Designing Sustainable Consumption The effect of environmental nudging and choice architecture in E-Groceries

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Designing Sustainable Consumption

The effect of environmental nudging and choice architecture in E-Groceries

MSc. Communication Science - Persuasive Communication Graduate School of Communication

University of Amsterdam

Author: Andres Aparicio Student ID: 11906359 Supervisor:Dr. E.S. (Eline) Smit

Date: 01/07/2022

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Abstract 2

1. Introduction 3

2. Theoretical framework 5

2.1. Sustainable behaviour 5

2.2. Sustainable by design 6

2.2.1. Environmental nudging 7

2.2.2. Choice architecture 8

2.2.3. Interaction Effect 9

2.3. Past sustainable behaviour 11

2.4. The role of response efficacy beliefs 12

3. Methodology 14

3.1. Design 14

3.2. Sampling 15

3.3. Procedures 16

3.3. Material & stimuli 17

3.4. Measures 18

4. Results 20

4.1. Manipulation check 20

4.2. Main effect 21

4.3. Mediation analysis 24

5. Discussion 26

6. Limitations 28

References 30

Appendix 39

Appendix 1 39

Appendix 2 40

Appendix 3 42

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Abstract

Online grocery shopping has seen continuous growth over the last few years.

E-groceries shopping has, therefore, become a key environmental opportunity in the food industry to achieve a more sustainable environment. Given its increasing importance and potential to contribute to a sustainable environment, this study tests in a 3x2 between-subject experimental design (N = 287) the effect of sustainable design strategies in E-grocery apps to examine the effect on consumers’ sustainable shopping behaviour. This experiment compares positive (ECO-based) and negative (CO2-based) and no environmental nudging, to identify differences in the user’s sustainable shopping behaviour. Furthermore, choice architecture is implemented into the e-grocery prototype apps, to examine its impact on the user’s shopping behaviour in combination with environmental nudging. Finally, response efficacy belief is analysed as a mediator variable. Results showed that both environmental nudging and choice architecture increase sustainable shopping behaviour in consumers. The interaction of both variables did, however, not show significant results. Additionally, no mediation effect was found in the model.

Keywords: sustainable design, E-grocery, environmental nudging, choice architecture, reponse efficacy beliefs, sustainable shopping

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1. Introduction

Environmental concerns such as the rise of global temperatures are firmly attached to human food consumption, with food accounting for 10-30% of a household’s carbon footprint (Center for Sustainable Systems, 2021). Consequently, substantial changes in food shopping behaviour are required to achieve a more sustainable environment. E-grocery shopping presents a key environmental opportunity to avoid further increases in the current food system. Not only could a higher use rate of e-commerce services, for example, help reduce emissions (Hardi et al., 2019), but it could also improve access to healthy and sustainable products (Alaimo et al., 2020).

Over the past few years, grocery shopping through mobile applications has seen continuous growth (Blanke et al., 2021), which has triggered an increase in research on the topic (Michie et al., 2008; Hallez et al., 2021; De Bauw et al., 2022). There is an extensive amount of previous literature regarding various techniques (e.g. nudging, labelling, pricing, and so forth) in E-grocery that aim to alter the users’ behaviour in order to purchase more sustainable products (Martín et al., 2019; de Magalhães, 2021; Neumayr & Moosauer, 2021).

Even though the effect of positive environmental nudging (e.g. eco-labels) on sustainable food choices in online environments has been previously demonstrated (Hallez et al., 2021;

Meijers et al., 2022), limited research has been conducted comparing the effect of positive and negative environmental nudging. Furthermore, very little research has been carried out in which the user is primed through the negative environmental nudging of his/her choices, that is, to alter an e-grocery environment by giving product scores based on the carbon footprint (CO2) emissions they cause. Additionally, the study by Hallez et al. (2021) compared nutritional & ecological labels and showed a positive influence on young adults to compose more sustainable meals. This study aims to compare positive (ECO-score) vs. negative (CO2-score) vs. no nudging, in order to further contribute to the literature in this field.

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Furthermore, as prior literature shows, by e.g. varying the presentation order of choice alternatives, the order attributes, and the selection of defaults one can influence the choice of the decision-maker (Thorndike, 2020). Therefore, choice architecture is implemented into the e-grocery apps, in order to examine whether it can impact the user’s sustainable shopping behaviour in combination with environmental nudging. Choice architecture presents an innovative approach to the literature in persuasive design. Since scholars suggest that choice architecture positively affects sustainable shopping behaviour by facilitating the identification of sustainable products (Panzone et al., 2021), it is the intention of this study to further contribute to the literature by comparing the effect of choice architecture with both positive and negative environmental nudging approaches.

Finally, the user’s response efficacy belief is included in the model in the form of a mediator to determine its influence on sustainable shopping behaviour. Specifically, this paper investigates if environmental nudging and choice architecture influence the user’s sustainable shopping behaviour through response efficacy beliefs (i.e. the belief that one is able to contribute to the solution of a problem; Bandura, 1977). Since prior scholarship suggests that response efficacy beliefs are an important predictor of behaviour change (Wou et al., 2018), this study aims to alter an E-grocery app interface, with the intention to increase users’ response efficacy beliefs and, consequently, provoke environmentally friendly shopping behaviour.

This paper intends to make a theoretical contribution, based on empirical research, regarding the impact of environmental nudging and choice architecture on online shopping behaviour. In addition, the variable response efficacy beliefs is included in the model in order to identify its mediating effect on online shopping behaviour, respectively. This results in the following research question: To what extent do environmental nudging approaches (positive vs. negative vs. absent) and choice architecture (present vs. absent) in E-grocery apps

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influence sustainable shopping behaviour in the user? Is this relationship mediated by response efficacy beliefs?

2. Theoretical framework

2.1. Sustainable behaviour

Sustainable behaviour, defined as any activity that is aimed at the conservation of the natural environment or natural resources (Tapia-Fonllem et al., 2013), has been thoroughly studied in different contexts ranging from activism all the way to online shopping. In order to explain sustainable behaviour, prior literature has focused on the underlying consumer values, attitudes and behavioural intentions towards environmentally friendly products (Michie et al., 2008; Lilley, 2009; Coskun et al., 2015). Similar to other human behaviours, environmentally friendly food choices are roughly driven by one’s motivations and ability (Andersson et al., 2006). According to Anable et al. (2006), there is often a gap between individuals' concerns about sustainability issues and the shopping behaviour that is consequently triggered. This gap has been documented in the literature as an attitude-behaviour gap (Vermeir & Verbeke, 2006), and explains how individuals exhibit positive attitudes but fail to execute these attitudes by engaging in responsible behaviours. Moreover, other studies have used the theory of planned behaviour (TPB) (Ajzen, 1985), a theory that states that (sustainable) behaviour is triggered via a combination of a consumer’s attitude, subjective norms, and perceived behavioural control. Such theories can make a significant contribution to designing a solution for assessing and improving the behaviour in question. The effect of such interventions has been previously demonstrated, with researchers manipulating product displays in the supermarket to increase sustainable consumption in the consumer (van Giesen & Leenheer, 2018). However, the practical application in online environments remains to be further discovered. Even if people have a positive attitude toward sustainability, they will not

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necessarily transfer this attitude into sustainable behaviours unless external triggers are put into action (Vlaeminck et al., 2014). It does not, therefore, need to be inadequate knowledge about sustainability issues which causes the problem, but rather how sustainability-related information is communicated to the consumer before making a specific choice.

2.2. Sustainable by design

As mentioned above, further research is necessary to design solutions that assess and improve the behaviours in question, especially in online environments. To address the practical problem of designing online interfaces, the field of User Experience (UX) has emerged (Blanke et al., 2021). This field specializes in investigating the interaction of people with products, systems, and services (ISO9241, 2019). UX design can shape the development of products which directly impacts the environment, reducing negative environmental impacts by purposefully shaping behaviour towards more sustainable practices (Lilley, 2008).

Some of the most studied sustainable design strategies to promote environmentally-friendly behaviour include customization: enhancing the user’s perceived active control over the mobile app environment and thereby helping users form autonomous motivation (Bol et al., 2019); tailoring: individual modifications of the environment to trigger user-specific behaviours (Fogg, 2002) and self‐monitoring: design features allowing users to track their own behaviour (Lockton et al., 2018). Nevertheless, this research study proposes two innovative approaches to sustainable design in order to investigate their impact on the user’s behaviour: environmental nudging & choice architecture.

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2.2.1. Environmental nudging

Environmental nudging, defined as “modifying the choice environment to help users make sustainable choices without limiting the number of choices” (Ferrari et al., 2019), is further examined in this paper. In this study, an environmental nudging approach is taken, based on the European standardization of labelling system. This system introduces ECO-score labels into supermarket products with the intention to reflect the products’

environmental footprints (PEF) (European Commission, 2019). According to the experiment by Vlaeminck et al., (2014) in which they investigated food labelling in Belgian supermarkets, ECO-scores by themselves result in more pro-environmental food choices.

Additionally, Weber (2021) showed that ECO-rankings of supermarket products resulted in less decision uncertainty leading to more sustainable product choices. According to the dual processing theory, stating that people make behavioural decisions applying different types of reasoning, namely fast and automatic reasoning (also referred to as system 1) or reflective and slow (also referred to as system 2) reasoning (Blom et al., 2021), digital nudges aim to target automatic processes presenting themselves as quite effective in environments in which individuals are facing limited time & knowledge such as online supermarkets (Marchiori et al., 2017).

Additionally, this paper intends to compare the effect of positive environmental nudging (ECO-scores) against a negative nudging condition in which the same products are given CO2-scores, reflecting the products’ “carbon footprint”. As emphasized by Thøgersen et al. (2010), there is a need for a better understanding of consumer response to environmental labels, and more specific eco-labelling initiatives based on the negative environmental impact specific products have. Literature on the topic suggests that providing information on the carbon footprint of individual food products increases the amount of

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pro-environmental products in the basket of goods purchased (Vanclay et al. 2011; Muller, Lacroix, and Ruffieux 2019). As to the comparison between the effect of ECO & CO2 scoring systems as environmental nudging approaches, previous literature suggests that consumers are less influenced by claims of sustainability when compared to messages addressing social conscience, such as those informing about the negative environmental impact. (Laroche et al., 2001; Rudell, 2006). This paper aims to compare both approaches in order to make a theoretical contribution about the differences in the effect of both strategies on the sustainable shopping behaviour in the user. According to the aforementioned information, the following hypothesis is established:

H1: Positive environmental nudging (ECO-based) in E-grocery apps results in more

sustainable shopping behaviour when compared to no-nudging, but in less sustainable shopping behaviour when compared to negative environmental nudging (CO2-based).

2.2.2. Choice architecture

An additional novelty is the study of the performance of this environmental nudging intervention in the presence of a second motivator: choice architecture. Choice architecture refers to a family of tools that changes the decisions consumers make by altering the way choices are presented to the user, without modifying the assortment of the store or the prices of the goods (Johnson et al., 2012). It reflects the fact that there are various ways to present a choice to the decision-maker and what is chosen often depends upon how the choice is presented (Thaler & Sunstein, 2008). In this article, two innovative choice architectural elements are studied. Firstly, a page is introduced that categorizes all products according to their environmental impact. Additionally, within each category products are prioritized (displayed first) based on their ECO & CO2 scores. A more exact description of the stimulus

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is available in Methodology. Previous literature has shown that by re-designing the choice architecture of online shopping interfaces, one is able to alter consumers’ food choices in predictable ways, including the trigger of sustainable shopping behaviours (Arno & Thomas, 2016; Hollands et al., 2019). A similar study by Panzone et al. (2021) demonstrated that changes to the architecture of a choice can be used to facilitate large-scale transitions to low-carbon grocery shopping. The theoretical explanation for this effect is that choice architecture can reduce externalities, such as product prices, in the decision-making process.

Thus, a stronger focus is set on the sustainability-related factors in their choice (Carlosson et al., 2019). Additionally, further research on the topic has demonstrated that choice architecture may also have a habit formation potential (White, Habib, & Hardisty 2019), causing users to form a habit in their choice of the most sustainable category resulting in increased exposure to more environmentally friendly products. Based on the aforementioned information, the following hypothesis is established:

H2: Present choice architecture in E-grocery apps results in more sustainable shopping behaviour when compared to absent choice architecture.

2.2.3. Interaction Effect

As mentioned before, there is a gap between the attitude about a topic and actual behaviour, described in the literature as an attitude-behaviour gap (Vermeir & Verbeke, 2006). Most behavioural interventions are based on providing information to the user (Thorndike, 2020), under the assumption that knowledge itself will trigger an intended sustainable behaviour.

Nevertheless, preceding research on the topic has shown that solely increasing awareness about sustainability to stimulate matching behaviours has shown to have a limited effect on behaviour (Spaargaren, 2011; Asif et al., 2018). This occurs due to the fact that ‘situational

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context’ is a key variable that may influence this gap (Carrington et al., 2010). That is, behavioural interventions solely relying on the products’ information (environmental nudging) can result in insignificant changes in sustainable behaviour due to interference or

influence of situational context variables. Hence, this research paper aims to investigate the interaction effect between environmental nudging and choice architecture, with the intention to understand whether a stronger stimulus based on the combination of sustainable design strategies will encourage users to further disregard the influence of situational context and evoke more sustainable purchase behaviours. According to previous literature, environmental nudging directly affects sustainable shopping behaviours, as it makes an inference in the

users’ unconscious and automatic reasoning (also referred to as system 1 in dual processing theory) (Marchiori et al., 2017). On the other hand, previous research has shown that choice architecture can result in a more long-lasting effect on the user's pro-environmental

behaviour, as it presents more habit-formation qualities (White, Habib, & Hardisty 2019). It is therefore hypothesized that when combined, a stronger effect on sustainable shopping behaviour occurs. Based on the abovementioned information, the following hypothesis is established:

H3: Present choice architecture combined with environmental nudging (either

positive or negative) in E-grocery apps results in more sustainable shopping behaviour when compared to either choice architecture or environmental nudging (either positive or negative) alone.

2.3. Past sustainable behaviour

Prior literature suggests the importance of understanding situational context variables to close the attitude-behaviour gap, in order to translate consumer attitudes into actual

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pro-environmental behaviour (Carrington et al., 2010). One of the most important predictors of pro-environmental behaviour is past sustainable behaviour, that is, the previous consumers’ efforts (or lack thereof) to reduce their environmental impacts through grocery shopping and consumption (Phang et al., 2021). Past sustainable behaviour in this context takes into account the extent to which consumers behave in a sustainable way. It is believed that understanding the role of past sustainable behaviour is an important precursor to any shift relating to consumption and the environment.

It has been demonstrated that although consumers express strong concern about the environmental impacts of their behaviours, their actions do not always reflect their concerns (Holdsworth, 2003). Habitual and routine behaviour contributes to the attitude–behaviour gap (Bhamra et al. 2011). Nevertheless, a lack of habit in behaviour can also result in behavioural barriers that prevent pro-environmental practices in the consumers. As explained by Verplanken (2012), behavioural models such as the Theory of Planned Behaviour do not make full justice to the nature of behaviour by dismissing the notion that most behaviours are repeated over and over again. Prior literature shows robust findings on how past behaviour is a strong predictor of future behaviour (Ajzen, 2002), especially when attitudes are not well-formed. Therefore this paper posits that past sustainable behaviour will result in more sustainable shopping behaviour, due to the spillover effect of past environmentally-related behaviours on future actions. Consequently, this paper analyzes the role of past sustainable behaviour on sustainable shopping behaviour as a covariate in the model.

2.4. The role of response efficacy beliefs

Previous literature explains that even though consumers have environmental concerns, they often find it difficult to make more pro-environmental choices (Keizer, & Perlaviciute,

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2014). This is partially explained due to certain barriers consumers experience such as financial reasons, time or physical effort. Additionally, pro-environmental behaviours do not have an immediate impact on the environment, but rather take a long time to be effective.

This causes lower response efficacy beliefs in consumers, which explains why even though consumers have environmental concerns, they do not always act according to these. Response efficacy beliefs are defined as the belief that one is able to contribute to the solution of a problem (Bandura, 1977), therefore, this article focuses on the user’s belief that he/she is able to contribute to a sustainable environment.

This article posits that environmental nudging in E-grocery apps has the ability to increase the user’s response efficacy beliefs. Namely, by exposing themselves to modifications in the choice environment based on the individual product’s environmental scores, users will recognize and acknowledge the environmental impact of their choice resulting in enhanced response efficacy beliefs. Previous research shows that environmental nudging has a positive effect on response efficacy beliefs by providing consumers with the opportunity to objectively consider environmental information in their food choices (De Bauw et al., 2022). The nutritional environmental impact of products is per definition a credence attribute, as it cannot be directly observed. However, environmental nudging is seen as an opportunity to account for that information (Weber, 2021). Additionally, this research paper postulates the positive effect of environmental choice architecture in E-grocery apps on users’ response efficacy beliefs. By facilitating pro-environmental choice environments, choice architectural elements can facilitate the users’ ability to process relevant information regarding their food choices (Johnson et al., 2012). This ability is usually considered an inherent characteristic of the individual, linked to knowledge and skills (Petty et al., 2009;

Lähteenmäki, 2013). Additionally, choice architecture offers a more enabling grocery environment that counters the users' limitations (due to information overload and limited

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time) to consider the environmental impact of their choices, consequently increasing response efficacy beliefs.

Numerous empirical studies have demonstrated the importance of response efficacy beliefs to incite pro-environmental behaviours (Barr & Gilg, 2007; Lauren et al., 2016).

Furthermore, this spillover effect between response efficacy beliefs and sustainable behaviour is explained in various theories based on behavioural change such as Social Cognitive Theory and Protection Motivation Theory (Bandura, 1977; Rogers, 1975). As response efficacy beliefs play a key role in instigating pro-environmental behavioural change, this paper investigates whether sustainable design strategies (environmental nudging & choice architecture) subsequently affect sustainable shopping behaviour in the user.

Based on previous research showing a positive effect of both sustainable design strategies on response efficacy on environmentally friendly behaviours in general (Kang et al., 2013; De Bauw, 2022), it is expected that this increase in response efficacy subsequently leads to more environmentally friendly attitudes and intentions, therefore, the following hypothesis is proposed:

H4: The effect of sustainable design strategies (environmental nudging & choice architecture) on sustainable shopping behaviour is mediated via response efficacy beliefs.

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Figure 1: Conceptual Model - IV: environmental nudging & choice architecture; Mediator:

response efficacy beliefs; Covariate: past sustainable behaviour; DV: sustainable shopping behaviour.

3. Methodology 3.1. Design

In order to test the abovementioned hypotheses, this study employed a 3 (Environmental nudging: positive vs negative vs absent) x 2 (Choice architecture: present vs absent) between-subject factorial design (see Figure 2). This study used sustainable shopping behaviour as a dependent variable, response efficacy beliefs as a mediator & past sustainable behaviour as a covariate. The experiment was embedded in a self-reported web-based questionnaire, and the participants were randomly assigned to one of six experimental conditions. Ethical approval was obtained from the University of Amsterdam’s Ethical Review Board (2022-PC-15233).

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Environmental Nudging

Choice Architecture

Absent Present

Absent Group 1: Control condition Group 2: Choice architecture Positive Group 3: Positive nudging Group 4: Positive nudging x

choice architecture

Negative Group 5: Negative nudging Group 6: Negative nudging x choice architecture

Figure 2. 3x2 between-subject factorial design: Environmental nudging (3 levels) x choice architecture (2 levels)

3.2. Sampling

The recruitment strategy followed in this study was the convenience and snowball strategy. The online experiment was distributed via various social networks such as LinkedIn, Facebook, Instagram & Whatsapp. Furthermore, the experiment was available on www.lab.uva.nl to attract the participation of students from the University of Amsterdam in exchange for research credits. In order to be eligible for this study, participants had to be at least 18 years old.

A total of 295 participants were recruited for this experiment. 91 participations came from www.lab.uva.nl, the remaining participants were recruited via social media and word of mouth. After excluding participants for incomplete questionnaire items, the final sample comprised 287 participants, including a total of 112 (39.1%) males, 172 (59.9%) females & 3 (0.1%) third gender or undisclosed participants. Years of age among the final participants ranged from 18 to 66 years (M = 24.83, SD = 7.84). As to the level of education in the sample, 30% had completed high school, 20.3% had completed some education in university but did not obtain a degree, 33.3% graduated with a Bachelor's degree and 15.9% graduated with a Master's degree.

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3.3. Procedures

The research study was set up on the survey software Qualtrics. Starting off, participants completed a series of questions regarding their demographic characteristics, such as age, gender and level of education. Next, they answered questions about their past sustainable behaviour. Participants were then randomly assigned to one of the six experimental conditions. In each condition, participants entered a different simulation of an E-grocery mobile app. They did so by scanning a QR with their phones, that would redirect them to the URL with the simulation. The URL was also available directly in the survey for participants that did not have access to a smartphone. All subjects had to complete the same shopping list made out of five different products: 1x bananas (6 pcs), 1x avocado (1 pc), 1x cheese (1 pc), 1x onions (500 g) & 1 x bread (1 pc). Within each product category, participants could choose the product according to their preference. Once the simulation was completed and all five items were added to the shopping basket, participants closed the simulation and returned to complete the remaining questions, continuing with three questions regarding the simulation as a manipulation check. The instructions to do so were clearly indicated before starting the simulation. Finally, the participants were requested to answer questions related to their response efficacy beliefs and intended sustainable shopping behaviour. A more detailed description of the stimuli and the survey items can be found in the following section.

3.3. Material & stimuli

The stimulus material of this study was based on the two independent variables, environmental nudging and choice architecture. In order to avoid limitations in the validity of the results due to prior familiarity, it was decided to create a fictitious E-grocery app in the

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form of a prototype. The independent variable levels (positive/negative environmental nudging & choice architecture ) were combined in the prototypes, resulting in a total of six different prototypes based on the same fictitious E-grocery app (see Appendix 1-3).

Mobile application - In order to test the effect of the independent variable, a prototype

of an E-grocery app was designed and developed. Following the suggestions of previous research investigating the effect of interactive websites on consumer behaviour (Hallez et al., 2021), it was decided to create a real, clickable prototype that participants could browse through. The intention was, that the experience should resemble as much as possible a real-life E-grocery shopping interaction. The prototype took as other E-grocery apps in the Dutch app store using similar interface structures and categorization of products as an example (for example: Albert Heijn, Jumbo, Gorillas, etc.). However, in order to avoid familiarity with the product, the prototype was entirely designed from scratch (see Appendix 1).

Environmental nudging - The environmental nudging manipulation was developed

based on the European labelling system of product score labels reflecting products’

environmental footprints (European Commission, 2019). Following this, products in the positive environmental nudging condition received a green ECO-score label, which indicated their impact on the environment, with a higher score reflecting a more environmentally friendly product. In order to clarify this for the user, all pages in the E-grocery app in this condition included a label with a green background colour including the following copy:

“ECO-Score: Higher ECO-Score results in a more positive impact on the environment.”.

Similarly, products in the negative environmental nudging condition received a red CO2-score label. The scores of these products were inverted compared to the positive condition, as a higher score reflected a less-environmentally friendly product. The same label

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was used, however, red colour was used and the copy said: “CO2-Score: Higher CO2-Score results in a more negative impact on the environment.” (see Appendix 2).

Choice architecture - The choice architecture manipulation was based on the

approach used in the study by Panzone et al., (2021). Firstly, a page was introduced in the prototype that categorized all products according to their environmental impact. This page appeared before displaying the product choices, separating, therefore, the product options into three categories: environmentally-friendly low, environmentally-friendly medium &

environmentally-friendly high. Once the decision regarding the category was made, only the products with scores in that environmentally-friendly category were displayed (see Appendix 3). Additionally, within the product choice page, products were prioritized based on their ECO & CO2 scores, meaning that they were displayed from left to right according to the products’ scores.

3.4. Measures

Sustainable shopping behaviour - The dependent variable sustainable shopping behaviour was measured by asking participants to rate on a 7-point Likert scale (1 = Totally disagree, 7 = Totally agree) the following five statements: (1) “When shopping online, I will

check if products are harmful to the environment.”, (2) “When shopping online, I will choose products that are environmentally friendly.”, (3) “When shopping online, I will pay attention to categories/sections with environmentally friendly products.”, (4) “When shopping online, I will take environmental labels into consideration before buying the products.” & (5) “When shopping online, I will purchase sustainable products.” (α = 0.95, M = 4.56, SD = 1.41).

Response efficacy beliefs - In order to measure response efficacy beliefs, the scale used in the study by Meijers et al (2022) was employed. Participants were asked to rank six statements on a 7-point Likert scale (1 = Totally disagree, 7 = Totally disagree) regarding

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both personal and collective response efficacy. The statements used were: (1) “When I purchase eco-friendly products, I can contribute to solving environmental issues.”, (2) “I can have a positive impact on the environment when I purchase eco-friendly products.”, (3) “By choosing eco-friendly products, I can prevent environmental issues from getting worse.”, (4)

“We can protect the environment if we all choose eco-friendly products.”, (5) “If we would all purchase eco-friendly products, this would help in solving environmental issues.” & (6)

“It helps if everyone chooses eco-friendly products because together we are able to protect the environment.” (α = 0.93, M = 4.77, SD = 1.18).

Past sustainable behaviour - The scale used to measure the participant’s past sustainable behaviour is an adaptation of the sustainable behaviour scale developed by Joshi

& Rahman (2017). The following 4 statements are ranked on a 7-point Likert scale (1 = Totally disagree, 7 = Totally agree): (1) “When shopping, I deliberately check products for

environmentally harmful ingredients.”. (2) “When shopping, I deliberately choose products with environmentally friendly packaging.”, (3) “I prefer to buy sustainable products even if they are more expensive than others.” & (4) “When shopping, I see environmental labels before buying the product.” (α = 0.87, M = 3.69, SD = 1.24).

Manipulation check - In order to examine whether the manipulations in the experiment were perceived by the subjects as intended, participants had to answer three questions on a 7-point Likert scale (1 = Not at all, 7 = Very much). The first two questions, regarding positive and negative environmental nudging, were as follows: (1) “To what extent was the positive impact of your product choices on the environment communicated?” & (2)

“To what extent was the negative impact of your product choices on the environment communicated?”. The third question testing the manipulation of the choice architecture was:

(3) “To what extent were products categorised according to their environmental impact?”.

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4. Results

4.1. Manipulation check

In order to test the manipulation checks, a one-way between-subjects ANOVA was executed using condition as an independent variable and three manipulation checks (perceived communication of positive impact, perceived communication of negative impact &

perceived communication of environmental categorisation) as dependent variables.

Additionally, a Bonferroni post-hoc analysis was executed to identify the diverse effects of each condition on the dependent variables.

Firstly, using perceived communication of positive impact as a dependent variable, a significant effect of conditions at the p < .05 level was found, F (5, 282) = 19.64, p < .001.

Post-hoc comparison using the Bonferroni test indicated that the mean score for positive environmental nudging (ECO) condition (M = 5.28, SD = 1.14) was significantly different than the control condition (M = 2.09, SD = 1.28) and the negative environmental nudging condition (M = 2.78, SD = 1.02). When using the perceived communication of negative impact as a dependent variable, a significant effect was found (F (5, 282) = 30.91, p < .001).

Post-hoc comparison using the Bonferroni test indicated that the mean score for negative environmental nudging (CO2) condition (M = 4.85, SD = 1.73) was significantly different than both the control condition (M = 2.16, SD = 1.32) and the positive environmental nudging (ECO) condition (M = 2.78, SD = 1.02). Finally, the analysis using perceived communication of environmental categorisation as a dependent variable indicated a

significant effect, F (5, 282) = 22.95, p < .001. The post-hoc comparison using the Bonferroni test indicated that the mean score for choice architecture condition (M = 4.92, SD = 1.78) was significantly different than both the control condition (M = 1.97, SD = 1.35).

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Based on the results of the analysis, one can confidently assume a successful experimental manipulation for all conditions.

4.2. Main effect

To analyse the direct effect of sustainable design strategies on sustainable shopping behaviour a two-way analysis of variance (ANOVA) was executed. In the analysis, the variables environmental nudging (absent, postive & negative) and choice architecture (absent &

present) were used. Additionally past sustainable behaviour was used in the form of a covariate and sustainable shopping behaviour as the dependent variable.

Since the treatment groups have sharply unequal sample sizes, the data needed to satisfy the homogeneity of variance assumption. The Levene’s test of equality of variance in the analysis was F (5, 282) = 0.585, p = 0.711. Therefore, the null hypothesis of equal error variances is rejecting, meaning that our data meets the homogeneity of variances assumption.

Firstly, the effect of environmental nudging on sustainable shopping behaviour was found significant, F (1, 282) = 10.88, p < .001. Eta² is 0.095, meaning that 9.5% of the variation in sustainable shopping behaviour can be predicted by environmental nudging.In order to identify the within-group differences of environmental nudging, a Bonferroni post-hoc test was executed. The results indicate that the mean of positive environmental (ECO) nudging (M = 4.41, SD = 1.47) was significantly different than the no nudging condition (M = 3.65, SD = 1.30). The mean of negative environmental (CO2) nudging (M = 4.49, SD = 1.28) was also found to be significantly different when compared to the no nudging condition, however, there was no significant difference when compared to the positive environmental (ECO) nudging.

Secondly, the effect of choice architecture was examined, showing significant results on sustainable shopping behaviour (F (1, 282) = 5.25, p = .023). Eta² for choice architecture

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is 0.023, meaning that 2.3% of the variation in sustainable shopping behaviour can be attributed to environmental nudging. There is, therefore, a significant difference in the mean of present choice architecture (M = 4.25, SD = 1.18) when compared to absent choice architecture (M = 3.66, SD = 1.3).

As to the interaction effect between environmental nudging and choice architecture, no significant effect on sustainable shopping behaviour was found (F (2, 282) = 1.23, p = .426). Eta² for the interaction effect is 0.006, meaning that 0.6% of the variation in sustainable shopping behaviour can be attributed to the interaction between environmental nudging and choice architecture. The mean of positive environmental nudging (ECO) combined with choice architecture was slightly lower (M = 4.95, SD = 1.35) when compared to the mean of negative environmental nudging (CO2) combined with choice architecture (M

= 5.07, SD = 1.42), however, not at a significance level of p < .05.

Finally, past sustainable behaviour was analysed as a covariate in the model. The results indicate a significant effect on sustainable shopping (F (1, 282) = 50.91, p < .001, Eta² = 0,178), indicating that past sustainable behaviour is a significant predictor of sustainable shopping behaviour accounting for 17.8% of the variance in the model. A summary of the results in the model can be found in Table 1.

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Table 2: Results two-way analysis of variance. Independent variables: environmental nudging, choice architecture, past sustainable behaviour. Depende variable: sustainable shopping behaviour

Sum of Squares df Mean Square F p

pastbehaviour 73.04 6 73.04 50.91 < 0.001

envnudg 36.87 1 18.44 12.83 < 0.001

choicearch 6.65 1 6.65 4.63 < 0.001

envnudg*choicearch 2.46 2 1.23 0.96 0.426

pastbehaviour 73.04 1 73.04 50.92 < 0.001

Total 4718.16 282

In conclusion, the results indicate significant effects for all variables with the exception of choice architecture. Therefore, support is found to accept H1, indicatingpositive environmental (ECO) nudging in E-grocery apps results in more sustainable shopping behaviour when compared to no-nudging, but in less sustainable shopping behaviour when compared to negative environmental (CO2) nudging. Regarding choice architecture, the results showed a significant direct effect. As a consequence, we find support to accept H2, stating that present choice architecture in E-grocery apps results in more sustainable shopping behaviour when compared to absent choice architecture. In addition, the interaction effect between environmental nudging and choice did not show significant results on the dependen variable, thus, support is found to reject H3. A graphic representation of the results is to be found in Figure 2.

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Figure 2: Means plot for two-way ANOVA. Independent variables: environmental nudging

& choice architecture. Dependent variable: sustainable shopping behaviour.

4.3. Mediation analysis

In order to test H4, proposing that the effect of sustainable design strategies on sustainable shopping behaviour is mediated via response efficacy beliefs (REB), a moderated mediation analysis using model 7 of the PROCESS Macro plug-in for SPSS was executed.

The model included environmental nudging as an independent variable, choice architecture as a moderator variable, response efficacy beliefs as a mediator variable and sustainable shopping behaviour as the dependent variable.

The index of moderated mediation was not significant, b = -0.19, 95% percentile CI [-0.44, -0.06], providing evidence that there is no moderated mediation in the model.

Furthermore, the first step in the mediation model showed a significant direct effect of environmental nudging on REB, b = 0.5, t (134) = 3.81, 95% CI [0.44; 1.41], p < .001.

However, for absent choice architecture, there is a significant moderation effect from

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environmental nudging on sustainable shopping behaviour via REB, b = 0.3, CI [0.17, 0.5].

Additionally, when looking the direct effect, the analysis indicates a significant positive effect of environmental nudging on sustainable shopping behaviour (b = 0.31, t (137) = 2.98, 95%

CI [0.1; 0.51], p = .003). As to the indirect effect of the independent variable on sustainable

shopping behaviour, it was found that the effect was not signifiant across all three levels of environmental nugding (no nudging: b = 0.28, CI [-0.04, 0.5]; positive nudging: b = 0.12, CI [-0.08, 0.33; negative nudging: b = 0.3, CI [0.17, 0.23]. Regarding the moderation effect, the results showed an insignificant interaction effect between environmental nudging and choice architecture on REB (b = -0.29, t (98) = -1.15.81, 95% CI [-0.68; 0.09], p = .132. R² = 0.01, indicating that the interaction between the independent variable and the moderator accounto for 1% of the variance in REB. Finally, the results indicate significant positive relationship between the moderator REB and sustainable shopping behaviour, b = 0.64, CI [0.51, 0.77]. A summary of the regression results can be found in Table 3.

Overall, there was no mediation found in the model. Therefore, support was found to reject H4 stating that the presence of sustainable design strategies results in stronger response efficacy beliefs and subsequently increases sustainable shopping behaviour.

Table 3: Regression results from the a-path from environmental nudging to REB and for the b-path from REB to sustainable shopping behaviour

Variable Model a-path Model b-path

b SE p b SE p

envnudg 0.5 0.04 <.001

choicearch 0.19 0.16 0.237

envnudg*choice arch

-0.29 0.19 0.132

REB 0.642 0.069 <.001

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5. Discussion

The aim of this study was to examine the impact of environmental nudging (positive

& negative) and choice architecture on sustainable shopping behaviour. In addition, the roles of response efficacy beliefs as a mediator and past sustainable behaviour as a covariate were tested. The experiment led to several key findings with relevant implications for communication theory and designers. In the following, the findings will be discussed.

The analysis outcome of the main effect presents results that are partly in line with this study’s expectations. Firstly, the effect of negative environmental nudging on sustainable shopping behaviour was stronger than the effect of positive environmental nudging. Even though the manipulation of the prototype was almost identical, except for the colour and the framing (ECO vs CO2), significant differences in the effect were found. The results, therefore, confirm the hypothesis in line with previous findings on the topic suggesting that providing customers with information regarding the negative impact (e.g. carbon footprint) of the product indeed promotes pro-environmental behaviour (Muller, Lacroix, & Ruffieux;

2019). In addition, the increasing effect on sustainable shopping with respect to positive environmental nudging confirms the findings by scholars Laroche et al. (2001) stating that consumers are more influenced by messages that address their social conscience than messages with claims of sustainability. Consequently, if negative environmental nudging is a strong predictor of sustainable shopping behaviour, both designers and communication specialists can consider a negative impact approach to further provoke sustainability-related behaviours in consumers.

As hypothesized, the manipulation of the categorisation of products (choice architecture) did suffice to show a significant effect on sustainable shopping behaviour.

Nevertheless, when paired with environmental nudging (either positive or negative), the

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effect did not increase as expected. One can therefore assume, that according to the findings by Carlosson et al., (2019) introducing choice architecture in E-grocery apps can eliminate externalities in the shopping process so that consumers could focus on sustainability-related factors. The elimination of these externalities does, however, seem not to be reinforced by environmental nudging. Another explanation can be based on the past findings by White et al.

(2019), who explained that choice architecture also has a habit formation. This habit-formation can be targeting automatic processes via exposure to an environmental factor.

Further research solely focused on choice architecture is required to further understand its effect on consumer shopping behaviours.

Another important finding is the lack of effect of the interaction between choice architecture and environmental nudging. Even though both variables present significant effects on sustainable shopping behaviour, the interaction of both did not significantly enhance this one. This can be attributed to the fact that the attitude-behaviour gap (Vermeir &

Verbeke, 2006) can be closed by implementing just one effective behavioural intervention. As seen in the analysis, both environmental nudging and choice architecture seem sufficient to do so. The combination with a second intervention becomes, therefore, inconsequential.

Finally, another key finding of this study is the absence of a mediation effect by response efficacy beliefs. Contrary to expectations, response efficacy beliefs did not show a significant mediation effect in the model. However, the results indicated a direct effect on sustainable shopping behaviour, proving its importance to predict sustainable shopping behaviours. Additionally, environmental nudging displayed a significant effect on response efficacy beliefs, which can imply a possible explanation for the effect of environmental nudging on sustainable shopping behaviour. This was not the case with choice architecture, which leaves the question open, of how the presence of choice architecture in E-groceries can persuade consumers to shop more sustainably. Nevertheless, further research is therefore

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required to better understand the role of response efficacy beliefs in relation to design strategies and sustainable shopping behaviours.

6. Limitations

While this experiment provides useful insight into the relation of persuasive design strategies with sustainability-related behaviour, certain limitations need to be addressed. First, this study employed a real, interactive mobile application prototype that could result in a complicated task for participants. It was assumed that all participants, regardless of their age and/or media savviness, knew how to complete a shopping list in an E-grocery app. As explained by Voramontri & Klieb (2019), consumer behaviour in online environments can be highly dependent on the media experience of the consumer. Future research should address this limitation by controlling for media savviness or familiarity with similar products previous to the app interaction.

Furthermore, because the experiment employed a prototype and not a live mobile application, the user features were limited. Participants could not access a shopping basket, so an overview of the products chosen was not available. Even though the buttons to add products were interactive, once the product was selected no further information regarding that decision was available. Future research should make a shopping basket available to the user in order to keep an overview of selected products with checkout processes. That way, the interaction can further resemble a real-life E-grocery experience.

In addition, this study recorded the dependent variable via a self-reported measure.

The lack of immediacy to record sustainable shopping behaviour opened the possibility for the lower direct effect of environmental nudging and choice architecture due to possible interferences. Moreover, the self-reported measure results in a more subjective evaluation by the participants themselves that only indicates a predisposition for the behaviour. Future

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research should measure the dependent variable via the products chosen by each participant.

That way, the effect is immediately recorded, avoiding possible interferences in the recording of variables, therefore, increasing the internal validity of the study.

Finally, pricing was not taken into account in this study. All products, irrelevant of environmental scores had the same price as other products in that category. Nevertheless, various researchers describe pricing as a strong predictor in consumer grocery decision-making (Glanz & Yarock, 2004; Zhang et al., 2018). It was the intention of this study for users to solely focus on the shopping experience, however, the external validity of this study is therefore lowered. Future research should control for the effect in various price ranges to more accurately generalize the results.

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Appendix Appendix 1

E-Grocery Prototype - Control condition

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Appendix 2

Environmental nudging - Positive (left) & negative (right) environmental nudging

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Appendix 3

Choice architecture

Appendix 4

Environmental nudging x choice architecture

Figure

Updating...

References

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