Organic defaults in online‐shopping: Immediate effects but no spillover to similar choices

Kuhn, Sascha; Ihmels, Max; Kutzner, Florian Article — Published Version Organic defaults in online‐shopping: Immediate effects but no spillover to similar choices Journal of Consumer Behaviour Provided in Cooperation with: John Wiley & Sons Suggested Citation: Kuhn, Sascha; Ihmels, Max; Kutzner, Florian (2021) : Organic defaults in onlineshopping: Immediate effects but no spillover to similar choices, Journal of Consumer Behaviour, ISSN 1479-1838, Wiley, Hoboken, NJ, Vol. 20, Iss. 2, pp. 271-287, https://doi.org/10.1002/cb.1850 This Version is available at: https://hdl.handle.net/10419/230217 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ SPECIAL ISSUE ARTICLE Organic defaults in online-shopping: Immediate effects but no spillover to similar choices Sascha Kuhn 1,2 | Max Ihmels 3 | Florian Kutzner 1 1 Social Psychology, Heidelberg University, Heidelberg, Germany 2 Transformation of Economic and Social Systems, German Development Institute, Deutsches Institut für Entwicklungspolitik (DIE), Bonn, Germany 3 Social Cognition and Decision Sciences, University of Tübingen, Tübingen, Germany Correspondence Sascha Kuhn, Department of Psychology, Heidelberg University, Heidelberg, Germany. Email: [email protected] or [email protected] Funding information Deutsche Forschungsgemeinschaft, Grant/ Award Number: KU - 3059/2-1 Abstract Changing defaults—the preselection that becomes effective without active choice—is becoming a prominent policy tool, after having been proven to be effective in areas as varied as retirement savings, organ donation and product customization. Yet, little is known about how default effects spill over to subsequent similar behaviors. In an online shopping scenario, we found standard default effects on the share of organically produced products in the overall selection of products. These effects did not spill over to subsequent active shopping choices. This was true for defaults that were hard and easy to change (Exp. 1, N= 90), for immediate and delayed subsequent choices (Exp. 2, N= 106) and for self-selected defaults (Exp. 3, N= 181). These findings suggest that the reach and scalability of default manipulations in policy making may be limited, but also speak against the possibility for negative spillover. 1|INTRODUCTION In order to address environmental issues, policy makers increasingly rely on behavioral insights to deploy cost-efficient and potentially scalable interventions to encourage pro-environmental behavior (PEB; Abrahamse & Steg, 2013; Barbarossa & De Pelsmacker, 2016; Osbaldiston & Schott, 2012; Prothero et al., 2011). When deciding about interventions, policy makers should ideally know about interventions' immediate and delayed effects on behaviors similar to and different from the target behaviors (Dolan & Galizzi, 2015; Nilsson, Bergquist, & Schultz, 2017; Truelove, Carrico, Weber, Raimi, & Vandenbergh, 2014). The most desirable behavior change interventions would be those that trigger the targeted PEB and “spill over”to subsequent behaviors (Stern, 2011; Thøgersen & Crompton, 2009; Whitmarsh & O'Neill, 2010). For example, an intervention that is set up in one supermarket to motivate consumers to buy organic groceries, could spill over and motivate organic choices in a different supermarket or at a later time. Delayed effects, however, have received little scientific attention (Marchiori, Adriaanse, & De Ridder, 2017; Rogers & Frey, 2014). In the current paper, we investigate the impact and potential spillover of manipulating “defaults”on organic grocery shopping. Defaults, pre-selected choice options that become effective if the decision maker does not actively specify otherwise, in other words does not “opt out,”have been proven effective and are popular with policy makers (Thaler, 1980; Thaler & Sunstein, 2008). Starting with Samuelson and Zeckhauser's (1988) demonstration of the so-called status-quo bias, default manipulations have been shown to impact major life decisions such as organ donations (Abadie & Gay, 2006; Johnson & Goldstein, 2003), medical decisions (Ansher et al., 2014; Halpern et al., 2013) and pension planning (Choi, Laibson, Madrian, & Metrick, 2002, 2003, 2004; Madrian & Shea, 2001). They have also been shown to influence more mundane consumption decisions such as configurations of computers (Brown & Krishna, 2004), bikes (Herrmann et al., 2011) and cars (Johnson, Hershey, Meszaros, & Kunreuther, 1993; Park, Jun, & MacInnis, 2000). More recently, defaults have been applied to promote PEBs, for example to influence choices for “green”electricity tariffs (Ebeling & Lotz, 2015; Pichert & Katsikopoulos, 2008; Vetter & Kutzner, 2016). In a field study, a vast majority of households that received a green electricity tariff by Received: 4 February 2020 Revised: 15 May 2020 Accepted: 14 June 2020 DOI: 10.1002/cb.1850 This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2020 The Authors. Journal of Consumer Behaviour published by John Wiley & Sons Ltd. J Consumer Behav. 2021;20:271–287. wileyonlinelibrary.com/journal/cb 271 default retained it, even though their previous “grey”tariff was cheaper than the green default option (Pichert & Katsikopoulos, 2008). A meta-analysis (Jachimowicz, Duncan, Weber, & Johnson, 2019) of 58 default studies (pooled n= 73,675) speaks for the considerable influence of defaults (d= 0.68, 95% confidence interval = 0.53– 0.83). Although growing evidence demonstrates the effectiveness of defaults for encouraging PEBs, these effects have usually been studied for efficiency behaviors, that is infrequent PEBs that are usually costly and have long term consequences, such as choosing a green energy tariff. However, to our knowledge, causal effects of default manipulations have not yet been studied for curtailment behaviors (Karlin et al., 2014), that is low-cost and frequent PEBs such as grocery shopping. Additionally, there is very limited evidence as to whether default effects impact subsequent PEBs. 2|SPILLOVER EFFECTS When examining how PEBs spill over, one usually distinguishes between three possible outcomes. Exercising one PEB might increase the probability of performing another PEB (positive spillover; Lanzini & Thøgersen, 2014, Thøgersen, 1999), decrease it (negative spillover, Mazar & Zhong, 2010; Zhong, Strejcek, & Sivanathan, 2010; Geng, Cheng, Tang, Zhou, & Ye, 2016) or leave it unaffected (no spillover, McKenzie-Mohr, Nemiroff, Beers, & Desmarais, 1995; see also Dolan & Galizzi, 2015; Nilsson et al., 2017; Thøgersen & Crompton, 2009; Truelove et al., 2014). Spillover has been investigated from one PEB to similar ones over time and to other PEBs, for example from water to electricity consumption, and across life contexts (Littleford, Ryley, & Firth, 2014; Thøgersen & Ölander, 2003). For interventions aimed at increasing PEBs, positive as well as some negative spillover effects have been documented (Maki et al., 2019; Truelove et al., 2014). For example, a recent correlational study revealed that consumers who started buying some organic food product(s) tend to subsequently increase the range of organic products they purchase (Juhl, Fenger, & Thøgersen, 2017). Positive spillover to PEBs such as turning off the lights and biking was also induced through verbal feedback and economic incentives to buy organically labeled groceries (Lanzini & Thøgersen, 2014). Self-perception (Bem, 1972; Freedman & Fraser, 1966) and consistency needs (Festinger, 1957; Festinger & Carlsmith, 1959; Thøgersen, 2004) appear to be partially responsible for positive spillovers. According to self-perception theory, engaging in an initial behavior changes one's self-perception as a certain type of person (e. g., an environmentalist), and this self-perception leads people to act in line with how such a person might behave (Ariely & Norton, 2008). Starting from the seminal demonstration of the foot-in-the-door effect (Burger, 1999; Freedman & Fraser, 1966), empirical research continues to support this explanation. In a study that particularly looked at the effect of self-perception, participants were asked to sign a petition and were subsequently asked to volunteer for a food drive. Individuals, who were labeled as “helpful individuals”by research confederates after signing the petition, were found to be more likely to volunteer than people in the control group (Burger & Caldwell, 2003). Similarly, positive spillover was found when making participants remember their past PEBs (Conway & Peetz, 2012; Cornelissen, Pandelaere, Warlop, & Dewitte, 2008; Fishbach, Dhar, & Zhang, 2006; Van der Werff, Steg, & Keizer, 2014a, 2014b). The fact that more similar behaviors are more likely to result in positive spillover also supports a consistency needs account (Margetts & Kashima, 2017; Truelove et al., 2014). It is further evidence for the role of self-perception and consistency needs that pre-existing pro-environmental attitudes and identities seem to moderate spillover effects. For example, it has been found that strong pro-environmental attitudes and identities not only predict PEBs (De Groot & Steg, 2007; Van der Werff et al., 2014a, 2014b; Van der Werff, Steg, & Keizer, 2013a, 2013b; Whitmarsh & O'Neill, 2010), but are also more likely to result in positive spillover following an intervention (Clot, Grolleau, & Ibanez, 2016; Meijers, 2014). Engaging in an initial behavior will make an individual's corresponding attitude about that behavior more salient, especially if the attitude is strong and already chronically activated. The perception of one's attitudes in turn will trigger a motivation to act consistent and lead to an increased likelihood that the individual will engage in other similar behaviors (Cornelissen et al., 2008; Van der Werff et al., 2014a, 2014b). It can thus be assumed that the situational salience as well as the chronic strength of pro-environmental attitudes facilitate positive spillover. 3|DEFAULT INTERVENTIONS AND SPILLOVER EFFECTS The self-perception and consistency explanation suggest that attributions of behavior might be a critical moderator of spillover effects (Weiner, 1985). Only to the degree that behaviors are attributed or explained internally, that is being caused by oneself as compared to caused by external demands, should they be relevant to self-percep- tion and consistency. Interventions that are designed to discourage external attributions and encourage internal attributions should therefore be more conductive of positive spillover. Against the backdrop, instigating positive spillover seems at least possible using default interventions. While the perceptual aspects of defaults are in principle transparent (Bovens, 2009; Hansen & Jespersen, 2013), it has repeatedly been argued that those confronted with them neither routinely perceive their presence nor their effect on behavior (Ivankovi c & Engelen, 2019; Schmidt, 2017). Additionally, people generally think that they are less influenced by behavior change interventions than others (Junghans, Cheung, & De Ridder, 2015)—a phenomenon called the third person effect (Perloff, 1993)—which has also been shown for defaults (Hummel & Maedche, 2019). For example, a study found that when disclosing a default manipulation for healthier food choices to customers, nearly all customers (87%) responded that it did not affect them (Kroese, Marchiori, & De Ridder, 2015). This suggests that defaulted behaviors 272 KUHN ET AL. are not attributed externally and might hence lead to positive spillover. Beyond the default setting itself, additional aspects of the decision context might promote positive spillover. Subtle interventions with minimal or gradual changes to the choice context have been shown to be more conductive of positive spillover. In one demonstration, gradual and weaker changes to thermostat defaults, in steps from 20 to 19C, showed positive temporal spillover whereas more abrupt and stronger changes, from 20 to 17C, not only eliminated the effects but even reversed them to yield negative spillover (Brown, Johnstone, Hašcˇicˇ, Vong, & Barascud, 2013). Whereas the subtle change might still have been perceived in line with one's identity and attitudes, the stark change might have led to external attributions and, additionally, to reactance tendencies to counteract the influence attempt (Brehm, 1966). Given the subjective perceptions of defaults discussed above, they should qualify as such; a subtle intervention. Second, interventions that allow behaviors to be construed more abstractly should be more conducive of positive spillover. More abstract construals elicit a focus on the ultimate purpose of a behavior, whereas concrete construals tend to highlight contextual reasons for it (Liberman, Sagristano, & Trope, 2002; Nussbaum, Trope, & Liberman, 2003). Consequently, it has been argued that abstract construals increase the propensity of a behavior to be attributed internally (Conway & Peetz, 2012). Empirical evidence shows that temporal-distance is a way to increase abstraction and induce positive spillover. In three studies Conway and Peetz (2012) found that recalling one's concrete actions in the recent past leads to compensatory behavior, whereas recalling one's abstract actions in the distant past leads to consistent behavior. Temporal-distance between the manipulated and subsequent behaviors should therefore increase spillover for PEBs. Third, interventions that can be self-designed seem a promising way to foster internal attributions of the subsequent behaviors and thus promote positive spillover (Hertwig, 2017; Hertwig & Grüne- Yanoff, 2017; Lades, 2014; Reijula & Hertwig, 2020). Giving people the option to reflect on a choice situation increases the salience of one's identity and attitudes (Lades, 2014) and should therefore increase consistency needs and the likelihood of spillover. A qualitative study indicates that people who nudged themselves to receive an organic food box had a stronger pro-environmental self-identity and involvement and bought more organic groceries (Torma, Aschemann- Witzel, & Thøgersen, 2017). Self-selected defaults might thus be a way to induce spillover. In sum, positive spillover seems possible following default interventions, most likely for interventions that “engage”the self, and when they are encountered by people with the existing predisposition to act pro-environmentally. 4|STUDY OVERVIEW AND HYPOTHESES In a series of three studies, we investigated default effects and their potential spillover in an online shopping scenario with decisions made between conventionally and organically produced groceries. We asked participants to compose shopping carts for a specified budget. After the data collection one of the composed shopping carts was randomly selected and awarded to the respective participant as an incentive. To fill the first cart, participants made dichotomous choices with either an organic or a conventional default setting. In order to study possible spillover effects over time, participants filled a second cart based on choices where no default was present, which we will henceforth refer to as “active choice.”As additional predictor and potential moderators of spillover effects, we assessed the attitude towards organically produced groceries and its strength. In Experiment 1, we established the paradigm. We hypothesized that (H1) an organic (conventional) default setting would lead to more organic (conventional) products in the shopping cart composed in the presence of the default. Furthermore, we hypothesized that positive attitudes towards organic products would also manifest in behavior and lead to more selected organic products (H2). Regarding spillover, we assumed that a defaulted behavior would leads to positive spillover in a subsequent choice situation (H3), reflected in a persisting effect of the initial default manipulation for active choices. We also hypothesized that strong pro-environmental attitudes would lead to even more positive spillover following the default manipulation (H4), reflected in an interaction effect between the default and attitudes for the active choices. We also manipulated how blatant the influence attempt was by creating defaults that were either easy or effortful to deselect. Hard to change defaults came with a pop-up window asking for confirmation about the deselection of the default and a short time delay. We expect the blatant version of the default to be perceived as a strong influence attempt, causing external attribution and reduce spillover (H5), reflected in an interaction between default and effort on active choices. Based on the finding that more abstract and distant past PEBs promote internal attribution and therefore positive spillover, we manipulated the time distance between initial and subsequent choices in Experiment 2. We predicted that increasing the time-distance between composing the initial and subsequent grocery cart would result in more positive spillover (H6). In Experiment 3, we aimed to directly foster internal attributions of the defaulted behavior by having participants self-select their default. Studying the effects of self-selection, however, requires a strict control for personal preferences as these will be related to the selection itself. We decided to rely on actual behavior, measured in a base-line shop rather than on attitudes for a control. We hypothesized that while controlling for existing preferences, self-selected as compared to other selected defaults would result in more positive spillover (H7). 5|EXPERIMENT 1 In Experiment 1 we tested the first five hypotheses. For the first set of choices we manipulated the default option (organic vs. conventional) and the effort required (high vs. low) to deselect the default option. Subsequently, we asked participants to make further product KUHN ET AL.273 choices in an active choice format. As a manipulation check of the effort manipulation, we included items measuring the perceived threat to freedom in making the choice after each shop. 5.1 |Methods 5.1.1 |Participants The 90 participants (72 women, 18 men) who took part in the study were between 18 and 32 years of age (M= 22.4, SD = 5.1), with one outlier aged 62. Nearly all were students. Thirty-five participants were psychology students, the rest were from a variety of other academic backgrounds. Participants took between 8 and 15 min (M= 11.4, SD = 1.6) to complete the study. The study was run in a laboratory as part of a series of four experiments. The current experiment was the fourth starting approximately 45 min into the session. The study was programmed with SoSci Survey (Leiner, 2014) and conducted via a browser in the laboratory. 5.1.2 |Procedure Participants were told that they would visit two online shops. They were instructed to compose a shopping cart consisting of 20 products they deemed attractive. They were also informed that they had the chance to win the cart together with the money they did not spend. At the very beginning of the study, we asked participants for their email address and an eight-digit code (consisting of the first two letters/digits of: their mother's name, their father's name, the city they were born in and their date of birth) in order to allow for anonymity while still being able to determine the product choices of the winner of the raffle. Next, participants received a list of groceries and were asked to select the products they were interested in. In that way we wanted to make product choices more relevant to participants. They had to select 20 products from a wide range of 72 products (e.g., spaghetti, yoghurt, carrots, banana, gherkins, tortilla chips). For easier orientation, items were classified and ordered into six different product categories (cereal products, dairy products and eggs, vegetables, fruits, canned and frozen food and sweets and snacks; see Figure 1). After this initial task, participants were asked to visit two different shops (see Figure 2). In each shops, participant had to decide for each product whether they wanted an organic or a conventional option of that product. The selected products from the list were divided between the two shops. In the first shop, participants were introduced to defaulted choice sets of half of the products and subsequently to the second shop with active choice sets without a default with the other half. For each shop we created a shop name and a blue and a yellow shop label that was presented at the top, while participants were partaking in the choice task of each shop. Names and labels were counterbalanced across conditions. Details on the choice task will follow below. After visiting the first shop, participants received feedback via another list about all the products they had chosen in the first shop. Every item was displayed with a small picture and its description, including whether it was organic or not. Furthermore, participants saw the additional amount of money that they would receive (details below). Next, participants were asked to rate their shopping experience. Interspersed with distractor items, we included items measuring participants' perceptions of external influence attempt. The second shop with containing active choice sets was followed by the same procedure: participants received feedback about their chosen products and the additional money, followed by the request to rate the shopping experience. Next, participants were asked to indicate their attitude towards organic groceries and the strength thereof. We placed these questions after the shopping simulation to avoid the non-shopping-related activation of environmental identities. Attitudes measures were unaffected by the experimental manipulations. An analysis of variance (ANOVA) revealed no significant difference between the default conditions F(1, 86) = 2.76, p= .10, η p2 = .03) or the effort conditions F(1, 86) = 0.00, p= .96, η p2 = .00. Finally, we collected demographics, thanked, and debriefed participants. 5.1.3 |Purchases of organic products Ten out of the 20 selected products were presented in each shop. The order of the products was randomized. Products were presented in dichotomous choice sets (see Figure 2). Each choice set consisted of an organic and a conventional option of the same quantity and product type. The positioning of the organic and conventional products (left or right side of the screen) was randomized between, but not within, participants. We used the German organic label that was introduced by the federal ministry of food, agriculture and consumer protection. In the experiment the label was added to every organic unpackaged product. Organic packaged products had the label directly on the packaging. We tried to select the products and their pictures carefully, so they would appear equally attractive. Participants were also presented with the price of the products. Prices were informed by realistic prices from online shops. None of the product prices exceeded four euros. Conventional products were always slightly cheaper than organic products. By clicking on the “Add to Cart”button, the chosen product was added to a participant's shopping cart, which was then put into the raffle. If participants chose the cheaper conventional product, the price difference was added as a cash payment to participants' potential win. Participants were informed about the process in the instructions. In that way, we wanted to implement the real-life tradeoff between organic products and the savings of a cheaper conventional product. 5.1.4 |Default manipulation In the first shop (default shop) one option was preselected by default for each product pair. In other words, whenever participants were 274 KUHN ET AL. confronted with a choice set, one tick box was already selected. Between participants, the default varied to be either organic or conventional. Within participants the default was kept the same for all choice sets. 5.1.5 |Effort manipulation For half of the participants, we increased the effort to deselect the default, making the default manipulation more blatant. When participants tried to select the non-defaulted product, a pop-up window would appear, asking participants to confirm the deselection of the product. On confirmation, the pop-up window disappeared only after a 2 s delay. If the participant reconsidered and decided to stay with the default, the pop-up window immediately disappeared. The other half of participants could simply select the non-defaulted product without additional effort, making the default manipulation subtler. In the following we will refer to these conditions as high and low effort condition. We included a manipulation check for the effort manipulation by measuring participants' perceptions of external influence on their behavior (modeled after Dillard & Shen, 2005, Shen & Dillard, 2005) FIGURE 1 List of grocery products from which participants could choose. Participants were asked to select at least from three categories and at least two products from chosen categories and a total of 20 products. Products were sorted into six categories. The original material was in German, which was translated for this graphic [Colour figure can be viewed at wileyonlinelibrary.com] KUHN ET AL.275 directly after each shop. In 11 items, participants were asked to rate the shopping experience on a 5-point Likert type scale. Four items measured the perception of an external influence attempt: “I was free to decide for myself,”“An attempt was made to make a decision for me,”“Attempts were made to manipulate me.”and “I was put under pressure.”The other seven items were distractor items, referring to the presentation of the products, the user interface or the name of the shop (e.g., “I found the pictures of the products appealing”). Distractor items were not included in the scale and were not analyzed. The scale was highly reliable (first shop: α= .82, M= 2.73, SD =1.02,MIN =1.0,MAX =4.75andsecond shop: α= .84, M=2.09,SD = .92, MIN =1.0,MAX = 4.00). 5.1.6 |Spillover effects of the default manipulation In the second shop, the remaining 10 products were also presented in choice sets consisting of an organic and conventional option, as in the first shop, but without preselected ticked boxes. The active choice format was chosen to measure the spillover effect of the default manipulation in the first shop. To measure the effect of the default on choices, we aggregated choices of each shop and used the percentage of organic products people chose in each shop as the main dependent variable. 5.1.7 |Attitude towards organic products To examine the predisposition to prefer organic products as a possible moderator of the default manipulation, we measured participants' attitudes towards organic products (e.g., Stolz, Stolze, Hamm, Janssen, & Ruto, 2011) and attitudes' strength (e.g., Brannon, Tagler, & Eagly, 2007; Vetter & Kutzner, 2016; Wegener, Downing, Krosnick, & Petty, 1995). The attitude scale consisted of 14 5-point Likert-type scale items. The extremes of the 5-point scale were labeled “I absolutely do not agree”and “I completely agree.”Even though the original FIGURE 2 Example product presentation: here organic (left) and conventional mango (right). Prices were presented in Euros. The tick mark could be changed by clicking on the tick boxes. Participants could add the product to the shopping cart by clicking on “Add to Cart.” The original material was in German, which was translated for this graphic [Colour figure can be viewed at wileyonlinelibrary.com] 276 KUHN ET AL. authors proposed the measure to contain five factors, we used it as a one-dimensional measure based on the mean of the 14 items. For easier interpretability, we use a mean score instead of principal component analysis, which proved to be highly reliable (α= .80, M= 3.62, SD = .57, MIN = 2.29, MAX = 4.71). We also measured attitude strength (Brannon et al., 2007; Vetter & Kutzner, 2016; Wegener et al., 1995). On a 7-point Likert-type scale participants could indicate the level of importance (“How important is the topic of organically produced food?.”“How important is the topic to you compared with other issues?”), certainty (“How sure are you that your opinion on organically produced food is correct?,”“How likely are you to change your opinions about the topic?,”“How certain do you feel about your attitude toward the topic?”), personal relevance (“How relevant is the topic of organically produced food for you personally?”), elaboration (“How much have you thought about the topic before?”), and subjective knowledge about the production of organic products (“How well informed are you about the topic?”). Furthermore, subjective ambivalence was measured similar to Priester and Petty (1996) with the following three statements: “Concerning the topic of organically produced food, I feel …no conflict at all/feel no indecision at all/completely one-sided reactions versus …maximal conflict/maximal indecision/completely mixed reactions.”As in previous studies (Brannon et al., 2007; Vetter & Kutzner, 2016), we used a one-dimensional index of attitude strength that we obtained by combining the different self-report measures, performing a principal component analysis, and then combining those items that clearly loaded on the same factor (as indicated by one large eigenvalue and a drop in eigenvalues thereafter, visible in a scree-plot). Theattitudestrengthitemsmeasuring different dimensions clearly loaded on one reliable factor (α=.90). 5.1.8 |Demographics Demographic data were measured at the end of the study and included gender, age, first language and occupation. We also assessed participants' food preferences by asking which of the following food types they eat: meat, fish, eggs, milk, grain, vegetables, fruits, canned food, frozen food, sweets, and snacks. Those were just used for exploratory purposes and not analyzed further in this paper. 5.2 |Results All data analyses were conducted in R (R Core Team, 2020). We analyzed effects with linear mixed-effects models, using the lmer() command in the lme4 package (Bates, Maechler, Bolker, & Walker, 2015). Further analyses were conducted with the lm() command. All regression analyses use mean-centered predictors. 5.2.1 |Manipulation checks At first, we looked at participants' perceived external reasons for their behavior in Shop 1 that contained the default. An independentsamples t-test was conducted to compare perceived external influence attempt in the high-effort and the low-effort conditions. There was a significant difference in the scores for high effort (M= 3.00, SD = 1.04, MIN = 1.25, MAX = 4.75) and low effort (M= 2.48, SD = .94, MIN = 1.0, MAX = 4.5) conditions; t(88) = 2.47, p= .015, 95% CI [0.10, .93], Hedges's g s = .51. The results suggest that participants who received a pop-up window whenever they wanted to change the default were more likely to perceive an external reason for their behavior than participants who did not receive a pop-up window. 5.2.2 |Attitude measurement Attitude and attitude strength were highly correlated, r= .61, p< .001. We calculated all analyses with both measures and found the same pattern. Since both measures yield similar results, we will just report results with the attitude measure to reduce multicollinearity. 5.2.3 |Default effect We used a mixed-effects model to predict the percentage of organic products being chosen in each shop. The predictors were the default setting in Shop 1 (organic vs. conventional), the effort manipulation in Shop 1 (low vs. high), the shop (Shop 1: default vs. Shop 2: active choice) and the attitude towards organic products. We modeled random intercepts for every subject and fixed effects for the experimental manipulations. The analysis revealed a significant effect for the default manipulation, b= 12.53, SE = 4.7, t(82) = 2.66, p= .009. Overall, participants who received an organic default chose more organic products than participants who received a conventional default. We also found a significant effect for participants' attitudes, b= 23.43, SE = 4.23, t (82) = 5.54, p< .001. Participants with high attitudes towards organic products chose more organic products than participants with low attitudes. There was no interaction between attitudes and the default manipulation, b= 1.18, SE = 8.45, t(82) = .14, p= .88. Participants with high attitude scores were equally influenced by the default as were participants with low attitudes. The analysis revealed no main effect for the effort manipulation, b= 3.94, SE = 4.71, t(82) = .84, p= .40 and no interaction between the effort and the default manipulation, b=−5.65, SE = 9.41, t (82) = −.60, p= .55. A higher effort to deselect the default did not have an influence on participants' choices. People in the high effort condition did not stay more often with the defaulted option than people in the low effort condition. Most critical to our study, the analysis revealed a significant interaction between the default and the shop, b=−18.34, SE = 3.79, t (82) = −4.84, p< .001. The interaction indicates that the default had a stronger influence in the first shop than (if any) in the second shop with active choice. In other words, participants who received an organic (conventional) default chose more organic (conventional) KUHN ET AL.277 products in the first shop, but this effect decreased or even disappeared in the second shop. The mixed-model analysis did not reveal any further significant effects, all t's< 1.59, particularly, it did not reveal a three-way interaction between the effort manipulation, the default and the shop, b= 7.61, SE = 7.59, t(82) = 1.00, p= .32. To further analyze the interaction between the default and shop, two further linear regressions were calculated corresponding to the plots in Figure 3. The first regression analysis was calculated with percentage of organic products chosen in the first shop as dependent variable, the second regression was run analogously on the second shop. In the first shop, where the default was manipulated, we found an effect of the default, b= 21.7, SE = 4.77, t(82) = 4.55, p< .001, and of participants' attitudes, b= 23.5, SE = 4.28, t(82) = 5.48, p< .001. Again this analysis confirms for choices in the first shop that participants who were confronted with an organic default, chose more organic products than participants who were confronted with a conventional default. Furthermore, independent of the default the higher a participant's attitude was, the more organic products he or she chose on average. There was no significant interaction between the two, b=−1.7, SE = 8.57, t(82) = −0.21, p= .837. The analysis did not reveal any further significant effects, all t's< 1.05. In the second shop, again including participants' attitudes, no evidence of an effect of the default was found, b= 3.35, SE = 5.36, t (82) = .63, p= .53, while participants' attitudes still affected the percentage of organic products chosen, b= 23.5, SE = 4.28, t(82) = 5.48, p< .001. In other words, this analysis in addition to the mixed-effect model clarifies that the effect of the default did not only decrease in the second shop, but disappeared. There was also no significant interaction between the default manipulation and attitudes, b= 4.14, SE = 9.62, t(82) = 0.43, p= .668. The analysis did not reveal any further effects, all t's< 0.89. 5.3 |Discussion As in previous studies, participants' decisions were influenced by the presence of a default manipulation (Johnson & Goldstein, 2003; Pichert & Katsikopoulos, 2008) confirming H1. Supporting H2, choices were predicted by attitudes. Replicating previous studies, the default manipulation did not interact with attitudes. It influenced participants' choices regardless of whether they held clearly positive attitudes, here, towards organic products, or not (Vetter & Kutzner, 2016). Furthermore, in line with the meta-analysis on defaults (Jachimowicz et al., 2019), higher effort to deselect the default did not strengthen the default effect. With regards to spillover, as soon as the default was replaced with an active choice format in a subsequent shop, only participants' attitudes predicted their choices. The default manipulation did not spill over (H3). This was true regardless of whether the default was encountered by participants holding positive attitudes towards organic groceries or not (H4). This is noteworthy given the already left-skewed distribution of mainly positive attitudes in the investigated population. Since we did not find evidence for positive spillover, the increased effort to deselect the default, which made the influence attempt more obvious, could not further reduce the spillover effects (H5). In the next two experiments, we investigated moderators that should increase the chance of finding positive spillover effects. We first focused on temporal distance. Conway and Peetz (2012) showed that recent moral or immoral behavior can lead to compensatory behavior, whereas temporally distant behavior can lead to consistent behavior. The authors argue that time distance will lead to more abstract representations of the former behavior and activate identity concerns that trigger commitment and amplify positive spillover 0% 25% 50% 75% 100% -1.0 -0.5 0.0 0.5 1.0 Attitude (mean-centered) Organic Products Shop 1: Defaulted Choice 0% 25% 50% 75% 100% -1.0 -0.5 0.0 0.5 1.0 Attitude (mean-centered) Shop 2: Active Choice Default in Shop 1 Organic Conventional FIGURE 3 Regression lines for the first shop with a default and the second shop with an active-choice format as a function of the default manipulation in the first shop. The regression plots show the relationship between the percentage of organic products participants chose and their attitudes as a function of the default manipulation in the first shop. The lines represent regression lines for each condition 278 KUHN ET AL. Dahlstrand, U., & Biel, A. (1997). Pro-environmental habits: Propensity levels in behavioral change 1. Journal of Applied Social Psychology,27 (7), 588–601. https://doi.org/10.1111/j.1559-1816.1997.tb00650.x De Groot, J., & Steg, L. (2007). General beliefs and the theory of planned behavior: The role of environmental concerns in the TPB. Journal of Applied Social Psychology,37(8), 1817–1836. https://doi.org/10.1111/ j.1559-1816.2007.00239.x Dillard, J. P., & Shen, L. (2005). On the nature of reactance and its role in persuasive health communication. Communication Monographs,72(2), 144–168. https://doi.org/10.1080/03637750500111815 Dolan, P., & Galizzi, M. M. (2015). Like ripples on a pond: Behavioral spillovers and their implications for research and policy. Journal of Economic Psychology,47,1–16. https://doi.org/10.1016/j.joep.2014. 12.003 Ebeling, F., & Lotz, S. (2015). Domestic uptake of green energy promoted by opt-out tariffs. Nature Climate Change,5(9), 868–871. https://doi. org/10.1038/nclimate2681 Festinger, L. (1957). A theory of cognitive dissonance, Stanford: Stanford University Press. Festinger, L., & Carlsmith, J. M. (1959). Cognitive consequences of forced compliance. The Journal of Abnormal and Social Psychology,58(2), 203– 210. https://doi.org/10.1037/h0041593 Fishbach, A., & Dhar, R. (2005). Goals as excuses or guides: The liberating effect of perceived goal progress on choice. Journal of Consumer Research,32(3), 370–377. https://doi.org/10.1086/497548 Fishbach, A., Dhar, R., & Zhang, Y. (2006). Subgoals as substitutes or complements: The role of goal accessibility. Journal of Personality and Social Psychology,91(2), 232–242. https://doi.org/10.1037/0022-3514.91.2.232 Freedman, J. L., & Fraser, S. C. (1966). Compliance without pressure: The foot-in-the-door technique. Journal of Personality and Social Psychology,4(2), 195–202. https://doi.org/10.1037/h0023552 Geng,L.,Cheng,X.,Tang,Z.,Zhou,K.,&Ye,L.(2016).Canprevious pro-environmental behaviours influence subsequent environmental behaviours? The licensing effect of pro-environmental behaviours. Journal of Pacific Rim Psychology,10, E9. https://doi.org/10.1017/ prp.2016.6 Ghesla, C., Grieder, M., & Schmitz, J. (2019). Nudge for good? Choice defaults and spillover effects. Frontiers in Psychology,10, 178. https:// doi.org/10.3389/fpsyg.2019.00178 Gneezy, A., Imas, A., Brown, A., Nelson, L. D., & Norton, M. I. (2012). Paying to be nice: Consistency and costly prosocial behavior. Management Science,58(1), 179–187. https://doi.org/10.1287/mnsc.1110.1437 Halpern, S. D., Loewenstein, G., Volpp, K. G., Cooney, E., Vranas, K., Quill, C. M., …Bryce, C. (2013). Default options in advance directives influence how patients set goals for end-of-life care. Health Affairs,32 (2), 408–417. https://doi.org/10.1377/hlthaff.2012.0895 Hansen, P. G., & Jespersen, A. M. (2013). Nudge and the manipulation of choice: A framework for the responsible use of the nudge approach to behaviour change in public policy. European Journal of Risk Regulation, 4(1), 3–28. https://doi.org/10.1017/S1867299X00002762 Herrmann, A., Goldstein, D. G., Stadler, R., Landwehr, J. R., Heitmann, M., Hofstetter, R., & Huber, F. (2011). The effect of default options on choice—Evidence from online product configurators. Journal of Retailing and Consumer Services,18(6), 483–491. https://doi.org/10.1016/j. jretconser.2011.06.005 Hertwig, R. (2017). When to consider boosting: Some rules for policymakers. Behavioural Public Policy,1(2), 143–161. https://doi.org/10. 1017/bpp.2016.14 Hertwig, R., & Grüne-Yanoff, T. (2017). Nudging and boosting: Steering or empowering good decisions. Perspectives on Psychological Science,12 (6), 973–986. https://doi.org/10.1177/1745691617702496 Hummel, D., & Maedche, A. (2019). How effective is nudging? A quantitative review on the effect sizes and limits of empirical nudging studies. Journal of Behavioral and Experimental Economics,80,47–58. https:// doi.org/10.1016/j.socec.2019.03.005 Liberman N., Sagristano M. D., Trope Y. (2002). The effect of temporal distance on level of mental construal. Journal of Experimental Social Psychology,38(6), 523–534. http://dx.doi.org/10.1016/s0022-1031(02) 00535-8. Ivankovi c, V., & Engelen, B. (2019). Nudging, transparency, and watchfulness. Social Theory and Practice,45(1), 43–73. https://doi.org/10. 5840/soctheorpract20191751 Jachimowicz, J. M., Duncan, S., Weber, E. U., & Johnson, E. J. (2019). When and why defaults influence decisions: A meta-analysis of default effects. Behavioural Public Policy,3,1–28. https://doi.org/10.1017/ bpp.2018.43 Johnson, E. J., & Goldstein, D. (2003). Do defaults save lives? Science,302 (5649), 1338–1339. https://doi.org/10.1126/science.1091721 Johnson, E. J., Hershey, J., Meszaros, J., & Kunreuther, H. (1993). Framing, probability distortions, and insurance decisions. Journal of Risk and Uncertainty,7(1), 35–51. https://doi.org/10.1037/a0017346 Juhl, H. J., Fenger, M. H., & Thøgersen, J. (2017). Will the consistent organic food consumer step forward? An empirical analysis. Journal of Consumer Research,44(3), 519–535. https://doi.org/10.1093/jcr/ ucx052 Junghans, A. F., Cheung, T. T., & De Ridder, D. D. (2015). Under consumer's scrutiny—An investigation into consumers attitudes and concerns about nudging in the realm of health behavior. BMC Public Health,15, 336. https://doi.org/10.1186/s12889-015-1691-8 Karlin, B., Davis, N., Sanguinetti, A., Gamble, K., Kirkby, D., & Stokols, D. (2014). Dimensions of conservation: Exploring differences among energy behaviors. Environment and Behavior,46(4), 423–452. https:// doi.org/10.1177/0013916512467532 Kroese, F. M., Marchiori, D. R., & De Ridder, D. T. D. (2015). Nudging healthy food choices: A field experiment at the train station. Journal of Public Health,38,1–5. https://doi.org/10.1093/pubmed/fdv096 Lades, L. K. (2014). Impulsive consumption and reflexive thought: Nudging ethical consumer behavior. Journal of Economic Psychology,41, 114– 128. https://doi.org/10.1016/j.joep.2013.01.003 Lanzini, P., & Thøgersen, J. (2014). Behavioural spillover in the environmental domain: An intervention study. Journal of Environmental Psychology,40, 381–390. https://doi.org/10.1016/j.jenvp.2014.09.006 Leiner, D. J. (2014). SoSci Survey (Version 2.5.00-i) [Computer Software]. Retrieved from https://www.soscisurvey.de Littleford, C., Ryley, T. J., & Firth, S. K. (2014). Context, control and the spillover of energy use behaviours between office and home settings. Journal of Environmental Psychology,40, 157–166. https://doi.org/10. 1016/j.jenvp.2014.06.002 Madrian, B. C., & Shea, D. F. (2001). The power of suggestion: Inertia in 401(k) participation and savings behavior. Quarterly Journal of Economics,116(4), 1149–1187. https://doi.org/10.1162/ 003355302753399535 Maki, A., Carrico, A. R., Raimi, K. T., Truelove, H. B., Araujo, B., & Yeung, K. L. (2019). Meta-analysis of pro-environmental behaviour spillover. Nature Sustainability,2(4), 307–315. https://doi.org/10. 1038/s41893-019-0263-9 Marchiori, D. R., Adriaanse, M. A., & De Ridder, D. T. (2017). Unresolved questions in nudging research: Putting the psychology back in nudging. Social and Personality Psychology Compass,11(1), e12297. https://doi. org/10.1111/spc3.12297 Margetts, E. A., & Kashima, Y. (2017). Spillover between pro-environmen- tal behaviours: The role of resources and perceived similarity. Journal of Environmental Psychology,49,30–42. https://doi.org/10.1016/j. jenvp.2016.07.005 Maslow, A. (1968). Some educational implications of the humanistic psychologies. Harvard Educational Review,38(4), 685–696. https://doi. org/10.17763/haer.38.4.j07288786v86w660 Mazar, N., & Zhong, C. (2010). Do green products make us better people? Psychological Science,21(4), 494–498. https://doi.org/10.1177/ 0956797610363538 KUHN ET AL.285 McKenzie-Mohr, D., Nemiroff, L. S., Beers, L., & Desmarais, S. (1995). Determinants of responsible environmental behavior. Journal of Social Issues,51(4), 139–156. https://doi.org/10.1111/j.1540-4560.1995. tb01352.x Meijers, M. H. C. (2014). On justifying eco-unfriendly behaviors. (PhD thesis). Univ. Amsterdam. p. 158. Nilsson, A., Bergquist, M., & Schultz, W. P. (2017). Spillover effects in environmental behaviors, across time and context: A review and research agenda. Environmental Education Research,23(4), 573–589. https:// doi.org/10.1080/13504622.2016.1250148 Nussbaum, S., Trope, Y., & Liberman, N. (2003). Creeping dispositionism: The temporal dynamics of behavior prediction. Journal of Personality and Social Psychology,84(3), 485–497. https://doi.org/10.1037/0022- 3514.84.3.485 Osbaldiston, R., & Schott, J. P. (2012). Environmental sustainability and behavioral science: Meta-analysis of proenvironmental behavior experiments. Environment and Behavior,44(2), 257–299. https://doi. org/10.1177/0013916511402673 Park, C. W., Jun, S. Y., & MacInnis, D. J. (2000). Choosing what I want versus rejecting what I do not want: An application of decision framing to product option choice decisions. Journal of Marketing Research,37(2), 187–202. https://doi.org/10.1509/jmkr.37.2.187.18731 Perloff, R. M. (1993). Third-person effect research 1983–1992: A review and synthesis. International Journal of Public Opinion Research,5, 167– 184. https://doi.org/10.1093/ijpor/5.2.167 Pichert, D., & Katsikopoulos, K. V. (2008). Green defaults: Information presentation and pro-environmental behaviour. Journal of Environmental Psychology,28(1), 63–73. https://doi.org/10.1016/j.jenvp.2007.09.004 Priester, J. R., & Petty, R. E. (1996). The gradual threshold model of ambivalence: Relating the positive and negative bases of attitudes to subjective ambivalence. Journal of Personality and Social Psychology,71(3), 431–449. https://doi.org/10.1037/0022-3514.71.3.431 Prothero, A., Dobscha, S., Freund, J., Kilbourne, W. E., Luchs, M. G., Ozanne, L. K., & Thøgersen, J. (2011). Sustainable consumption: Opportunities for consumer research and public policy. Journal of Public Policy & Marketing,30(1), 31–38. https://doi.org/10.1509/ jppm.30.1.31 R Core Team. (2020). R: A language and environment for statistical computing, Vienna, Austria: R Foundation for Statistical Computing ISBN 3– 900051–07-0. Retrieved from https://www.R-project.org Reijula, S., & Hertwig, R. (2020). Self-nudging and the citizen choice architect. Behavioural Public Policy,1–31. https://doi.org/10.1017/pbb. 2020.5 Rettie, R., Burchell, K., & Barnham, C. (2014). Social normalisation: Using marketing to make green normal. Journal of Consumer Behaviour,13(1), 9–17. https://doi.org/10.1002/cb.1439 Rogers, T., & Frey, E. (2014). Changing behavior beyond the here and now. In Gideon Keren & George Wu. Blackwell handbook of judgment and decision-making. Oxford, England. https://doi.org/10.1002/9781 118468333.ch25 Samuelson, W., & Zeckhauser, R. (1988). Status quo bias in decision making. Journal of Risk and Uncertainty,1(1), 7–59. https://doi.org/10. 1007/BF00055564 Schmidt, A. T. (2017). The power to nudge. American Political Science Review, 111(2), 404–417. https://doi.org/10.1017/S0003055417000028 Shen, L., & Dillard, J. P. (2005). Psychometric properties of the Hong psychological reactance scale. Journal of Personality Assessment,85(1), 74– 81. https://doi.org/10.1207/s15327752jpa8501_07 Stern, P. C. (2011). Contributions of psychology to limiting climate change. American Psychologist,66(4), 303–314. https://doi.org/10.1037/ a0023235 Stolz, H., Stolze, M., Hamm, U., Janssen, M., & Ruto, E. (2011). Consumer attitudes towards organic versus conventional food with specific quality attributes. NJAS-Wageningen Journal of Life Sciences,58(3–4), 67– 72. https://doi.org/10.1016/j.njas.2010.10.002 Suh, E. M. (2002). Culture, identity consistency, and subjective well-being. Journal of Personality and Social Psychology,83(6), 1378–1391. https:// doi.org/10.1037/0022-3514.83.6.1378 Susewind, M., & Hoelzl, E. (2014). A matter of perspective: Why past moral behavior can sometimes encourage and other times discourage future moral striving. Journal of Applied Social Psychology,44(3), 201– 209. https://doi.org/10.1111/jasp.12214 Thaler, R. H. (1980). Toward a positive theory of consumer choice. Journal of Economic Behavior and Organization,1(1), 39–60. https://doi.org/10. 1016/0167-2681(80)90051-7 Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. New Haven, CT: Yale University Press. Thøgersen, J. (1999). Spillover processes in the development of a sustainable consumption pattern. Journal of Economic Psychology,20(1), 53– 81. https://doi.org/10.1016/S0167-4870(98)00043-9 Thøgersen, J. (2004). A cognitive dissonance interpretation of consistencies and inconsistencies in environmentally responsible behavior. Journal of Environmental Psychology,24(1), 93–103. https://doi.org/10. 1016/S0272-4944(03)00039-2 Thøgersen, J., & Crompton, T. (2009). Simple and painless? The limitations of spillover in environmental campaigning. Journal of Consumer Policy, 32(2), 141–163. https://doi.org/10.1007/s10603-009-9101-1 Thøgersen, J., & Ölander, F. (2003). Spillover of environment-friendly consumer behaviour. Journal of Environmental Psychology,23(3), 225–236. https://doi.org/10.1016/S0272-4944(03)00018-5 Torma, G., Aschemann-Witzel, J., & Thøgersen, J. (2017). I nudge myself: Exploring ‘self-nudging’strategies to drive sustainable consumption behaviour. International Journal of Consumer Studies,42(1), 141–154. https://doi.org/10.1111/ijcs.12404 Truelove, H. B., Carrico, A. R., Weber, E. U., Raimi, K. T., & Vandenbergh, M. P. (2014). Positive and negative spillover of pro-envi- ronmental behavior: An integrative review and theoretical framework. Global Environmental Change,29, 127–138. https://doi.org/10.1016/j. gloenvcha.2014.09.004 Van der Werff, E., Steg, L., & Keizer, K. (2013a). It is a moral issue: The relationship between environmental self-identity, obligation-based intrinsic motivation and pro-environmental behaviour. Global Environmental Change,23(5), 1258–1265. https://doi.org/10.1016/j. gloenvcha.2013.07.018 Van der Werff, E., Steg, L., & Keizer, K. (2013b). The value of environmental self-identity: The relationship between biospheric values, environmental self-identity and environmental preferences, intentions and behaviour. Journal of Environmental Psychology,34,55–63. https://doi. org/10.1016/j.jenvp.2012.12.006 Van der Werff, E., Steg, L., & Keizer, K. (2014a). Follow the signal: When past pro-environmental actions signal who you are. Journal of Environmental Psychology,40, 273–282. https://doi.org/10.1016/j.jenvp.2014.07.004 Van der Werff, E., Steg, L., & Keizer, K. (2014b). I am what I am, by looking past the present: The influence of biospheric values and past behavior on environmental self-identity. Environment and Behavior,46(5), 626– 657. https://doi.org/10.1177/0013916512475209 Verplanken, B., & Roy, D. (2016). Empowering interventions to promote sustainable lifestyles: Testing the habit discontinuity hypothesis in a field experiment. Journal of Environmental Psychology,45, 127–134. https://doi.org/10.1016/j.jenvp.2015.11.008 Verplanken, B., Walker, I., Davis, A., & Jurasek, M. (2008). Context change and travel mode choice: Combining the habit discontinuity and selfactivation hypotheses. Journal of Environmental Psychology,28(2), 121–127. https://doi.org/10.1016/j.jenvp.2007.10.005 Vetter, M., & Kutzner, F. (2016). Nudge me if you can—How defaults and attitude strength interact to change behavior. Comprehensive Results in Social Psychology,1(1–3), 8–34. https://doi.org/10.1080/23743603. 2016.1139390 Walker, I., Thomas, G. O., & Verplanken, B. (2015). Old habits die hard: Travel habit formation and decay during an office relocation. 286 KUHN ET AL. Environment and Behavior,47(10), 1089–1106. https://doi.org/10. 1177/0013916514549619 Wegener, D. T., Downing, J., Krosnick, J. A., & Petty, R. E. (1995). Measures and manipulations of strength-related properties of attitudes: Current practice and future directions. In R. E. Petty & J. A. Krosnick (Eds.), Attitude strength. Antecedents and consequences (Vol. 4, pp. 455–487). Mahwah, NJ: Lawrence Erlbaum Associates. Weiner, B. (1985). An attributional theory of achievement motivation and emotion. Psychological Review,92(4), 548–573. https://doi.org/10. 1037/0033-295X.92.4.548 Whitmarsh, L., & O'Neill, S. (2010). Green identity, green living? The role of pro-environmental self-identity in determining consistency across diverse pro-environmental behaviours. Journal of Environmental Psychology,30(3), 305–314. https://doi.org/10.1016/j.jenvp.2010.01.003 Zhong, C. B., Strejcek, B., & Sivanathan, N. (2010). A clean self can render harsh moral judgment. Journal of Experimental Social Psychology,46(5), 859–862. https://doi.org/10.1016/j.jesp.2010.04.003 AUTHOR BIOGRAPHIES Sascha Kuhn obtained his Master's degree in 2015 at the University of Heidelberg, where he currently is a PhD student at the department of social psychology. His research interests are situated in the fields of behavior change and sustainable consumption with a focus on defaults and social norms. He is currently employed at the German Development Institute - Deutsches Institut für Entwicklungspolitik (DIE) where he is working on a project studying the consumption patterns of middle income countries. Dr. Max Ihmels obtained his Master's degree at the University of Heidelberg in 2012, where he also acquired his PhD in the social psychology department in 2016. He then worked as a post-doc- toral researcher at the social psychology department in Tübingen before leaving academia to work as a data scientist for an online marketplace (2018). In his research, he focuses on contingency learning, priming, framing effects and evaluative conditioning PD Dr. Florian Kutzner is currently employed as post-doctoral researcher at the chair for social psychology at the University of Heidelberg. He obtained his Diploma in psychology (2006) at the University of Jena and his PhD (2009) at the University of Heidelberg. He spent one year (2012–2013) working with the behavioral science group of Prof. Nick Chater at Warwick Business School, UK. His research revolves around human rationality and the question how sustainable behavior can be promoted using monetary and psychological or symbolic incentives. He is active promoting behavioral insights to NGO's and governmental institutions. How to cite this article: Kuhn S, Ihmels M, Kutzner F. Organic defaults in online-shopping: Immediate effects but no spillover to similar choices. J Consumer Behav. 2021;20:271–287. https://doi.org/10.1002/cb.1850 KUHN ET AL.287