How to facilitate healthy product decisions?
The influence of Product Sorting and Product Filtering on the healthiness of consumers’decisions
Jasmijn Kluivingh
University of Groningen
2 THE INFLUENCE OF PRODUCT SORTING AND PRODUCT FILTERING ON THE
HEALTHINESS OF PRODUCT DECISIONS
University of Groningen
Faculty of Economics and Business
Marketing Management
Master Thesis
JASMIJN KLUIVINGH
Gedempte Zuiderdiep 51B1, Groningen
+31622500717
Email: jasmijnkluivingh@hotmail.com
Student number: S2946637
Supervisor: Prof. dr. ir. K. van Ittersum
Second supervisor: D. Olk (Msc.)
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Abstract
Up to this time, one of the world’s biggest problems is the rising obesity rates. This study examines two online functionalities which can facilitate healthy product decisions, and subsequently improves the healthiness of the society. In the (online) supermarket context, consumers nowadays face an enormous information overload daily, which causes consumers to fail in understanding the information properly. To facilitate this process and help consumers to choose healthy, a new national front-of-pack nutrition label has been chosen by the Dutch government: the Nutri-Score. However, an overload of choices remains a problem and still stands in the way of healthy choices. To help consumers narrowing down the enormous list of choices they face, websites provide simple decision aids like Product Sorting and Product Filtering. Combining these decision aids with the new Nutri-Score and provide Product Sorting and Product Filtering based on the Nutri-Score levels is expected to have a positive influence on the healthiness of consumers’ decisions. To test whether their effect enhances together, the moderating effect of Product Filtering on the relation between Product Sorting and Decision Healthiness has been researched. A two-way ANOVA was conducted to test the hypothesis. Both Product Sorting and Product Filtering turned out to have an insignificant effect on Decision Healthiness and no significant moderating effect was found. Gender was added as a Covariate, but only a significant direct effect of Gender was found. To extend this analysis, the variable General Health Interest was included in the first model. A significant direct effect of General Health Interest was found.
Keywords: choice overload, product sorting, product filtering, Nutri-Score, healthiness, consumer choice behavior, supermarket
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Table of Content
1. Introduction ... 5 2. Theoretical Framework ... 8 2.1 Information Overload ... 8 2.2 Nutrition labels ... 9 2.2.1 Nutri-Score ... 10 2.2.2 Decision Healthiness ... 11 2.3 Choice overload ... 12 2.4 Product Sorting ... 13 2.5 Product filtering ... 15 3. Methodology... 17 3.1 Research design ... 17 3.2 Sample ... 19 3.3 Procedure ... 20 3.4 Analysis ... 20 4. Results ... 21 4.1 Data control ... 21 4.2 Testing Assumptions ... 214.3 Two-way ANOVA with interaction ... 22
4.4 Two-way ANCOVA with interaction controlling for Gender ... 23
4.5 Two-way ANOVA with interaction split up per product category ... 24
5. Extra analysis of General Health Interest ... 26
5.1 Two-way ANOVA with interaction ... 27
5.2 Two-way ANOVA with interaction split up per category ... 28
6. Discussion ... 31
6.1 Product Sorting and Product Filtering ... 31
6.2 Interaction effect of Product Sorting and Product Filtering ... 32
6.3 General Health Interest ... 33
7. Implications ... 34
8. Limitations and recommendations for future research ... 35
9. Conclusion ... 36
10. References ... 38
11. Appendices ... 43
11.1 Appendix 1 ... 43
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1. Introduction
Food, sometimes our best friend, but sometimes our biggest enemy. One of the biggest problems among human society is their bodyweight. Since 1980, people are getting more and more heavy, which leads us to a population where one-third is classified as overweight or obese (Global Burden of Disease Study, 2015). This number is even more horrific in the Netherlands, where half of the people above 18 years old were overweight or obese in 2019 (Volksgezondheidzorg, 2020). According to the World Health Organization (2017), overweight and obesity are the cause of death of 2.8 million people every year. Thereby, it is a big risk factor in several chronic diseases such as diabetes and several types of cancer (GBD obesity collaborators, 2017). According to WHO (2016) several parties do play a vital role in the prevention of obesity (e.g. governments and the private sector). They need to enable the consumer to make informed choices while purchasing food (WHO, 2004).
The supermarket plays an important role in food purchasing (Glanz & Yaroch, 2004). WHO (2003) already points out the that providing nutrition information through nutrition labels on the products’ packaging is of high importance in order to make an informed choice. However, this seems not enough. Studies show that consumers, even though they are interested and motivated in choosing healthy good, fall short in comprehending the nutrition information on the back of the packaging (Graham, Orguin, Visschers, 2012). Besides, the amount of information also plays a role. The supermarket is a complex area, where a lot of information is available at the same time. Excessive information can overwhelm consumers and prevent effective processing, which results in more weak-informed decisions (Wansink, 2003; Hasanzade, Osburg & Toporowski, 2018). So, an overload of information together with not understanding the information properly results in consumers falling short in the capability to make an informed choice, even though they would want to.
Nutri-6 Score (Chantal & Hercberg, 2017). In 2017 it was announced as the official front-of-pack nutrition label for France. Next to France, Germany, Belgium, and Spain also the Dutch national institute for health and environment (Rijksinstituut voor Volksgezondheid en Milieu, RIVM) decided that the Nutri-Score will be the new official FOP-label (RIVM, 2019). Several studies have proven the effectiveness of the Nutri-Score on the awareness of healthier alternatives (Egnell, Ducrot., Touvier, Allès, Hercberg, Kesse-Guyot, Julia, 2018; Chantal & Hercberg, 2017) and subsequently healthier choices (Ducrot et al., 2016; Egnell, Galan, Farpour-Lambert, Talati, Pettigrew, Hercberg, et al., 2020). Unfortunately, even though the effect of the Nuri-Score is clearly significant, it also is quite small (Hagmann and Siegrist, 2020). But even though the effect of the Nutri-Score is still little, it does steer people into making healthier choices, which makes it meaningful to find a way to enhance this positive effect.
As said before, the supermarket plays a major role in food purchasing of consumers (Glanz & Yaroch, 2004). As of recent years, the amount of people purchasing their groceries online has been increasing, particularly in the Netherlands. One-third of the consumers in the Netherlands bought their groceries online in 2019 (Statista, 2020). Research has shown that online fewer impulse purchases are made (Pitts, Blitstein, NG, Gustafson & Niculescu, 2018) and subsequently a lower number of unhealthy products that typically satisfy short-term benefits are chosen (Huyghe, Verstraeten, Geuens and Van Kerckhove, 2017). This indicates the relevance of doing research regarding facilitating health decisions online.
7 a decision support tool for consumers (Cai & Xu, 2008; Quaschning, 2013). It changes the display in order to help the consumers find their desired products (Mirhoseini, Leger & and Senecal, 2017). Since the presence of the Nutri-Score itself does steer people into more healthy choices, question rises whether this effect is enhanced through enabling people to sort the product list based on the Nutri-Score levels. In other words, will this improve the healthiness of the decision? This leads to the first research question:
“What is the effect of Product Sorting based on the Nutri-Score levels on the website of a supermarket on consumers’ decision healthiness?”
Another way to reduce the options available for consumers is Product Filtering. Filtering eliminates all other products that do not contain a certain condition (Lurie & Wen, 2014). Filtering mechanisms help consumers prioritize relevant information, thereby offering them a sense of control (Park & Jank, 2013). Products that do not obtain a certain condition will not be displayed, so the consumers will not see it (Lurie & Wen, 2014). If people are able to eliminate certain Nutri-Score levels from the displayed product list, will this lead to people choosing healthier thus improving the healthiness of their decision? This leads to the second research question:
“What is the effect of Product Filtering based on the Nutri-Score levels on the website of a supermarket on consumers’ decision healthiness?”
In this research, I conduct an experiment in the context of an online supermarket in the form of an online questionnaire. Effects that have an impact on the decision healthiness of a consumer in an online supermarket are simulated.
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2. Theoretical Framework
This research paper hypothesizes that the two variables, Product Sorting and Product Filtering, influence the healthiness of consumers’ decisions. In this section the variables will be discussed and based on literature the hypothesis will be formed.
2.1 Information Overload
9 As mentioned in the introduction, one way to shorten the amount of information and limit the complexity of information, thus providing information that is processible for every level of brain capacity, are nutrition labels, especially Front-Of-Package (FOP) labels.
2.2 Nutrition labels
In the ongoing battle against obesity, nutrition labelling on food products has become the prominent policy tool for promoting healthy eating. (Campos, Doxy & Hammond, 2011). Making healthier choices requires consumers to differentiate between healthier and less healthy products (Campos, Doxy & Hammond, 2011). In order to provide the easily understandable nutrition information that people need, nutrition labels were developed in the form of back-of-package labels, which is the standardized nutritional information on the back of any product, and front-of-package labels (Feunekes, Gortemaker, Willems, Lion & Kommer 2008). Unfortunately, back-of-package labeling still is not clear enough because even consumers motivated to select healthy foods fall short in correctly assessing food healthfulness due to barriers in comprehending the nutrition information on the back of the package (Graham, Orguin & Visschers 2012). When choosing between products, consumers have to take into account several nutrients simultaneously, and therefore, they find it difficult to make these comparisons (Campos, Doxy & Hammond, 2011). Cowburn and Stockley (2005) found that consumers particularly find it hard to convert the information given ‘per 100 grams’ to a ‘grams per serving’. Also, consumers with lower education and income levels together with older consumers tend to face trouble in understanding the labels (Cowburn and Stockley, 2005).
10 information contained. Then, interpretive labels can be further categorized based on the degree of information aggregation, namely: interpretive nutrient-specific labels and interpretive summary labels. This distinction has also been made in the paper of Ducrot et al. (2016). Nutrient-specific labels provide information about the nutrient content, focusing on total lipids, fatty acids, sugars, and sodium (Ducrot et al, 2016). It gives an evaluation on the healthfulness of one or more individual nutrients of a product (Ikonen et al, 2019). An example of an interpretive summary label is the Multiple Traffic Light system which uses colour to emphasize whether the level of a particular nutrient is high, medium, or low (Ducrot et al., 2016; Ikonen et al, 2019). Summary labels provide an evaluation of the overall healthiness of a product, which makes it easier to read and understand. An example of a summary label is the five-star health rating system or the five-colour nutrion label (Nutri-Score) (Ducrot et al., 2016; Ikonen et al., 2019).
2.2.1 Nutri-Score
Several studies have researched the effectiveness of different FOP labels in different countries. Ducrot et al. (2016) compared the Multi Traffic Light with Tick and the Nutri-Score and found that the Nutri-Score performed significantly better in improving the nutritional quality of the shopping chart. In addition to that, Egnell et al. (2020) found that the Nutri-Score is the most effective FOP label in informing consumers of the nutritional quality of food products with as little information as possible. Hagmann & Siegrist (2020) compared the Multi Traffic Light and the Nutri-Score in terms of their effect on consumers’ healthiness evaluation of salty snacks. Again, the Nutri-Score scored best, but it depends on how pervasively it is used. The Nutri-Score works best when all products carry it on their package (Hagmann & Siegrist, 2020).
11 Figure 1 – Visualization of the Nutri-Score levels
The studies of Temmerman, Heeremans, Slabbinck & Vermeir (2020) suggest higher purchase intentions for products with a Score of A or B than for products with a Nutri-Score D or E. Next to this, their research provides evidence that the Nutri-Nutri-Score has the potential to boost sales of healthy products. But some have concerns about FOP labels. FOP labels facilitate the identification of healthier options, this does not automatically translate into purchases (Ikonen et al. 2020). Given the evidence of the effectiveness of the Nutri-Score, the Nutri-Score has been chosen by the Dutch National Institute for Health and Environment (RIVM) to be the new official FOP-label (RIVM, 2019). This means that supermarkets and are allowed to show the Nutri-Score everywhere in the supermarket, including online. All in all, the presence of the Nutri-Score will result consumers choosing healthier products (Ducrot et al., 2016; Temmerman et al., 2020), however, this effect will remain small (Ikonen et al., 2020; Hagmann & Siegrist, 2020).
2.2.2 Decision Healthiness
12 2.3 Choice overload
While the Nutri-Score decreases the problem of information overload while shopping for groceries, there will still be a load of different options to choose from (Schwartz, 2004). Older papers argue that customers chances on matching their purchase with their preferences is higher with larger choice sets (Bellenger and Korgaonkar, 1980), or it increases the freedom of choice (Kahn, Moore & Glazer, 1987). However, according to newer findings people tend to suffer from having too many alternatives to choose from (Leyengar & Lepper, 2000). They proved that when people have too many options to consider, they simply will strive to make an end to the ‘choice making nightmare’. This result in choosing something that is merely satisfactory, rather than optimal (Leyengar & Lepper, 2000). This phenomenon is called choice overload. Choice overload is defined as the scenario in which the individual’s cognitive resources are exceeded by the complexity of the decision problem faced by an individual (Chernev, 2015). This differs from information overload, because choice overload accounts for an overload on choice options, where information overload accounts for excessive product information(Denizci Guillet, Mattila & Gao, 2020).
13 (Haynes, 2009). Participants who had a bigger set to choose from found the decisions to be more difficult and frustrating. Haynes (2009) also proved this outcome for people under a time constraint.
The negative effects of choice overload, which mostly refers to not choose anything at all, do not always occur and depend on some necessary preconditions (Scheibehenne, Greifeneder and Todd, 2010). Leyenar & Lepper (2000) found that familiarity with, or prior preferences for the items in the choice set causes people to rely merely on selecting something that matches their own preferences. This is in line with the research of Mogilner et al (2008) which argues that negative effects of choice overload occur only when people are relatively less familiar with the choice domain. Thereby, according to the experiment of Le Lec, Lumeau & Tarroux (2016) individuals who face a small choice set, prefer familiar items less frequently than those who face a large choice set. This is an important finding if applied to the supermarket context. When people are doing groceries, and have too many options to consider, they are more likely to choose something that they are familiar with, rather than a healthier and less familiar option for example.
2.4 Product Sorting
On the website of a supermarket, every single product available is shown to the consumer, and causes choice overload for most consumers (Schwartz, 2004). The common way of displaying these products is within a product list, which is a number of products displayed sequentially in a web page (Diehl & Zauberman, 2005). These product lists are commonly used to show the product catalog or when consumers search for something (Cai &Xu, 2008). It is popular to manipulate the common product list beforehand, which gives a company the opportunity to put their most popular items on top (Cai & Xu, 2008). When choosing between products or searching for products, consumers also have the ability to manipulate how the product list is sorted themselves, through a simple decision aid such as sorting (Lurie & Wen, 2014). In this way, sorting acts as a decision support tool for consumers. It changes information display in order to help consumers find their desired products (Mirhoseini, Leger & and Senecal, 2017).
14 price or quality. Diehl, Kornish & Lynch (2003) has demonstrated consumers get more price sensitive when options are sorted based on quality. Further to this, Suk, Lee & Lichtenstein (2012) investigate the influence of price order on consumers’ choice. Sorting options makes the evaluation of attribute levels easier. For example best-to-worst orders, it is clear for everyone that the attribute level in first place is the best attribute level. Since consumers tend to use the first seen options as reference points for their decision making, a descending product list based on price causes consumers to be more likely to choose higher-priced (higher perceived quality) options (Suk, Lee & Lichtenstein, 2012). This phenomenon is also described by Cai & Xu (2008), who suggested that if the product list was previously sorted based on quality in a descending order, the higher quality products dominate the choice. This quality sorting method places higher priced products higher in the list due to correlation between price and quality. Furthermore, this sorting effect is stronger for hard-to-evaluate attributes than for easy-to-evaluate ones (Quasing, 2013).
Sharkey (2009) indicates the importance of consumers to manipulate the product list they see themselves. According to their paper, they expect the consideration set to be of a better quality when consumers sort the product list compared to an automatic sorted product list because the data presented will be better suiting the consumers’ specific preferences. Mirhoseini, Leger & Senecal (2017) go further into the manipulation of the product list by consumers’ themselves. According to them, a sorting tool helps consumers to narrow down their considerations set because it helps by determining the relative utility of alternatives and contributes to the minimization of consumers’ mental workload. Consumers face a trade-off while improving their decision quality between maximizing accuracy and saving on cognitive effort. For low-value products like groceries, people tend to choose for minimizing the cognitive load, which raises the importance for a sorting tool. Their research shows that a product sorting tool will therefore only decrease the cognitive load for making a decision when the tool matches the consumers’ goal (Mirhoseini, Leger & Senecal, 2017). Thus, if a consumers’ goal is to find the healthiest products a sorting tool based on healthiness will facilitate the decision-making.
15 product list sorted based on the Nutri-Scores from best to worse, will result in consumers using the highest Nutri-Scores as a reference point. Furthermore, consumers tend to focus on minimizing cognitive load while doing groceries, product sorting will help doing so, thus consumers can focus more on decision accuracy, for example choosing the healthiest product. This, together with the effect of the Nutri-Score itself, is expected to lead to consumers choosing healthier products. Based on this the first hypothesis is defined as:
Hypothesis 1: The availability of a Product Sorting option (vs. not available) based on Nutri-Scores positively influences Decision Healthiness.
2.5 Product filtering
16 control, it enhances the consumers’ confidence which subsequently leads to better decisions. (Denizci, Guillet Mattila & Gao, 2020). Above that, the consumer limits themselves to only the healthier products, this should result in making a healthier choice. This leads to the second hypothesis:
Hypothesis 2: The availability of a Product Filtering option (vs. not available) based on the Nutri-Scores positively influences Decision Healthiness.
Literature assumes a positive effect of both a sorting option and a filtering option. In addition to this, it is also suggested that their effect enhances when they are both available (Lurie & Wen, 2014). Based on this, the following final hypothesis was formed:
Hypothesis 3: The positive effect of a sorting option based on the Nutri-Score on the Decision Healthiness will increase when there is also a filtering option (vs no filtering option).
The conceptual model is shown in figure 1.
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3. Methodology
The relationship between three variables is investigated in this research. The two independent variables ‘Product Sorting’ and ‘Product Filtering’ are expected to have a positive effect on the dependent variable ‘Decision Healthiness’.In this chapter the research design will be discussed, and an explanation of the sample, procedure and analysis used will be given.
3.1 Research design
This research contains an experiment regarding the purchase process of a consumer while ordering groceries online. This has been done on the platform Qualtrics with an online questionnaire (Appendix 1). This experiment contains four different conditions based on the availability of two factors to which the participants were randomly assigned to. The factors are: Product Sorting (available vs. not available) and Product Filtering (available vs. not available). Table 1 shows an overview of the four conditions with their specifications.
Product Sorting/Product Filtering
Not available Available
Not available Control condition Product Filter Only
Available Product Sorting Only Product Sorting and Product
Filtering combined
Table 1 - Explanation of conditions
18 from an unevenly distributed Nutri-Score levels, thus, 6 products of each Nutri-Score level have been used. This equals a number of 30 product options for each product category.
The product categories are shown in a random sequence to the participants, this to prevent from bias resulting from the sequence in which the categories are shown. The product options within the product categories are also shown in a random order to prevent from bias resulting from the order in which the products options are presented. Furthermore, the products are shown in 3 columns of 10 products which is the most realistic representation of how products are presented in real online supermarkets (see e.g. www.ah.nl). For the visualization of the products to be the most realistic, pictures from the Dutch supermarket Albert Heijn were used together with their actual price and their corresponding Nutri-Score. This to guarantee external validity. An example of this is shown in figure 2. An overview of all products used for all categories are displayed in Appendix 2.
Figure 2 – Product visualization
The dependent variable Decision Healthiness is operationalized by the Nutri-Score level of the chosen products. To calculate the Nutri-Score of each product, an online Nutri-Score calculator was used ( https://nutriscore.colruytgroup.com/colruytgroup/nl/nutri-score-calculator/). The nutritional values of the products were filled in, and then the Nutri-Score of the product is automatically computed. With all the Score per product the average Nutri-Score of the chosen products by the participants can be calculated after the experiment is completed. The Products with a Nutri-Score A were assigned a value of 1. This increases systematically to a value of 5 for products with a Nutri-Score E.
19 the product list. Figure 3 shows an example of the condition with both Product Sorting and Product Filtering. In the other two conditions only one of the two decision aids is displayed to the participant. The product sorting option gives participants the opportunity to sort the product list based on the Nutri-Levels in a descending and an ascending order (from A to E and from E to A). The product filtering option makes it possible for participants to select one or more Nutri-Score levels which they only desire to see. Levels that are not selected are hidden from the product list.
Figure 3 – Example of sorting and filter option
Furthermore, the participants were not completely informed about the true purpose of the study. In the introduction of the online survey the purpose of the study was very briefly explained by saying that the study was about customer purchasing behavior. In this way, participants were not fully aware of the purpose, which prevents them from behaving differently and to motivate realistic behavior and results. Moreover, participants were told that they could win a price in order to extra motivate the participants to fill in the survey and to motivate normal shopping behavior. The price that participants could win was one of the products that were shown in the survey.
3.2 Sample
20 on high school and 72(23.6%) are MBO educated. All the participants who filled in the survey, were living in the Netherlands at that moment.
3.3 Procedure
Data was collected via an online questionnaire on the platform Qualtrics. The link for joining the online experiment was spread online through several social media platforms like WhatsApp, Instagram, Facebook, and LinkedIn. Convenience sampling and snowball sampling are used to reach as many people as possible. The participants were sent a message with an anonymous link to participate in the online questionnaire on Qualtrics. At first, it was made clear to the participants that their participation was completely voluntary, and the obtained data was available for use this study only. Thereafter, participants needed to read the introduction, which included a brief description of the purpose of the study with a little background information about the Nutri-score labeling. In this way, each participant had knowledge about how to interpret the Nutri-Score label. Then, participants were asked to choose between products in 7 different product categories with the imagination they were doing their actual groceries. For each product category, a total of 30 different products were shown to the participant. The 30 products were displayed in 3 columns of 10 products, each with their corresponding Nutri-Score and price. After choosing between the products, participants needed to answer questions regarding their general health interest and their price interest. The scale was based on the multi-item scale of Roininen et al (1999). At last, the participants were asked some demographic questions including gender, age, education level and residence. At the end of the survey, participants got the option to fill in their email address in order to win one of the products of the survey. This was done to motivate participants to answer truthfully.
3.4 Analysis
21 the Nutri-Score consists of 5 levels. Products with Nutri-Score A were coded as 1, products with Nutri-Score B were coded as 2 and so on. Thus, the lower the score, the healthier the product choice.
4. Results
In this chapter, the results of the analysis to test the hypothesis will be discussed.
4.1 Data control
In total, three hundred and five respondents filled in the survey. Before the analysis, the data was checked for outliers and cleaned. It took three people more than two hours to fill in the survey. Because this survey was not designed to take more than 2 hours, these three participants were removed from the dataset. This did not influence the results. Furthermore, the dataset was checked on missing data, but no data was missing. So, further data reduction based on incomplete data was not necessary. All in all, testing the hypotheses was done with the remaining three hundred and two respondents.
4.2 Testing Assumptions
22 4 conditions are insignificant which means that the 0-hypothesis of a normal distribution is not rejected. The p-values of the different conditions are as follows: Control: p=0.074; Product Sorting Only: p=0.2; Product Filtering Only: p=0.200; Product Sorting and Product Filtering combined: p=0.200. This means that this requirement is met.
The Decision Healthiness of the participants per condition is calculated by taking the average of all Nutri-Scores of the seven product categories. Participants in the Product Filter Only condition did choose the healthiest products (M=1.96, SD=0.6143). The lower the score, the healthier the product choice. Further details of the Decision Healthiness of participants per condition is displayed in table 2.
Condition Control Product
Sorting Only
Product Filter Only
Product Sorting and Product Filter
combined
N 76 73 77 76
Mean 2.0545 1.9824 1.9610 1.9981
SD 0.6979 0.5957 0.6143 0.6103
Table 2 – Decision Healthiness per condition
4.3 Two-way ANOVA with interaction
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Variables F
Product Sorting 0.057 Product Filtering 0.282 Product Sorting * Product Filtering 0.557
*significant at p<0.1 **significant at p<0.05 ***significant at p<0.01 Table 3 – Results of the two-way ANOVA with interaction
Figure 4 – Visualization of the results of the two-way ANOVA with interaction
4.4 Two-way ANCOVA with interaction controlling for Gender
24 effect of Product Sorting and Product Filtering on Decision Healthiness even when there has been controlled for the effect of gender. Meanwhile, there is a significant main effect of the control variable Gender on the dependent variable Decision Healthiness, F(1,213)=5.319, p=0.022. The f-values of the two-way ANCOVA are presented in table 4. As visualized in figure 5, women significantly chose healthier products (M=1.93, SD=0.6350) than men (M=2.12, SD=0.6671).
Variables F
Gender 5.319** Product Sorting 0.023 Product Filtering 0.392 Product Sorting * Product Filtering 0.693
*significant at p<0.1 **significant at p<0.05 ***significant at p<0.01 Table 4 – Results of the two-way ANCOVA with interaction
Figure 5 – Visualization of the results of direct effect of Gender
4.5 Two-way ANOVA with interaction split up per product category
25 between the product categories. Therefore, a two-way ANOVA with interaction was conducted for every product category apart. There is found a marginal significant direct effect of Product Sorting on Decision Healthiness of the category Bakery, F(1,298)=3.510, p=0.062. Participants with Product Sorting available chose healthier products (M=1.59, SD=1.087) compared to when Product Sorting was not available (M=1.85, SD=1.277). Visualization of this result is shown in figure 6. Thus, for the product category Bakery the availability of Product Sorting does significantly influences Decision Healthiness. The f-values of all the separate two-way ANOVA analyses per product category are shown in table 5.
Product category / variables Product Sorting Product Filtering Product Sorting *Product filtering Categories together 0.057 0.282 0.557 Desserts 0.338 0.003 0.223 Drinks 0.120 0.080 0.795 Sandwich filling 0.017 1.681 0.746 Bakery 3.510* 1.675 0.492 Ready-to-eat Meals 0.082 0.129 0.183 Breakfast cereals 0.000 0.009 2.404 Cookies 0.617 0.740 1.519 *significant at p<0.1 **significant at p<0.05 ***significant at p<0.01 Table 5 – F-values of the Two-way ANOVA analyses
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5. Extra analysis of General Health Interest
Choice of food is a complicated function with a lot of different influences (Chen, 2015). The effects of the previous analysis are insignificant, which could be due to motivations and interests of consumers in which they may differ. Schwartz (2004) already points out differences in choice behavior based on differences in character traits. It is therefore interesting to include health characteristics of consumers in the previous model to see if the modest results are due to these differences in characteristics. People are more likely to choose healthy food when they possess high levels of knowledge of nutrition and high levels of calorie consciousness (Chen, 2015). General Health Interest is a widely defined and complex concept. Shin & Mattila (2019) refer to general health interest as the readiness of consumers to take healthy actions. This reflects a consumer’s readiness to do something for his or her own health. Thereby, General Health Interest influences the willingness to pay for healthy foods (Bower et al., 2003), is associated with higher fruit and vegetable intake (Wardle & Steptoe, 2003), and positively influences the intentions to buy and willingness to pay of healthy foods (Bower, Saadat & Witten, 2003). In addition to this, a high General Health Interest enables consumers to actively engage in behaviors to maintain or improve their health (Shin & Mattila, 2019). It is therefore expected that General Health Interest will positively affect the Decision Healthiness of the consumers. Above that, the research of Shin & Mattila (2019) suggests that a higher degree of an individuals’ General Health Interest increases the positive effect of attitude towards high nutritious foods on the willingness to use high nutritious foods (Shin & Mattila, 2019). Since General Health Interest has a positive effect on the willingness to use nutrition labels (Drichoutis, Lazaridis & Nayga, 2006), it is expected that it also will influence the effect of Product Sorting and Product Filtering in this study. This is because it is based on the new nutrition label ‘Nutri-Score’. Moreover, General Health Interests should act as a moderator on the effect of the availability of Product Sorting and Product Filtering on Decision Healthiness.
27 5.1 Two-way ANOVA with interaction
To analyze the effects of General Health Interest, a 2 (Product Sorting: Available vs. not available) x 2 (Product Filtering: Available vs. not available) x 2 (General Health Interest: High vs. low) was conducted. The direct effect of General Health Interest on Decision Healthiness is found to be significant, F(1,292=74.884; p=0.000). This means that the variable General Health Interest positively influences the variable Decision Healthiness. People with a high General Health Interest chose significantly healthier (M=1.74, SD=0.282) than people with a low General Health Interest (M=2.30, SD=0.6132). These results are visualized in figure 7.
Furthermore, the direct effect of Product Sorting on Decision Healthiness is still insignificant, F(1,292)=2.037, p=0.155. The significance increased in comparison with the first model since the p-value is lower. Also the direct effect of Product Filtering on Decision Healthiness is still found to be insignificant, F(1,292)=1.726; p=0.190).
Figure 7 – Visualization of the results of the direct effect of General Health Interest
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Variables F
Product Sorting 2.037
Product Filtering 1.726
General Health Interest (GHI) 74.884** Product Sorting * Product Filtering 0.000
Product Sorting * GHI 0.053
Product Filtering* GHI 0.033
Product Sorting*Product Filtering* GHI 1.596
*significant at p<0.1 **significant at p<0.05 ***significant at p<0.01 Table 6 – Results of a two-way ANOVA with interaction
5.2 Two-way ANOVA with interaction split up per category
29 Category/ Variables Product Sorting Product Filtering General Health Interest Product Sorting *Product filtering Product Sorting *GHI Product Filtering *GHI Product Sorting*Product filter*GHI Desserts 0.009 0.148 23.458*** 0.010 0.454 0.075 0.595 Drinks 0.044 0.224 37.428*** 0.044 3.533* 0.446 0.024 Sandwich filling 0.212 2.578* 10.455*** 1.302 0.293 0.243 1.378 Bakery 7.167** 2.604* 31.229*** 0.016 0.824 0.079 0.182 Ready-to-eat Meals 0.820 0.000 11.070*** 0.003 0.000 0.814 1.773 Breakfast cereals 0.320 0.093 22.104*** 1.335 0.054 0.182 0.746 Cookies 0.091 0.340 16.532*** 0.552 0.026 0.058 0.192 *significant at p<0.1 **significant at p<0.05 ***significant at p<0.01 Table 7 – Results of the two-way ANOVA with interaction slit up per product category
30 Figure 9 – Visualization of the direct effect of Product Filtering
At last, for the product category Drinks a marginal significant interaction effect of Product Sorting and General Health Interest was found, F(1,294 = 3.533, p=0.061. This means that the effectiveness of the availability of Product Sorting depends on the level of General Health Interest. People with a high General Health Interest choose healthier products when Product Sorting is available (M=1.73, SD=1.063) compared to when Product Sorting is not available (M=2.03, SD=1.181). Furthermore, a healthier choice in the product category Drinks was made by people with a high General Health Interest (M=1.90, SD=1.138) compared to people with a low General Health Interest (M=2.77, SD=1.131). Figure 10 shows a visualization of this interaction effect.
31
6. Discussion
In this chapter, the results of the analysis in the chapter above will be discussed. The goal of this paper was to answer the following research questions: (1) “What is the effect of Product Sorting based on the Nutri-Score levels on the website of a supermarket on consumers’ decision healthiness?” and (2) “What is the effect of Product Filtering based on the Nutri-Score levels on the website of a supermarket on consumers’ decision healthiness?”.
6.1 Product Sorting and Product Filtering
To answer the first research question, the effect of Product Sorting on Decision Healthiness has been investigated. The direct effect of product Sorting on Decision Healthiness turned out to be insignificant. This means that there is no statistical evidence found to support hypothesis 1. Only for the product category Bakery there exists a marginal significant effect of Product Sorting on the Decision Healthiness. This could suggest that the availability of Product Sorting only affects certain product categories. It might be a possibility that the effect of Product Sorting may occur on other product categories, since there were only seven product categories included in this study. It could be that the perceived healthiness of the product category influences the results. Maybe for healthy product categories people are more open to improve their choice to a healthier one whereas for more unhealthy product categories like cookies consumers just want to pick what they like most, since it is already relative unhealthy. It could also be that the hedonic vs. utilitarian shopping motivations per category might play a role in influencing the outcomes. Since hedonic shopping motivations are inspired by the desire for pleasure, joy, and fun (Arnold & Reynolds, 2003), more unhealthy categories like cookies and desserts could be perceived as hedonic products. Maybe for those categories a decision supporting tool based on health will not be effective, because the consumer will only choose the product that will satisfy the hedonically motivated appetite (Arnold & Reynolds, 2003).
32 not meet those expectations of a positive effect. An explanation could be that the effectiveness of Product Sorting depends on the goal of the consumer (Mirhoseini, Leger & Senecal, 2017). This means that only if a consumers’ goal is to find the healthiest product, only then a sorting function based on health will decrease the cognitive load for the consumers and lead to a better decision (Mirhoseini, Leger & Senecal, 2017). If not every participant has this health-goal, the Sorting option will not be sufficient.
To answer the second research question the independent variable Product Filtering was tested on consumers’ Decision Healthiness. A positive influence of Product Filtering was expected because literature suggests that Product Filtering decreases the perceived overload for consumers (Chen et al, 2009), thus consumers being more able to make a well-informed choice. Above that, a positive influence of Product Sorting was also expected because a product filter mechanism is increasing consumers’ confidence which leads to better decisions (Denizci et al, 2020). It turned out that the relationship between Product Filtering and Decision Healthiness was insignificant. Again, no statistical evidence was found to support second hypothesis.
Food choices are an area in which consistent behavioral differences have been observed (Wardle, Haase, Steptoe, Nillapun, Jonqutiwes & Bellisie, 2004). Reason for an insignificant direct effect of Product Sorting and Product Filtering could be explained by the research of Lurie & Wen (2014) where they suggest that the effectiveness of simple decision aids like Product Sorting and Product Filtering depend on the choice conflict and whether the availability of the Sorting and Filter option is limited or unlimited. Since this experiment there was unlimited access to use the Sorting and Filter options and the choice conflict in the experiment was different than for normal grocery shopping, this could have influenced the outcomes.
6.2 Interaction effect of Product Sorting and Product Filtering
33 Familiarity, or prior preferences for the choice options causes people to rely merely on selecting something that matches their own preferences (Leyenar & Lepper, 2000; Mogilner et al., 2008). Furthermore, Mogilner et al, (2008) argues that the negative effects of choice overload only occur when consumers are relatively less familiar with the choice domain. The domain of this research, which is groceries, is something that people do on a weekly or even a daily basis. Consumers have a lot of previous experience in this field and are highly familiar with the product categories that were used in the experiment. This could explain the insignificance of the effect of Product Sorting and Product Filtering. This means that consumers chose the products based on merely selecting something they are familiar with and matches their prior experience.
Next to this, gender has been added to the model as a covariate. Results show an insignificant interaction effect of Product Filtering and Product Sorting on the Decision Healthiness whilst controlling for gender. There was a significant direct effect found for the variable Gender and Decision Healthiness, meaning that the average healthiness of the decision was significantly different across the genders. This seems to be a logical result according to previous literature that suggests that women are more likely to attach greater importance to healthy eating. Furthermore, the differences in food choices are accountable to higher concern about weight control of women and their stronger beliefs in healthy eating (Wardle, Haase, Steptoe, Nillapun, Jonqutiwes & Bellisie, 2004). The health beliefs and motivation in weight control may explain even up to 50% of the gender differences in food choice (Westenhoefer, 2004; Wardle et al, 2004; Manippa, Padulo, van der Laan, Brancucci; 2017).
6.3 General Health Interest
As said above, differences in health beliefs explain differences in food choice (Westenhoefer, 2004). Interests and motivations of consumers influences their behavior. More healthy choices are made by consumers with a relative high nutritional knowledge (Chen, 2015). Because of this, an additional analysis of General Health Interest has been conducted. Consumers with high general health interest are more likely to actively engage in behaviors to maintain or improve their health (Shin & Mattila, 2019). This means that the higher the General Health Interest of people, the more they are willing to take actions to be healthy, thus buying healthy products.
34 category Bakery is found. In addition to this, for the product category Sandwich Filling also a marginal significant effect of Product Filtering on Decision Healthiness is found. This suggest that for this product category, Filtering helps for consumers to make a healthier choice. As mentioned earlier, since there were only seven product categories included in this study, it could be that Product Sorting and Product Filtering only have an impact on certain product categories and maybe on categories that are not included in this study. It could be that these two categories are perceived as healthier, in which consumers with a high General Health Interest would be more interested in. It could also be that since people with a relatively high General Health Interest are more willing to make use of decision aids based on nutrition labels (Drichoutis, Lazaridis & Nayga, 2006), and thus are more willing to use the Product Sorting and Product Filtering based on the Nutri-Score. Since this effect is rather small, this requires further investigation.
At last, a marginal significant moderating effect of Product Sorting and General Health Interest on Decision Healthiness was found for the product category Drinks. This means that, in line with previous research, that the effectiveness of the availability of Product Sorting indeed depends on the level of the General Health Interest of consumers. People with a relative high General Health Interest make more healthy choices for Drinks when there is Product Sorting available.
7. Implications
The aim of this research is to test if Product Sorting and Product Filtering based on health facilitate healthy decisions. The results of this paper are relevant for different parties, for example the government and supermarkets. In the introduction has been mentioned that the already horrific obesity rates are still growing, which is a huge threat among human society. It is therefore important for the government to investigate new opportunities to facilitate healthy behavior which is in favor of obtaining a healthier society. Results of this research indicate a significant effect for certain product categories, which means that the use of Product Sorting and Product Filtering only helps consumers to make healthier choices in those domains. Not only are results on what works important, also options that do not work are helpful in the search for opportunities to make the society healthier.
35 evidence exists of the effectiveness of the Nutri-Score in helping consumers to make more healthy choices. Thereby, results of this research can also contribute to the decision whether to implement the new Nutri-Score as a new Sort and Filter functionality online. Even though findings of this study are quite small and only appear for some product categories, the effect is still positive. In other words, there is some evidence that the Sort and Filter functions based on the Nutri-Score indeed help people to choose healthier products. And in this ongoing battle against obesity and the striving for a healthier society, every little bit counts. Implementing this functionality based on the Nutri-Score will be one little step forward in helping people become more healthy.
8. Limitations and recommendations for future research
Regardless of the fact that this research was executed with thought, it contains several limitations. At first, although the study was designed to mimic a realistic shopping experience as much as possible through giving a high number of product options, it is still not as much as a real online supermarket. Therefore, the product categories were displayed through an online survey on Qualtrics, which is not as realistic to a real supermarkets’ website. Further analysis of the effects of Product Sorting and Product Filtering on an existing supermarket website is recommended.
Second, a limitation of this study comprises the motivation of the participants. In the beginning of the survey participants were asked to make decisions based on how they would normally choose between products while doing their groceries. It could be that people did not do this, and solely chose products that they were familiar with or based their choice on something that they were in the mood for at that moment. In addition to this, in the discussion is mentioned that differences in the results for the different categories could be due to different shopping motivations per category (Arnold & Reynolds, 2003). A recommendation for future research is to consider the hedonic and utilitarian shopping motivations and add this into the study.
36 can be due to the snowball sampling method that has been used. This is not generalizable to the Dutch society. Future research should consider this while conducting a research in this area and collect a more representative sample.
Also, due to a lack of technology, if participants did or did not use the Product Sorting option or the Product Filter option when available was not specifically known. So if the differences are completely accountable to the availability of the Product Sorting and Product Filter functions is not sure. Recommendation for future research is conduct a research where the usage and effects of the Sorting and Filtering option can be analyzed. Another recommendation for future research could be to investigate the differences in the effect of the Sorting and Filtering function according to their salience. For this research, a simple design was chosen for Product Sorting and Product Filtering (see figure 4). Maybe these functions need to stand out a bit more to get the consumers’ attention, and subsequently to be used. It could be a possibility if their design is more obvious, or they use different colours, that their effectiveness increases.
9. Conclusion
This study did not find statistical evidence to prove an influence of Product Sorting and Product Filtering on the healthiness of the decisions of the consumers. Through an online questionnaire among three hundred and five Dutch participants evidence of the effects of Product Sorting and Product Filtering was tried to be found. A two-way ANOVA with interaction was conducted to research the effects. The direct effects of Product Sorting and Product filtering together with the interaction effect were found to be insignificant. This means that there is no statistical evidence to prove the hypotheses. In other words, Product Sorting and Product Filtering does not influence the healthiness of the decisions of consumers and the relationship between Product Sorting and Decision Healthiness does not depend on the availability of Product Filtering. Thus, consumers do not make healthier choices when either Product Sorting is available or Product Filtering is available or when both Product Filtering and Product Sorting are available, compared to when they are not available.
For the product category Bakery a marginal significant direct effect of Product Sorting on Decision Healthiness was found. This means that for this product category, people make healthier choices when there is Product Sorting available.
38
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43
11. Appendices
11.1 Appendix 1
Nutri-Score filter/sorting
Start of Block: INTRODUCTION
Welkom!
Wat fijn dat je mij wilt helpen met afstuderen!
Voor mijn scriptie doe ik onderzoek naar het online koopgedrag van consumenten. Het invullen van de lijst zal slechts 5 minuutjes kosten. De vragenlijst vul je volledig anoniem in. Daarbij zal de data die wordt verkregen door middel van deze vragenlijst uitsluitend worden gebruikt voor dit onderzoek en niet met derden worden gedeeld.
WIN! Wil je kans maken om één van de getoonde producten in de vragenlijst GRATIS thuisgestuurd te krijgen? Laat dan aan het einde jouw e-mailadres achter!!
Alvast bedankt voor jouw tijd!
Page Break
Tijdens het boodschappen doen moet je tussen heel veel verschillende producten kiezen. Welke neem je mee? De keuze die je maakt kan op verschillende factoren gebaseerd zijn. Met behulp van labels op verpakkingen proberen producenten en supermarkten je een handje te helpen met het maken van die keuze. Je ziet in één oogopslag of iets bijvoorbeeld een gezonde keuze, een duurzame keuze of een goedkope keuze is.
In 2021 wordt de Nutri-Score het nieuwe officiële label in Nederland dat laat zien hoe gezond het product is. Dit label zal je dan zowel in de winkels als online tegenkomen. De score loopt van A (relatief gezondste) naar E (relatief ongezondst), en ziet er als volgt uit:
44 Stel je bij de volgende vragen voor dat je online boodschappen aan het bestellen bent. Er staan 7 dingen op je boodschappenlijstje, namelijk: Drinken, broodbeleg, ontbijtgranen, een koekje, iets van de bakkerij, een kant-en-klare maaltijd en een toetje.
Je krijgt achtereenvolgens deze 7 productcategorieën te zien waaruit je steeds één product moet kiezen. Maak een keuze aan de hand van hoe je normaal ook zou kiezen tussen verschillende producten tijdens het doen van je dagelijkse boodschappen.
WIN! Door jouw e-mailadres achter te laten aan het eind van deze vragenlijst maak je kans één van de getoonde producten GRATIS thuisgestuurd te krijgen!
End of Block: INTRODUCTION
Start of Block: Toetjes (both)
45
End of Block: Toetjes (both)
Start of Block: Drinken (both)
Drinken Kies één product Q7 Timing First Click (1) Last Click (2) Page Submit (3) Click Count (4) Page Break
End of Block: Drinken (both)
Start of Block: Broodbeleg (both)
46
End of Block: Broodbeleg (both)
Start of Block: Bakkerij (both)
Bakkerij Kies één product Q29 Timing First Click (1) Last Click (2) Page Submit (3) Click Count (4) Page Break
End of Block: Bakkerij (both)
Start of Block: Kant-en-klare maaltijden (both)
47
End of Block: Kant-en-klare maaltijden (both)
Start of Block: Ontbijtgranen (both)
Ontbijtgranen Kies één product Q27 Timing First Click (1) Last Click (2) Page Submit (3) Click Count (4) Page Break
End of Block: Ontbijtgranen (both)
Start of Block: Koekjes (both)
Koekjes Koekjes Kies één product Q26 Timing First Click (1) Last Click (2) Page Submit (3) Click Count (4) Page Break
End of Block: Koekjes (both)
Start of Block: General Healt interest
48 In het volgende blok krijg je een aantal algemene stellingen te zien.
Geef aan in hoeverre je het eens of oneens bent met deze stellingen.
Page Break
GHI In hoeverre ben je het eens of oneens met de volgende stellingen? De schaal loopt van 1 (helemaal mee oneens) tot 7 (helemaal mee eens).
Helemaal mee oneens
Neutraal Helemaal mee eens
1 2 3 4 5 6 7
49
End of Block: General Healt interest
Start of Block: Price interest
PI In hoeverre ben je het eens of oneens bent met de stellingen?
De schaal loopt van 1 (helemaal mee oneens) tot 7 (helemaal mee eens). Helemaal mee
oneens
Neutraal Helemaal mee eens
1 2 3 4 5 6 7
Prijs is belangrijk voor mij wanneer ik kies voor een product () Meestal streef ik om de goedkoopste producten te kopen () De prijs van een product is doorslaggevend in de keuze die ik maak ()
Q31 Timing First Click (1) Last Click (2) Page Submit (3) Click Count (4) Page Break
End of Block: Price interest
Start of Block: Demographics
Q36 Bedankt!
51 Leeftijd Wat is je leeftijd?
________________________________________________________________ Q42 Timing First Click (1) Last Click (2) Page Submit (3) Click Count (4) Page Break
Woon Ben je woonachtig in Nederland?
