the sample size was Spanish (54.4%), followed by German (17.7%), and then French, Mexican, Argentinian, and Dutch (3.3%, 2.8%, 2.3%, and 2.3% respectively). The other nationalities had percentages below 2%. The education level was generally high with 74% of respondents holding a university degree, 24.7% a high school diploma, and only 1.4% less than high school diploma. In terms of BMI the mean value was 22.5 reaching an overall healthy sample in which 82% had healthy values (<25), 16% were overweight (values between 25 and 30), and 2% were obese (>30).

Table 2. Descriptive Statistics

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Figure 2. Grocery shopping frequency distribution

4.3 Binary Logistic Regression

As priory introduced in part 3.3 “plan of analysis” the three different hypothesis will be tested using a binary logistic regression model. On a first step, the proposed ten control variables, that in case of having a significant effect on the dependent variable would need to be included in the final model, will be tested. On a second step, assumptions of the model as well as the main model will be tested. A final model based on these two

steps and its results will be presented.

4.3.1 Control Variables

Ten different control variables were selected to test their possible significance, explaining certain variation of the dependent variable as explained in section 3.2.4 “control variables”. In case of a significant effect, the control variable should be included in the final model as a covariate. Commonly, covariates are used to remove extraneous variation from the dependent variable by adjusting its mean value (Malhotra, 2009).The following model to test the variables is proposed and estimated, where pboth represents

the probability that both products (tomato and cucumber) were selected during the online grocery shopping experiment and αi are the parameters to be estimated.

log 𝑒 ( 𝑝𝑏𝑜𝑡ℎ

1 − 𝑝𝑏𝑜𝑡ℎ) = α0 + α1xGENDER + α2x𝐴𝐺𝐸 + α3x𝐸𝐷𝑈𝐶𝐴𝑇𝐼𝑂𝑁 + + α4x𝐶𝑂𝑈𝑁𝑇𝑅𝑌𝑂𝑅𝐼𝐺𝐼𝑁 + α₅x_{𝐺𝑅𝑂𝐶𝐸𝑅𝑌𝐹𝑅𝐸}+ α₆x_{𝐻𝐸𝐴𝐿𝑇𝐻}+ α₇x_{𝑃𝑅𝐼𝐶𝐸𝑆𝐸𝑁𝑆}+ α₈x_{𝐶𝑂𝑂𝐾𝐼𝑁𝐺𝐹𝑅𝐸}

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Results from the estimated model are shown in table 3, which shows insignificant effects for all variables as all p values are above 0.05. Thus, any of the control variables will be included as covariates in the final logistic model.

Table 3. Logistic regression model including control variables

4.3.2 Assumptions of Logistic Regression

Before running the final logistic regression in order to create a valid final model to test the hypothesis of the conceptual model, it is required to see if the assumptions for this kind of regression are met. Although logistic regression does not require many of the classical principles of linear regression as linearity of relationships between dependent and independent variables, normality of the error distribution or homoscedasticity of the errors, other assumptions apply and have to be considered (Park, 2013).

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(Park, 2013). In order to meet this assumption, control variables were checked in advance in order to see if they improve the model. A final assumption is related with the fact that observations need to be independent and the model should have little multicollinearity. Although SPSS does not have an option to test this assumption in logistic regression, it can be done by running a linear regression analysis in order to check tolerance and VIF values (Field, 2009). Appendix D shows that tolerance values are fine as they are not less than 0.1 as suggested by Menard in 1995 (Field, 2009) and VIF values are also fine as they are lower than 10 as suggested by Myers in 1990 (Field, 2009).

4.3.3 Final model

The final model includes the dependent variable, the independent variable recipe stimulus, the independent variable price promotion, and the moderator variable recipe stimulus*price promotion. It has been estimated as follows:

log 𝑒 (

𝑝𝑏𝑜𝑡ℎ

1 − 𝑝𝑏𝑜𝑡ℎ

) = α

+ α

x

+ α

x

+ α

x

where:

pboth represents the probability that a case belongs to the selection of both products and

αi are the parameters to be estimated. XRECIPE is a binary dummy variable, which equals 1 if the recipe stimulus is present and 0 if it is absent. XPRICEPROM is a binary dummy variable, which equals 1 if the price promotion is present and 0 if it is absent. XRECIPE*PRICEPROM is the interaction term of both independent variables that equals 1 if both elements / conditions are present and 0 if they are absent. Table 4 shows the regression results of this model.

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The final model shows no significant effect for any of the variables as the significant values are higher than the confidence interval .05 (.63, .152, and .132). In addition, the low R2 _{coefficients show that the variation of the dependent variable is not explained by} the independent variables. Therefore, regarding the three initial hypothesis (H1, H2, and H3) we cannot accept them based on these first results and state that the different conditions affect produce sales positively.

However, the output on SPSS does also show additional information as “the variables not in the equation” (table 5), a score test that predicts the significance of the variables in the so called “null model” in which no predictors are included and only the intercept. Looking at significance values, it is possible to see that the variable Recipe Stimulus is significant with a p-value of .033 (<.05) and therefore needs to contrasted more in detail as the variable might explain certain variation of the dependent variable and hint interesting significant results.

Table 5. Logistic regression output “Variables not in the Equation”

As table 5 shows a significant effect, a binary regression model only including the independent variable recipe stimulus was run in order to see the strength and direction of the effect (table 6). The table shows that the variable as well as the constant are significant and the value of the odds ratio for the Exp (B) is 1.801. This positive value can be interpreted as it is 1.801 times more likely that both products are chosen when the recipe stimulus is present. Thus, the recipe stimulus affects positively the produce sales and therefore increases the healthiness in the shopping basket (hypothesis 1 would be then accepted).

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V CONCLUSIONS

This final conclusion part will firstly summarize and discuss the results from the prior analysis in order to give conclusive results, which will be compared taking the theoretical framework into consideration. Secondly, a final conclusion section explaining the academic and managerial contributions of the study, followed by a final third part with limitations and future research recommendations will be provided.

5.1 Discussion

The aim of this research study is to determine and find new effective point-of-purchase interventions, whose objective is to increase the purchases or sales of healthy food products as in the case of fruits and vegetables (produce category). Glanz, Bader, & Iyer (2012) proposed four possible different strategies, which can be combined in order to develop effective interventions. Nevertheless, the mixed results and limited effectiveness of two of the four strategies, information and availability strategies, which have been mostly implemented, give an indication regarding the other two strategies, promotional and price strategies, as possible effective solutions and alternatives to the current scenario. In addition, as the ineffectiveness might be attributable to the fact that information and availability strategies are not able to attract, influence and convince consumers in an effective manner due to the low presence of rationality and cognitive processes during the decision-making processes, makes these two strategies interesting from a research point of view as their influence is mainly extra-rational following a behavioral learning approach.

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seems to be significant and have a positive effect if it is examined alone. Figure 3 does also show a positive scenario for this strategy by comparing the amount of participants who selected both products in the different conditions in comparison to the control condition in which no strategy was implemented. The selection percentage of both products was higher in the conditions in which the recipe condition was present (4.6% higher using promotional strategy and 10.2% higher combining a promotional and a price strategy). This result corroborates the fact that promotional strategies can lead to positive effects and are promising strategies as supported by literature (Finkelstein, Ruhm, & Kosa, 2005; Glanz, Bader, & Iyer, 2012; Payne, Niculescu, Just, & Kelly, 2014; Seymour, Yaroch, Serdula, Blanck, & Kahn, 2004).

Figure 3. % Selection of category 1 of produce sales (both products) per experimental condition in comparison to control condition

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ranging from 10% to 50%, in a physical setting, and over longer time periods (from 3 months to 12 months). The lack of these aspects in the study might have led to an opposite unexpected effect of this strategy. Respondents might have not been able to get as much as attracted by the mere and general idea of a price discount as in a real setting exact percentages and specific prices are present and have a more objective character.

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Figure 4. % Selection of category 1 of produce sales (both products) per experimental condition comparing respondents with BMI < 25 and BMI > 25.

5.2 Conclusion

The high complexity of the global overweight and obesity epidemic, which has increasingly spread throughout the entire world bringing negative and alarming consequences for the health system due to related health risks as well as other social and economic consequences (McCormick & Stone, 2006; Tomer, 2011), has pressured governments towards the vital need to understand the phenomenon and find effective solutions. In a current obesogenic environment in which overconsumption and unhealthy food is highly promoted, point-of-purchase interventions have been identified as main tools to turn around this situation representing an excellent opportunity. Nevertheless, the mixed results of information and availability strategies for interventions, which have been the main strategic focus, make other strategies as in the case of promotional or price strategies interesting from a research point of view in order to determine if they are efficient prospective solutions for the alarming health burden.

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Table 7. Final hypothesis overview and results

Academic contribution

This research paper is a contribution to the current academic field of point-of-purchase interventions related with healthy food as it combines two strategies, which have not been thoroughly discussed and addressed. Although the study has certain limitations, which will be later discussed, there is evidence of a positive effect of the recipe stimulus and thus of the efficacy of promotional / advertising strategies as a sales increase of the promoted products took place. Although there was not significant evidence of the positive effect of price promotions and the combination of both strategies, the fact that the effect of these strategies was noticeable stronger for the overweight and obese part of the respondents sheds some light regarding for which target group these interventions might be more effective. In addition, the recipe stimulus was also stronger for the unhealthy sample part. Therefore, it is possible to say that this study contributed to the current literature hinting the potential positive effect of these strategies at the point-of-purchase and in particular for an obese and overweight target group. Both strategies should be investigated more in depth in order to understand the most efficient implementation in order to change the environment and promote healthy products and combat obesity.

Managerial contribution

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paper should be seen as a valuable input for the development and implementation of point-of-purchase activities. On one side, managers should be aware that although information and availability strategies are relevant as consumers should be able to gather easily information regarding the benefits of a healthy diet and the products should also be made easily available, it is important to bear in mind that promotion/advertising and price strategies must be incorporated as an essential key part of their marketing plan. In order to be able to address a diverse group of consumers, as the way in which consumers process and understand the environment is complex and does not follow homogenous patterns, from conscious to unconscious, a combination of strategies should be taken into account. Thanks to a higher efficacy level of their activities, companies will not only benefit from higher sales volumes but will also comply with the required engagement and cooperation that governments and the retail sector currently demands leading to a win-win situation.

5.3 Limitations and Future Research Recommendations

The results of the research study should not be presumed without taking into account several limitations, which might have affected the extent to which it is possible to generalize the results. These limitations can be classified into three groups: the scope of the research, the manipulation of the different conditions, and the setting in which the study took place.

The first limitation is regarding the scope, which is directly related with the sample size used for the study and its analysis. Although the sample size did reach a number above 200 participants, which is meant to be the minimum size for this kind of research (Malhotra, 2009), a bigger sample size could have given more precise results of the effects of the two implemented strategies. In addition, as these two strategies are part of an intervention that aims to influence not only the general population but in particular a population with obesity and overweight, the low amount of obese and overweight people in the sample that was n=37, should also be taken into consideration as an important limitation. The high percentage of Spanish respondents (more than half) can also be seen as a limitation as the sample is not equally distributed in terms of nationalities.

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desirability of the elements within the recipe condition could have impacted participants’ decisions. Although common products in the market as tomatoes and cucumbers as well as a common recipe as in the case of the salad were used for this condition, it is uncertain if the fact of not liking the products or the recipe could have affected the sales. On the other side the more abstract element of the price discounts, as no exact prices and discounts were specified, could have led to misleading effects. Respondents could have interpreted the discounts in different ways as no exact values were provided.

The third limitation is the setting used to test the different conditions. Firstly, the conditions were tested in an online environment, whose design aimed to represent an online website of a supermarket, but it was not identical. Consequently, respondents knew that it was a survey and not a real retailer website, which might have led to a different behavior. Secondly, some respondents participated via their mobile phones that displayed the survey differently, in comparison to a full screen as in the case of a computer, as not all products of the category were shown at the same time and it was more complicated and confusing. In addition, the fact that half of the respondents were Spanish, a country in which the penetration of online grocery shopping is low representing only 1% of total grocery sales (El País, 2017), might have influenced negatively the study due to the fact that respondents are less accustomed to an online setting.

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than in the case of a controlled setting as it was in the study. The conditions should be tested in different supermarkets and countries to reach a higher validity and reliability. In case that the experiment is tested in an online setting, the design should be more similar to a real online platform setting in order to avoid possible bias.

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