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Human Behavior

The effectiveness of nudging: A meta-analysis of choice architecture interventions across behavioral domains – pnas.org

Significance

Changing individuals behavior is key to tackling some of todays most pressing societal challenges such as the COVID-19 pandemic or climate change. Choice architecture interventions aim to nudge people toward personally and socially desirable behavior through the design of choice environments. Although increasingly popular, little is known about the overall effectiveness of choice architecture interventions and the conditions under which they facilitate behavior change. Here we quantitatively review over a decade of research, showing that choice architecture interventions successfully promote behavior change across key behavioral domains, populations, and locations. Our findings offer insights into the effects of choice architecture and provide guidelines for behaviorally informed policy making.

Over the past decade, choice architecture interventions or so-called nudges have received widespread attention from both researchers and policy makers. Built on insights from the behavioral sciences, this class of behavioral interventions focuses on the design of choice environments that facilitate personally and socially desirable decisions without restricting people in their freedom of choice. Drawing on more than 200 studies reporting over 450 effect sizes (n = 2,149,683), we present a comprehensive analysis of the effectiveness of choice architecture interventions across techniques, behavioral domains, and contextual study characteristics. Our results show that choice architecture interventions overall promote behavior change with a small to medium effect size of Cohens d = 0.45 (95% CI [0.39, 0.52]). In addition, we find that the effectiveness of choice architecture interventions varies significantly as a function of technique and domain. Across behavioral domains, interventions that target the organization and structure of choice alternatives (decision structure) consistently outperform interventions that focus on the description of alternatives (decision information) or the reinforcement of behavioral intentions (decision assistance). Food choices are particularly responsive to choice architecture interventions, with effect sizes up to 2.5 times larger than those in other behavioral domains. Overall, choice architecture interventions affect behavior relatively independently of contextual study characteristics such as the geographical location or the target population of the intervention. Our analysis further reveals a moderate publication bias toward positive results in the literature. We end with a discussion of the implications of our findings for theory and behaviorally informed policy making.

Many of todays most pressing societal challenges such as the successful navigation of the COVID-19 pandemic or the mitigation of climate change call for substantial changes in individuals behavior. Whereas microeconomic and psychological approaches based on rational agent models have traditionally dominated the discussion about how to achieve behavior change, the release of Thaler and Sunsteins book NudgeImproving Decisions about Health, Wealth, and Happiness (1) widely introduced a complementary intervention approach known as choice architecture or nudging, which aims to change behavior by (re)designing the physical, social, or psychological environment in which people make decisions while preserving their freedom of choice (2). Since the publication of the first edition of Thaler and Sunstein (1) in 2008, choice architecture interventions have seen an immense increase in popularity (Fig. 1). However, little is known about their overall effectiveness and the conditions under which they facilitate behavior changea gap the present meta-analysis aims to address by analyzing the effects of the most widely used choice architecture techniques across key behavioral domains and contextual study characteristics.

Number of citations of Thaler and Sunstein (1) between 2008 and 2020. Counts are based on citation search in Web of Science.

Traditional microeconomic intervention approaches are often built around a rational agent model of decision making, which assumes that people base their decisions on known and consistent preferences that aim to maximize the utility, or value, of their actions. In determining their preferences, people are thought to engage in an exhaustive analysis of the probabilities and potential costs and benefits of all available options to identify which option provides the highest expected utility and is thus the most favorable (3). Interventions aiming to change behavior are accordingly designed to increase the utility of the desired option, either by educating people about the existing costs and benefits of a certain behavior or by creating entirely new incentive structures by means of subsidies, tax credits, fines, or similar economic measures. Likewise, traditional psychological intervention approaches explain behavior as the result of a deliberate decision making process that weighs and integrates internal representations of peoples belief structures, values, attitudes, and norms (4, 5). Interventions accordingly focus on measures such as information campaigns that aim to shift behavior through changes in peoples beliefs or attitudes (6).

Over the past years, intervention approaches informed by research in the behavioral sciences have emerged as a complement to rational agent-based approaches. They draw on an alternative model of decision making which acknowledges that people are bounded in their ability to make rational decisions. Rooted in dual-process theories of cognition and information processing (7), this model recognizes that human behavior is not always driven by the elaborate and rational thought processes assumed by the rational agent model but instead often relies on automatic and computationally less intensive forms of decision making that allow people to navigate the demands of everyday life in the face of limited time, available information, and computational power (8, 9). Boundedly rational decision makers often construct their preferences ad hoc based on cognitive shortcuts and biases, which makes them susceptible to supposedly irrational contextual influences, such as the way in which information is presented or structured (1012). This susceptibility to contextual factors, while seemingly detrimental to decision making, has been identified as a promising lever for behavior change because it offers the opportunity to influence peoples decisions through simple changes in the so-called choice architecture that defines the physical, social, and psychological context in which decisions are made (2). Rather than relying on education or significant economic incentives, choice architecture interventions aim to guide people toward personally and socially desirable behavior by designing environments that anticipate and integrate peoples limitations in decision making to facilitate access to decision-relevant information, support the evaluation and comparison of available choice alternatives, or reinforce previously formed behavioral intentions (13) (see Table 1 for an overview of intervention techniques based on choice architecture*).

Taxonomy of choice architecture categories and intervention techniques

Unlike the assumption of the rational agent model, people rarely have access to all relevant information when making a decision. Instead, they tend to base their decisions on information that is directly available to them at the moment of the decision (14, 15) and to discount or even ignore information that is too complex or meaningless to them (16, 17). Choice architecture interventions based on the provision of decision information aim to facilitate access to decision-relevant information by increasing its availability, comprehensibility, and/or personal relevance to the decision maker. One way to achieve this is to provide social reference information that reduces the ambiguity of a situation and helps overcome uncertainty about appropriate behavioral responses. In a natural field experiment with more than 600,000 US households, for instance, Allcott (18) demonstrated the effectiveness of descriptive social norms in promoting energy conservation. Specifically, the study showed that households which regularly received a letter comparing their own energy consumption to that of similar neighbors reduced their consumption by an average of 2%. This effect was estimated to be equivalent to that of a short-term electricity price increase of 11 to 20%. Other examples of decision information interventions include measures that increase the visibility of otherwise covert information (e.g., feedback devices and nutrition labels; refs. 19, 20), or that translate existing descriptions of choice options into more comprehensible or relevant information (e.g., through simplifying or reframing information; ref. 21).

Not only do people have limited access to decision-relevant information, but they often refrain from engaging in the elaborate cost-benefit analyses assumed by the rational agent model to evaluate and compare the expected utility of all choice options. Instead, they use contextual cues about the way in which choice alternatives are organized and structured within the decision environment to inform their behavior. Choice architecture interventions built around changes in the decision structure utilize this context dependency to influence behavior through the arrangement of choice alternatives or the format of decision making. One of the most prominent examples of this intervention approach is choice default, or the preselection of an option that is imposed if no active choice is made. In a study comparing organ donation policies across European countries, Johnson and Goldstein (22) demonstrated the impact of defaults on even highly consequential decisions, showing that in countries with presumed consent laws, which by default register individuals as organ donors, the rate of donor registrations was nearly 60 percentage points higher than in countries with explicit consent laws, which require individuals to formally agree to becoming an organ donor. Other examples of decision structure interventions include changes in the effort related to choosing an option (23), the range or composition of options (24), and the consequences attached to options (25).

Even if people make a deliberate and potentially rational decision to change their behavior, limited attentional capacities and a lack of self-control may prevent this decision from actually translating into the desired actions, a phenomenon described as the intentionbehavior gap (26). Choice architecture interventions that provide measures of decision assistance aim to bridge the intentionbehavior gap by reinforcing self-regulation. One example of this intervention approach are commitment devices, which are designed to strengthen self-control by removing psychological barriers such as procrastination and intertemporal discounting that often stand in the way of successful behavior change. Thaler and Benartzi (27) demonstrated the effectiveness of such commitment devices in a large-scale field study of the Save More Tomorrow program, showing that employees increased their average saving rates from 3.5 to 13.6% when committing in advance to allocating parts of their future salary increases toward retirement savings. If applied across the United States, this program was estimated to increase the total of annual retirement contributions by approximately $25 billion for each 1% increase in saving rates. Other examples of decision assistance interventions are reminders, which affect decision making by increasing the salience of the intended behavior (28).

Despite the growing interest in choice architecture, only a few attempts have been made to quantitatively integrate the empirical evidence on its effectiveness as a behavior change tool (2932). Previous studies have mostly been restricted to the analysis of a single choice architecture technique (3335) or a specific behavioral domain (3639), leaving important questions unanswered, including how effective choice architecture interventions overall are in changing behavior and whether there are systematic differences across choice architecture techniques and behavioral domains that so far may have remained undetected and that may offer new insights into the psychological mechanisms that drive choice architecture interventions.

The aim of the present meta-analysis was to address these questions by first quantifying the overall effect of choice architecture interventions on behavior and then providing a systematic comparison of choice architecture interventions across different techniques, behavioral domains, and contextual study characteristics to answer 1) whether some choice architecture techniques are more effective in changing behavior than others, 2) whether some behavioral domains are more receptive to the effects of choice architecture interventions than others, 3) whether choice architecture techniques differ in their effectiveness across varying behavioral domains, and finally, 4) whether the effectiveness of choice architecture interventions is impacted by contextual study characteristics such as the location or target population of the intervention. Drawing on an exhaustive literature search that yielded more than 200 published and unpublished studies, this comprehensive analysis presents important insights into the effects and potential boundary conditions of choice architecture interventions and provides an evidence-based guideline for selecting behaviorally informed intervention measures.

Our meta-analysis of 455 effect sizes from 214 publications (N = 2, 149, 683) revealed a statistically significant effect of choice architecture interventions on behavior (Cohens d=0.45, 95% CI [0.39, 0.52], t(340)=14.38, P < 0.001) (Fig. 2). Using conventional criteria, this effect can be classified to be of small to medium size (40). The effect size was reliable across several robustness checks, including the removal of influential outliers, which marginally decreased the overall size of the effect but did not change its statistical significance (d=0.42, 95% CI [0.37, 0.46], t(338)=17.06, P < 0.001). Additional leave-one-out analyses at the individual effect size level and the publication level found the effect of choice architecture interventions to be robust to the exclusion of any one effect size and publication, with d ranging from 0.43 to 0.46 and all P < 0.001.

Forest plot of all effect sizes (k = 455) included in the meta-analysis with their corresponding 95% confidence intervals. Extracted Cohens d values ranged from 0.69 to 4.69. The proportion of true to total variance was estimated at I2 = 99.67%. ***P<0.001.

The total heterogeneity was estimated to be 2=0.23, indicating considerable variability in the effect size of choice architecture interventions. More specifically, the dispersion of effect sizes suggests that while the majority of choice architecture interventions will successfully promote the desired behavior change with a small to large effect size, 15% of interventions are likely to backfire, i.e., reduce or even reverse the desired behavior, with a small to medium effect (95% prediction interval [0.48, 1.39]) (4042).

Visual inspection of the relation between effect sizes and their corresponding SEs (Fig. 3) revealed an asymmetric distribution that suggested a one-tailed overrepresentation of positive effect sizes in studies with comparatively low statistical power (43). This finding was formally confirmed by Eggers test (44), which found a positive association between effect sizes and SEs (b=2.28, 95% CI [1.31, 3.25], t(339)=4.61, P < 0.001). Together, these results point to a publication bias in the literature that may favor the reporting of successful as opposed to unsuccessful implementations of choice architecture interventions in studies with small sample sizes. Sensitivity analyses imposing a priori weight functions on a simplified random effects model suggested that this one-tailed publication bias could have potentially affected the estimate of our meta-analytic model (43). Assuming a moderate one-tailed publication bias in the literature attenuated the overall effect size of choice architecture interventions by 26.79% from Cohens d = 0.42, 95% CI [0.37, 0.46], and 2=0.20 (SE=0.02) to d=0.31 and 2=0.23. Assuming a severe one-tailed publication bias attenuated the overall effect size even further to d=0.03 and 2=0.34; however, this assumption was only partially supported by the funnel plot. Although our general conclusion about the effects of choice architecture interventions on behavior remains the same in the light of these findings, the true effect size of interventions is likely to be smaller than estimated by our meta-analytic model due to the overrepresentation of positive effect sizes in our sample.

Funnel plot displaying each observation as a function of its effect size and SE. In the absence of publication bias, observations should scatter symmetrically around the pooled effect size indicated by the gray vertical line and within the boundaries of the 95% confidence intervals shaded in white. The asymmetric distribution shown here indicates a one-tailed publication bias in the literature that favors the reporting of successful implementations of choice architecture interventions in studies with small sample sizes.

Supported by the high heterogeneity among effect sizes, we next tested the extent to which the effectiveness of choice architecture interventions was moderated by the type of intervention, the behavioral domain in which it was implemented, and contextual study characteristics.

Our first analysis focused on identifying potential differences between the effect sizes of decision information, decision structure, and decision assistance interventions. This analysis found that intervention category indeed moderated the effect of choice architecture interventions on behavior (F(3,337)=9.79, P < 0.001). With average effect sizes ranging from d=0.31 to 0.55, interventions across all three categories were effective in inducing statistically significant behavior change (all P<0.001; Fig. 4). Planned contrasts between categories, however, revealed that interventions in the decision structure category had a stronger effect on behavior compared to interventions in the decision information (b = 0.17, 95% CI [0.03, 0.31], t(337)=2.32, P = 0.02) and the decision assistance category (b=0.24, 95% CI [0.11, 0.36], t(337)=3.79, P < 0.001). No difference was found in the effectiveness of decision information and decision assistance interventions (b=0.07, 95% CI [0.19,0.05], t(337)=1.16, P = 0.25). Including intervention category as a moderator in our meta-analytic model marginally reduced the proportion of true to total variability in effect sizes from I2=99.67% to I2=99.57% (I(3)2=92.44%; I(2)2=7.13%; SI Appendix, Table S3).

Forest plot of effect sizes across categories of choice architecture intervention techniques (see Table 1 for more detailed description of techniques). The position of squares on the x axis indicates the effect size of each respective intervention technique. Bars indicate the 95% confidence intervals of effect sizes. The size of squares is inversely proportional to the SE of effect sizes. Diamond shapes indicate the average effect size and confidence intervals of intervention categories. The solid line represents an effect size of Cohens d = 0. The dotted line represents the overall effect size of choice architecture interventions, Cohens d = 0.45, 95% CI [0.39, 0.52]. Identical letter superscripts indicate statistically significant (P < 0.05) pairwise comparisons.

To test whether the effect sizes of the three intervention categories adequately represented differences on the underlying level of choice architecture techniques, we reran our analysis with intervention technique rather than category as the key moderator. As illustrated in Fig. 4, each of the nine intervention techniques was effective in inducing behavior change, with Cohens d ranging from 0.30 to 0.62 (all P < 0.01). Within intervention categories, techniques were generally consistent in their effect sizes (for all contrasts, P > 0.05). Between categories, however, techniques showed in parts substantial differences in effect sizes. In line with the previously reported results, techniques within the decision structure category were consistently stronger in their effects on behavior than intervention techniques within the decision information or the decision assistance category. The observed effect size differences between the decision information, the decision structure, and the decision assistance category were thus unlikely to be driven by a single intervention technique but rather representative of the entire set of techniques within those categories.

Following our analysis of the effectiveness of varying types of choice architecture interventions, we next focused on identifying potential differences among the behavioral domains in which interventions were implemented. As illustrated in Fig. 5, effect sizes varied quite substantially across domains, with Cohens d ranging from 0.25 to 0.72. Our analysis confirmed that the effectiveness of interventions was moderated by domain (F(6,334)=4.62, P < 0.001). Specifically, it showed that choice architecture interventions, while generally effective in inducing behavior change across all six domains, had a particularly strong effect on behavior in the food domain, with d=0.72 (95% CI [0.49, 0.95]). No other domain showed comparably large effect sizes (for all contrasts, P < 0.05). The smallest effects were observed in the financial domain. With an average intervention effect of d = 0.25 (95% CI [0.12, 0.37]), this domain was less receptive to choice architecture interventions than the other behavioral domains we investigated. Introducing behavioral domain as a moderator in our meta-analytic model marginally reduced the ratio of true to total heterogeneity among effect sizes from I2=99.67% to I2=99.58% (I(3)2=94.56%; I(2)2=5.02%; SI Appendix, Table S3).

Forest plot of effect sizes across categories of choice architecture interventions and behavioral domains. The position of squares on the x axis indicates the effect size of each intervention category within a behavioral domain. Bars indicate the 95% confidence intervals of effect sizes. The size of squares is inversely proportional to the SE of effect sizes. Diamond shapes indicate the overall effect size and confidence intervals of choice architecture interventions within a behavioral domain. The solid line represents an effect size of Cohens d = 0. The dotted line represents the overall effect size of choice architecture interventions, Cohens d = 0.45, 95% CI [0.39, 0.52]. Identical letter superscripts indicate statistically significant (P < 0.05) pairwise comparisons.

Comparing the effectiveness of decision information, decision structure, and decision assistance interventions across domains consistently showed interventions within the decision structure category to have the largest effect on behavior, with Cohens d ranging from 0.33 to 0.86 (Fig. 5). This result suggests that the observed effect size differences between the three categories of choice architecture interventions were relatively stable and independent from the behavioral domain in which interventions were applied. Including the interaction of intervention category and behavioral domain in our meta-analytic model reduced the proportion of true to total effect size variability from I2=99.67% to I2=99.52% (I(3)2=91.86%; I(2)2=7.67%; SI Appendix, Table S3).

Last, we were interested in the extent to which the effect size of choice architecture interventions was moderated by contextual study characteristics, such as the location of the intervention (inside vs. outside of the United States), the target population of the intervention (adults vs. children and adolescents), the experimental setting in which the intervention was investigated (conventional laboratory experiment, artifactual field experiment, framed field experiment, or natural field experiment; ref. 45), and the year in which the data were published. As can be seen in Table 2, choice architecture interventions affected behavior relatively independently of contextual influences since neither location nor target population had a statistically significant impact on the effect size of interventions. In support of the external validity of behavioral measures, our analysis moreover did not find any difference in the effect size of different types of experiments. Only year of publication predicted the effect of interventions on behavior, with more recent publications reporting smaller effect sizes than older publications.

Parameter estimates of three-level meta-analytic models showing the overall effect size of choice architecture interventions as well as effect sizes across categories, techniques, behavioral domains, and contextual study characteristics

Changing individuals behavior is key to solving some of todays most pressing societal challenges. However, how can this behavior change be achieved? Recently, more and more researchers and policy makers have approached this question through the use of choice architecture interventions. The present meta-analysis integrates over a decades worth of research to shed light on the effectiveness of choice architecture and the conditions under which it facilitates behavior change. Our results show that choice architecture interventions promote behavior change with a small to medium effect size of Cohens d = 0.45, which is comparable to more traditional intervention approaches like education campaigns or financial incentives (4648). Our findings are largely consistent with those of previous analyses that investigated the effectiveness of choice architecture interventions in a smaller subset of the literature (e.g., refs. 29, 30, 32, 33). In their recent meta-analysis of choice architecture interventions across academic disciplines, Beshears and Kosowksy (30), for example, found that choice architecture interventions had an average effect size of d=0.41. Similarly, focusing on one choice architecture technique only, Jachimowicz et al. (33) found that choice defaults had an average effect size of d=0.68, which is slightly higher than the effect size our analysis revealed for this intervention technique (d = 0.62). Our results suggest a somewhat higher overall effectiveness of choice architecture interventions than meta-analyses that have focused exclusively on field experimental research (31, 37), a discrepancy that holds even when accounting for differences between experimental settings (45). This inconsistency in findings may in part be explained by differences in metaanalytic samples. Only 7% of the studies analyzed by DellaVigna and Linos (31), for example, meet the strict inclusion and exclusion criteria of the present meta-analysis. Among others, these criteria excluded studies that combined multiple choice architecture techniques. While this restriction allowed us to isolate the unique effect of each individual intervention technique, it may conflict with the reality of field experimental research that often requires researchers to leverage the effects of several choice architecture techniques to address the specific behavioral challenge at hand (see Materials and Methods for details on the literature search process and inclusion criteria). Similarly, the techniques that are available to field experimental researchers may not always align with the underlying psychological barriers to the target behavior (Table 1), decreasing their effectiveness in encouraging the desired behavior change.

Not only does choice architecture facilitate behavior change, but according to our results, it does so across a wide range of behavioral domains, population segments, and geographical locations. In contrast to theoretical and empirical work challenging its effectiveness (4951), choice architecture constitutes a versatile intervention approach that lends itself as an effective behavior change tool across many contexts and policy areas. Although the present meta-analysis focuses on studies that tested the effects of choice architecture alone, the applicability of choice architecture is not restricted to stand-alone interventions but extends to hybrid policy measures that use choice architecture as a complement to more traditional intervention approaches (52). Previous research, for example, has shown that the impact of economic interventions such as taxes or financial incentives can be enhanced through choice architecture (5355).

In addition to the overall effect size of choice architecture interventions, our systematic comparison of interventions across different techniques, behavioral domains, and contextual study characteristics reveals substantial variations in the effectiveness of choice architecture as a behavior change tool. Most notably, we find that across behavioral domains, decision structure interventions that modify decision environments to address decision makers limited capacity to evaluate and compare choice options are consistently more effective in changing behavior than decision information interventions that address decision makers limited access to decision-relevant information or decision assistance interventions that address decision makers limited attention and self-control. This relative advantage of structural choice architecture techniques may be due to the specific psychological mechanisms that underlie the different intervention techniques or, more specifically, their demands on information processing. Decision information and decision assistance interventions rely on relatively elaborate forms of information processing in that the information and assistance they provide needs to be encoded and evaluated in terms of personal values and/or goals to determine the overall utility of a given choice option (56). Decision structure interventions, by contrast, often do not require this type of information processing but provide a general utility boost for specific choice options that offers a cognitive shortcut for determining the most desirable option (57, 58). Accordingly, decision information and decision assistance interventions have previously been described as attempts to facilitate more deliberate decision making processes, whereas decision structure interventions have been characterized as attempts to advance more automatic decision making processes (59). Decision information and decision assistance interventions may thus more frequently fail to induce behavior change and show overall smaller effect sizes than decision structure interventions because they may exceed peoples cognitive limits in decision making more often, especially in situations of high cognitive load or time pressure.

The engagement of internal value and goal representations by decision information and decision assistance interventions introduces a second factor that may impact their effectiveness to change behavior: the moderating influence of individual differences. Nutrition labels, a prominent example of decision information interventions, for instance, have been shown to be more frequently used by consumers who are concerned about their diet and overall health than consumers who do not share those concerns (60). By targeting only certain population segments, information and assistance-based choice architecture interventions may show an overall smaller effect size when assessed at the population level compared to structure-based interventions, which rely less on individual values and goals and may therefore have an overall larger impact across the whole population. From a practical perspective, this suggests that policy makers who wish to use choice architecture as a behavioral intervention measure may need to precede decision information and decision assistance interventions by an assessment and analysis of the values and goals of the target population or, alternatively, choose a decision structure approach in cases when a segmentation of the population in terms of individual differences is not possible.

In summary, the higher effectiveness of decision structure interventions may potentially be explained by a combination of two factors: 1) lower demand on information processing and 2) lower susceptibility to individual differences in values and goals. Our explanation remains somewhat speculative, however, as empirical research especially on the cognitive processes underlying choice architecture interventions is still relatively scarce (but see refs. 53, 56, 57). More research efforts are needed to clarify the psychological mechanisms that drive the impact of choice architecture interventions and determine their effectiveness in changing behavior.

Besides the effect size variations between different categories of choice architecture techniques, our results reveal considerable differences in the effectiveness of choice architecture interventions across behavioral domains. Specifically, we find that choice architecture interventions had a particularly strong effect on behavior in the food domain, with average effect sizes up to 2.5 times larger than those in the health, environmental, financial, prosocial, or other behavioral domain. A key characteristic of food choices and other food-related behaviors is the fact that they bear relatively low behavioral costs and few, if any, perceived long-term consequences for the decision maker. Previous research has found that the potential impact of a decision can indeed moderate the effectiveness of choice architecture interventions, with techniques such as gain and loss framing having a smaller effect on behavior when the decision at hand has a high, direct impact on the decision maker than when the decision has little to no impact (61). Consistent with this research, we observe not only the largest effect sizes of choice architecture interventions in the food domain but also the overall smallest effect sizes of interventions in the financial domain, a domain that predominantly represents decisions of high impact to the decision maker. This systematic variation of effect sizes across behavioral domains suggests that when making decisions that are perceived to have a substantial impact on their lives, people may be less prone to the influence of automatic biases and heuristics, and thus the effects of choice architecture interventions, than when making decisions of comparatively smaller impact.

Another characteristic of food choices that may explain the high effectiveness of choice architecture interventions in the food domain is the fact that they are often driven by habits. Commonly defined as highly automatized behavioral responses to cues in the choice environment, habits distinguish themselves from other behaviors through a particularly strong association between behavior on the one hand and choice environment on the other hand (62, 63). It is possible that choice architecture interventions benefit from this association to the extent that they target the choice environment and thus potentially alter triggers of habitualized, undesirable behaviors. To illustrate, previous research has shown that people tend to adjust their food consumption relative to portion size, meaning that they consume more when presented with large portions and less when presented with small portions (39). Here portion size acts as an environmental cue that triggers and guides the behavioral response to eat. Choice architecture interventions that target this environmental cue, for example, by changing the default size of a food portion, are likely to be successful in changing the amount of food people consume because they capitalize on the highly automatized association between portion size and food consumption. The congruence between factors that trigger habitualized behaviors and factors that are targeted by choice architecture interventions may not only explain why interventions in our sample were so effective in changing food choices but more generally indicate that choice architecture interventions are an effective tool for changing instances of habitualized behaviors (64). This finding is particularly relevant from a policy making perspective as habits tend to be relatively unresponsive to traditional intervention approaches and are therefore generally considered to be difficult to change (62). Given that choice architecture interventions can only target the environmental cues that trigger habitualized responses but not the association between choice environment and behavior per se, it should be noted though that the effects of interventions are likely limited to the specific choice contexts in which they are implemented.

While the present meta-analysis provides a comprehensive overview of the effectiveness of choice architecture as a behavior change tool, more research is needed to complement and complete our findings. For example, our methodological focus on individuals as the unit of analysis excludes a large number of studies that have investigated choice architecture interventions on broader levels, such as households, school classes, or organizations, which may reduce the generalizability of our results. Future research should target these studies specifically to add to the current analysis. Similarly, our data show very high levels of heterogeneity among the effect sizes of choice architecture interventions. Although the type of intervention, the behavioral domain in which it is applied, and contextual study characteristics account for some of this heterogeneity (SI Appendix, Table S3), more research is needed to identify factors that may explain the variability in effect sizes above and beyond those investigated here. Research has recently started to reveal some of those potential moderators of choice architecture interventions, including sociodemographic factors such as income and socioeconomic status as well as psychological factors such as domain knowledge, numerical ability, and attitudes (6567). Investigating these moderators systematically cannot only provide a more nuanced understanding of the conditions under which choice architecture facilitates behavior change but may also help to inform the design and implementation of targeted interventions that take into account individual differences in the susceptibility to choice architecture interventions (68). Ethical considerations should play a prominent role in this process to ensure that potentially more susceptible populations, such as children or low-income households, retain their ability to make decisions that are in their personal best interest (66, 69, 70). Based on the results of our own moderator analyses, additional avenues for future research may include the study of how information processing influences the effectiveness of varying types of choice architecture interventions and how the overall effect of interventions is determined by the type of behavior they target (e.g., high-impact vs. low-impact behaviors and habitual vs. one-time decisions). In addition, we identified a moderate publication bias toward the reporting of effect sizes that support a positive effect of choice architecture interventions on behavior. Future research efforts should take this finding into account and place special emphasis on appropriate sample size planning and analysis standards when evaluating choice architecture interventions. Finally, given our choice to focus our primary literature search on the terms choice architecture and nudge, we recognize that the present meta-analysis may have failed to capture parts of the literature published before the popularization of this now widely used terminology, despite our efforts to expand the search beyond those terms (for details on the literature search process, see Materials and Methods). Due to the large increase in choice architecture research over the past decade (Fig. 1), however, the results presented here likely offer a good representation of the existing evidence on the effectiveness of choice architecture in changing individuals behavior.

Few behavioral intervention measures have lately received as much attention from researchers and policy makers as choice architecture interventions. Integrating the results of more than 450 behavioral interventions, the present meta-analysis finds that choice architecture is an effective and widely applicable behavior change tool that facilitates personally and socially desirable choices across behavioral domains, geographical locations, and populations. Our results provide insights into the overall effectiveness of choice architecture interventions as well as systematic effect size variations among them, revealing promising directions for future research that may facilitate the development of theories in this still new but fast-growing field of research. Our work also provides a comprehensive overview of the effectiveness of choice architecture interventions across a wide range of intervention contexts that are representative of some of the most pressing societal challenges we are currently facing. This overview can serve as a guideline for policy makers who seek reliable, evidence-based information on the potential impact of choice architecture interventions and the conditions under which they promote behavior change.

The meta-analysis was conducted in accordance with guidelines for conducting systematic reviews (71) and conforms to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (72) standards.

We searched the electronic databases PsycINFO, PubMed, PubPsych, and ScienceDirect using a combination of keywords associated with choice architecture (nudge OR choice architecture) and empirical research (method* OR empiric* OR procedure OR design). Since the terms nudge and choice architecture were established only after the seminal book by Thaler and Sunstein (1), we restricted this search to studies that were published no earlier than 2008. To compensate for the potential bias this temporal restriction might introduce to the results of our meta-analysis, we identified additional studies, including studies published before 2008, through the reference lists of relevant review articles and a search for research reports by governmental and nongovernmental behavioral science units. To reduce the possibly confounding effects of publication status on the estimation of effect sizes, we further searched for unpublished studies using the ProQuest Dissertations & Theses database and requesting unpublished data through academic mailing lists. The search concluded in June 2019, yielding a total of 9,606 unique publications.

Given the exceptionally high heterogeneity in choice architecture research, we restricted our meta-analysis to studies that 1) empirically tested one or more choice architecture techniques using a randomized controlled experimental design, 2) had a behavioral outcome measure that was assessed in a real-life or hypothetical choice situation, 3) used individuals as the unit of analysis, and 4) were published in English. Studies that examined choice architecture in combination with other intervention measures, such as significant economic incentives or education programs, were excluded from our analyses to isolate the unique effects of choice architecture interventions on behavior.

The final sample comprised 455 effect sizes from 214 publications with a pooled sample size of 2,149,683 participants (N ranging from 14 to 813,990). SI Appendix, Fig. S1 illustrates the literature search and review process. All meta-analytic data and analyses reported in this paper are publicly available on the Open Science Framework (https://osf.io/fywae/) (74).

Due to the large variation in behavioral outcome measures, we calculated Cohens d (40) for a standardized effect size measure of the mean difference between control and treatment conditions. Positive Cohens d values were coded to reflect behavior change in the desired direction of the intervention, whereas negative values reflected an undesirable change in behavior.

To categorize systematic differences between choice architecture interventions, we coded studies for seven moderators describing the type of intervention, the behavioral domain in which it was implemented, and contextual study characteristics. The type of choice architecture intervention was classified using a taxonomy developed by Mnscher and colleagues (13), which distinguishes three broad categories of choice architecture: decision information, decision structure, and decision assistance. Each of these categories targets a specific aspect of the choice environment, with decision information interventions targeting the way in which choice alternatives are described (e.g., framing), decision structure interventions targeting the way in which those choice alternatives are organized and structured (e.g., choice defaults), and decision assistance interventions targeting the way in which decisions can be reinforced (e.g., commitment devices). With its tripartite categorization framework the taxonomy is able to capture and categorize the vast majority of choice architecture interventions described in the literature, making it one of the most comprehensive classification schemes of choice architecture techniques in the field (see Table 1 for an overview). Many alternative attempts to organize and structure choice architecture interventions are considered problematic because they combine descriptive categorization approaches, which classify interventions based on choice architecture technique, and explanatory categorization approaches, which classify interventions based on underlying psychological mechanisms, within a single framework. The taxonomy we use here adopts a descriptive categorization approach in that it organizes interventions exclusively in terms of choice architecture techniques. We chose this approach to not only omit common shortcomings of hybrid classification schemes, such as a reduction in the interpretability of results, but also to warrant a highly reliable categorization of interventions in the absence of psychological outcome measures that would allow us to infer explanatory mechanisms. Using a descriptive categorization approach further allowed us to generate theoretically meaningful insights that can be easily translated into concrete recommendations for policy making. Each intervention was coded according to its specific technique and corresponding category. Interventions that combined multiple choice architecture techniques were excluded from our analyses to isolate the unique effect of each approach. Based on previous reviews (73) and inspection of our data, we distinguished six behavioral domains: health, food, environment, finance, prosocial behavior, and other behavior. Contextual study characteristics included the type of experiment that had been conducted (conventional laboratory experiment, artifactual field experiment, framed field experiment, or natural field experiment), the location of the intervention (inside vs. outside of the United States), the target population of the intervention (adults vs. children and adolescents), and the year in which the data were published. Interrater reliability across a random sample of 20% of the publications was high, with Cohens ranging from 0.76 to 1 (M=0.87).

We estimated the overall effect of choice architecture interventions using a three-level meta-analytic model with random effects on the treatment and the publication level. This approach allowed us to account for the hierarchical structure of our data due to publications that reported multiple relevant outcome variables and/or more than one experiment (7577). To further account for dependency in sampling errors due to overlapping samples (e.g., in cases where multiple treatment conditions were compared to the same control condition), we computed cluster-robust SEs, confidence intervals, and statistical tests for the estimated effect sizes (78, 79).

To identify systematic differences between choice architecture interventions, we ran multiple moderator analyses in which we tested for the effects of type of intervention, behavioral domain, and study characteristics using mixed-effects meta-analytic models with random effects on the treatment and the publication level. All analyses were conducted in R using the package metafor (80).

This research was supported by Swiss National Science Foundation Grant PYAPP1_160571 awarded to Tobias Brosch and Swiss Federal Office of Energy Grant SI/501597-01. It is part of the activities of the Swiss Competence Center for Energy Research Competence Center for Research in Energy, Society and Transition, supported by the Swiss Innovation Agency (Innosuisse). The funding sources had no involvement in the preparation of the article; in the study design; in the collection, analysis, and interpretation of data; nor in the writing of the manuscript. We thank Allegra Mulas and Laura Pagel for their assistance in data collection and extraction.

Author contributions: S.M., M.H., U.J.J.H., and T.B. designed research; S.M. and M.H. performed research; S.M. analyzed data; and S.M., M.H., U.J.J.H., and T.B. wrote the paper.

The authors declare no competing interest.

This article is a PNAS Direct Submission.

This article contains supporting information online at https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.2107346118/-/DCSupplemental.

*While alternative classification schemes of choice architecture interventions can be found in the literature, the taxonomy used in the present meta-analysis distinguishes itself through its comprehensiveness, which makes it a highly reliable categorization tool and allows for inferences of both theoretical and practical relevance.

Please note that our results are robust to the exclusion of nonretracted studies by the Cornell Food and Brand Laboratory which has been criticized for repeated scientific misconduct; retracted studies by this research group were excluded from the meta-analysis.

Search terms were adapted from Szaszi et al. (73).

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The effectiveness of nudging: A meta-analysis of choice architecture interventions across behavioral domains - pnas.org

Human Behavior

Ron Johnson announces run for third Senate term in Wisconsin | TheHill – The Hill

Sen. Ron JohnsonRonald (Ron) Harold JohnsonSenate Democrats release first TV attack ad against Johnson in Wisconsin The Hill's Morning Report - Voting rights takes center stage for Democrats Rebecca Kleefisch raises .3 million in Wisconsin gubernatorial bid MORE (R-Wis.) announcedSunday he will run for reelectionin November, setting up a high-stakes Senate battle in a key swing state.

Johnsons decision to run for a third term breaks a vow he made in his 2016 campaign that hed seek only two six-year stints in the Senate. However, he had increasingly sent signals that he planned to run againin November, maintaining his fundraising and making frequent appearances on Fox News.

In a statement and an op-ed in The Wall Street Journal, Johnson said he would prefer to retire but cast his decision to run for reelection as one made to fight against Democrats' unified control in Washington and "disastrous policies."

"During the 2016 campaign, I said it would be my last campaign and final term. That was my strong preference, and my wifeswe both looked forward to a normal private life. Neither of us anticipated the Democrats complete takeover of government and the disastrous policies they have already inflicted on America and the world, to say nothing of those they threaten to enact in the future," he wrote in the Journal.

Johnson also forecast a fierce campaign, warning that Democrats would attack him with language offering a nod to top culture war issues for the GOP.

"Tens of millions of dollars will be spent trying to destroy and defeat me. The mainstream media and Big Tech will contribute their powerful and corrupt voices as the unofficial but reliable communication apparatus of the Democrats. We face powerful forces that desire even more power and control over our lives. Their path, paved with false hope and greater dependency, always leads to tyranny. We cannot let them win," he wrote.

The announcement comesagainst the backdrop of what is anticipated to be a fearsome battle for the Senate, which is currently divided 50-50. Any one seat could decide which party controls the upper chamber come 2023, and both Republicans and Democrats have clamored for Johnson to make his bid official.

Republicans boast that Johnson is their strongest candidate in the Badger State. He unseated former Sen. Russ Feingold (D) in the Tea Party wave in 2010, overcoming a titan in Wisconsin politics despite being left for dead. He was similarly abandoned by the party establishment in 2016, when he unexpectedly fended off Feingold in a rematch, a victory GOP operatives suggest highlights a unique connection Johnson enjoys with the Wisconsin electorate.

Johnson is anticipated to cruise to the nomination, another advantage in addition to his already high name recognition as Democrats battle it out in a crowded primary.

However, Democrats too have clamored for Johnson to run again.

The urge runs counter to conventional wisdom, which contends that open seats are typically easier to flip than going against a sitting incumbent with an existing campaign bank account and large name recognition. But party operatives point to outlandish comments on the coronavirus, 2020 election, racial justice protests and more and view him as vulnerable.

Johnson has been dogged by criticism of provocative remarks since 2010, when he said sunspots were more likely to contribute to climate change than human behavior.

More recently, however, he has commented that mouthwash has been proven to kill the coronavirus and questioned the point of vaccines if fully vaccinated individuals can still catch COVID-19.

He has also been a vocal proponent of an election audit in Wisconsin and praised rioters who stormed the capitol on Jan. 6, 2021, as people who love this country.

Those comments and more are expected to be featured heavily in attack ads by Democrats who are chomping at the bit to take on the two-term senator.

Democrats also note that Johnson is closely allied with former President TrumpDonald TrumpGeorgia prosecutor says decision on Trump election interference case likely coming soon Overnight Defense & National Security US, Russia have face-to-face sit down Hillicon Valley Dems press privacy groups over kids' safety MORE, who narrowly lost Wisconsin in 2020.

Ron Johnson is what you get when QAnon and the Tea Party have a baby. And I hope that he does run. His candidacy makes the race far more competitive for Democrats, Wisconsin Democratic consultant Ben Nuckels told The Hill earlier this month.

Among the Democrats running in the race are Lt. Gov. Mandela Barnes, state Treasurer Sarah Godlewski, Milwaukee Bucks executive Alex Lasry and Outagamie County Executive Tom Nelson.

Democrats pounced on Johnson's announcement, noting his previous two-term pledge and casting the senator, who is independently wealthy, of looking out for his own interests over those of Wisconsinites.

"The only people celebrating Ron Johnsons announcement are his donors and the corporate special interest groups hes bailed out time and time again," Barnes said in a statement. "Lets get to work and retire this failed senator."

"Ron Johnson has spent the last decade catering to the ultra-wealthy millionaires and corporate interests who fund his campaign," Nelson added in his own statement. "Wisconsin needs a Senator that will promote Main Street solutions to our rigged economy, not another millionaire or billionaire."

Wisconsin for decades has been characterized by razor-thin margins in statewide contests and has been a top battleground for several consecutive cycles. Trump barely won the state in 2016 before losing it by a similarly tight margin in 2020. Wisconsin will also host a competitive gubernatorial election this year as Gov. Tony EversTony EversRebecca Kleefisch raises .3 million in Wisconsin gubernatorial bid Ron Johnson announces run for third Senate term in Wisconsin Ex-Rep. Duffy rejects Trump entreaties, won't run for Wisconsin governor MORE (D) fights for a second term.

Updated 11:00 a.m.

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Ron Johnson announces run for third Senate term in Wisconsin | TheHill - The Hill

Human Behavior

Science skepticism appears to be an important predictor of non-compliance with COVID-19 shelter-in-place policies – PsyPost

Attitudes about science were associated with compliance with shelter-in-place policies during the initial stages of the COVID-19 pandemic in the United States, according to research that analyzed anonymous cell phone location data. The study indicates that regions where people are more skeptical of science tend to adhere less strictly to stay-at-home orders. The findings have been published in Nature Human Behaviour.

Following the outbreak of COVID-19, social scientists quickly became interested in studying factors that impact compliance with government policies that mandate physical distancing.

Since we are from economics and public policy backgrounds, we were naturally interested in studying individual behavior in response to public policy in the context of the COVID-19 pandemic. We got the idea for the paper when state governments across the U.S. started introducing shelter-in-place policies in a staggered fashion in March 2020, said study author David Van Dijcke, a PhD student at the University of Michigan.

We realized we could use the variation in the timing of when those policies were introduced to trace out their effects. Since it was apparent to us that non-compliance with the policies people not staying at home would be an important issue for their efficacy, we started thinking about what might affect such non-compliance. While we look at science skepticism, other studies have found important roles for partisanship and poverty as well.

Around the same time, we stumbled across SafeGraph, the company that provided us with the anonymized mobile device data that we used to estimate the extent to which people were staying home, Van Dijcke said.

The researchers measured responses to the shelter-in-place policies at the county level by analyzing location data from more than 40 million mobile devices across the United States.

Van Dijcke and his team used data from a previous study on climate change opinions, which aggregated data from 12 nationally representative surveys, to assess science skepticism. The surveys included responses from 12,061 individuals in total and the data were used to estimate the percentage of people per county who agreed with the statement that global warming is caused by humans.

Because of the lack of granular geographic data on science skepticism, we used belief in anthropogenic (human-made) global warming as a proxy for science skepticism and validated this measure by benchmarking it against measures of science skepticism from other, smaller-scale datasets, Van Dijcke explained.

Those other datasets included the American Values Survey, which asks respondents the extent to which they agree with the statement I am worried that science is going too far and is hurting society rather than helping it, and the World Values Survey, which includes survey items such as We depend too much on science and not enough on faith.

The researchers found that the proportion of people who stayed at home after shelter-in-place policies went into effect tended to be higher in counties with lower levels of skepticism compared to counties with higher levels of science skepticism.

Previous research has found that shelter-in-place policies tended to be less effective in regions with a greater share of Donald Trump voters. But Van Dijcke and his colleagues found that their results held even after controlling for political partisanship.

The main takeaway is that whether or not people stayed at home during the first COVID-19 lockdowns in the States depended to a significant extent on whether they were skeptical about science, Van Dijcke told PsyPost. That is the case irrespective of peoples political affiliation, income, education, etc. We also find some evidence that science skepticism undermined compliance with other public health interventions during the pandemic, such as mask-wearing and vaccination. We think these are important findings since they underline the importance of science education and communication, as well as the danger of misinformation about these topics.

A caveat to our study is that it applies to the United States during the first wave of the pandemic, and thus may not be generalizable beyond that setting, Van Dijcke noted. The study examined the proportion of people who stayed at home between March 1 and April 19, 2020.

But the most important limitation of the study is the fact that the researchers had to rely on belief in anthropogenic global warming as their primary measure of science skepticism.

An obvious lacuna to fill is the availability of large-scale, representative data on science skepticism that can be mapped to granular geographies in the United States such as counties, Van Dijcke said. I think the pandemic has forcefully demonstrated how detrimental science skepticism can be to the implementation of public policy. Such data would open the way for a large array of additional questions regarding science skepticism to be studied, since researchers could link it to any other data available at the county level, most prominently Census data.

However, the results are in line with another study published in Nature Human Behaviour, which found that people with lower levels of trust in doctors, scientists, economists, professors, and experts were less likely to engage in behaviors intended to prevent the spread of COVID-19.

The study, Science skepticism reduced compliance with COVID-19 shelter-in-place policies in the United States, was authored by Adam Brzezinski, Valentin Kecht, David Van Dijcke, and Austin L. Wright.

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Science skepticism appears to be an important predictor of non-compliance with COVID-19 shelter-in-place policies - PsyPost

Human Behavior

SCAD | School of Business Innovation – Business Wire

SAVANNAH, Ga.--(BUSINESS WIRE)--The Savannah College of Art and Design launches the SCAD School of Business Innovation, which strategically incorporates a diverse array of top-ranked academic programs focused on preparing creative professionals to lead transformative change across key industries.

Bolstering SCADs international reputation as the preeminent source of knowledge in the disciplines it teaches, the school offers 15 graduate and undergraduate degrees in advertising and branding, business of beauty and fragrance, creative business leadership, design management, luxury and brand management, service design, and social strategy and management.

For more than 40 years, SCAD has continually reinvented itself in service of our mission to prepare students for creative professions, always and forever focused on the future, said SCAD President and Founder Paula Wallace. We changed the game for R&D with SCADpro, our innovation studio where students work directly with the worlds most valuable brands from Amazon and Google to Delta, Deloitte, HP, and Capital One. SCADs buoyant partnerships with the professions are why SCAD grads have enjoyed a 99% employment rate for the last four years straight. And now, to ensure the continued elite career preparation of tomorrows leaders in every sector of the global economy, SCAD invents again. Im so pleased to announce the formation of the SCAD School of Business Innovation.

The SCAD School of Business Innovation prepares the next generation of creative leaders to navigate the rapidly changing business landscape through in-depth industry knowledge, design thinking, research, and collaboration. With curriculum focused on the fundamentals of business design and economics, quantitative insights, global supply chain management, lifecycle marketing, brand acceleration, social analytics, and more, the schools premier degree programs empower students to become forward-thinking subject matter experts who will deliver transformative innovation to businesses.

Academic leaders

Victor Ermoli

The School of Business Innovation is led by Dean Victor Ermoli. Also overseeing the School of Design, Dean Ermoli has been with SCAD for more than two decades and has led the curriculum design of several programs in both schools. Ermoli has been named one of the 25 Most Admired Educators in America by DesignIntelligence, holds undergraduate and graduate industrial design degrees, and leads the new school through the lens of design and entrepreneurship. In addition to patents in the U.S. and Canada, and more than 30 years of design experience, Dean Ermoli led studio classes where his students designed products for Coca-Cola, Fossil, Pentair, Dell Computers, and many more prestigious companies.

Meloney Moore

The School of Business Innovation is also led by Associate Dean Meloney Moore, who held executive and management leadership roles in companies including Este Lauder, Liz Claiborne, and Toys R Us. Moore, who also leads the SCAD business of beauty and fragrance program, holds undergraduate and graduate business administration degrees and brings brand-oriented, global business perspective to the school leadership.

Jon Denham

Jon Denham is a visionary in the business industry with extensive experience building brand identities and delivering billion-dollar growth for leading global companies such as Procter & Gamble and Kraft Foods. Most recently, Denham served as the strategy and account vice president for Lextant Corporation, where he worked with clients such as Pfizer, Clorox, and SC Johnson.

Alessandro Cannata

Alessandro Cannata worked for more than a decade in the luxury sector across three continents in executive-level positions and with a focus on business development and communication. Serving as director of sales for companies such as Boglioli, Isaia, and Sutor Mantellassi in Milan, Cannata is an expert in luxury branding and consumer behavior. He holds two terminal degrees from leading European business schools: ESSEC Business School in Paris and Singapore and Universita Commerciale Luigi Bocconi in Milan.

Christopher Peeler

Peabody and Emmy Award winner Christopher Peeler joined SCAD in 2020 after serving as a senior producer and senior director of video news and programming at CNN and CNN Digital, where he grew the CNN Digital audience by more than 400% over a five-year period and expanded coverage from 17 hours to 24 hours per day. At CNN Digital, Peeler led digital content strategies for a portfolio of international digital products that drove more than $6 billion in annual video views and $90 million in revenue. Prior to CNN, Peeler was an executive producer at Sony Pictures Entertainment, where he achieved the networks highest show rating in 2005 with Games Across America.

Oscar Betancur

Before joining SCAD in 2012, Oscar Betancur worked as VP and associate creative director at The Star Group and has won multiple advertising awards. Betancurs client experience includes Warner Brothers Music, Philadelphia Museum of Art, Campbells, Johnson & Johnson, Mattel, and Tyco. As associate chair of social strategy and management at SCAD, Betancur brings a multidisciplined approach with his background in advertising, graphic design, motion media design, and fashion marketing and management.

Programs

Advertising and branding

SCAD advertising and branding students research, create, and deploy brand messaging that boosts engagement, drives action, and wins prestigious accolades like National ADDY awards and more. Guided by SCAD faculty, students have contributed to major campaigns for companies like Comcast, Chase Bank, Kodak, and Nintendo, and upon graduation, work for the worlds top agencies; the program features a 100% alumni employment rate. By mastering cutting-edge resources like game engines and augmented and virtual reality equipment to create their own branded experiences, students graduate as versatile, multiplatform storytellers prepared for career paths in emerging creative technology.

Business of beauty and fragrance

Beauty and fragrance power a $530-billion industry. In the SCAD business of beauty and fragrance program, students graduate with a globally minded, business-centric degree that lands jobs at top brands. Led by faculty from powerhouses like Este Lauder Companies and mentored by celebrated guests from international companies like LOral as well as boutique lines, SCAD students gain an in-depth understanding of the beauty industry grounded in future-forward marketing techniques, product development, branding packaging, and entrepreneurship. The program culminates with the development of a unique beauty brand or product and launch strategy.

Creative business leadership

Facing constant transformation, the most established businesses seek creative leaders to reimagine their services, products, strategy, and operations. Enter SCAD creative business leadership. In this one-year M.A. program that complements all SCAD undergraduate degrees, SCAD students transform into entrepreneurs prepared to run successful businesses or intrapreneurs who promote corporate innovation within existing organizations thanks to SCADpro collaborations with Fortune 500 brands and mentorship by visionaries at Tiffany and Co., Samsung, Clayco, and more. Students use simulation software to mimic the multifactor, high stakes decision-making scenarios CEOs face, and to understand market fluctuations and the challenges of raising capital.

Design management

SCAD, the worlds premier site for the study and practice of design thinking, is a living laboratory for the application of design management a discipline that empowers companies to spark innovation and think and act like designers. Design management students gain experience that will mirror their pivotal careers in the professional world and are prepared to enter a market that values creative design thinking, business theory, consumer needs, prototype development, and product testing. The program bolsters students knowledge and methods of business strategy, design theory, data visualization, communication techniques, social innovation, financial systems, and marketing.

Luxury and brand management

As future innovators in the luxury market, SCAD students enter this $350 billion global industry through five avenues: tech, travel, hospitality, beauty and fragrance, and fashion. The worlds first M.A. and M.F.A. degrees in luxury and brand management reflect the vigor of an expansive and evolving luxury market. The customized curriculum at SCAD, centered on global distribution and marketing strategies, financial analysis, supply chain management, and consumer engagement, explores the entire spectrum of the international luxury industry.

Service design

SCAD offers the first and only service design B.F.A., M.A., and M.F.A. in the U.S. Service designers create intuitive systems that organize three elements people, processes, and physical components to improve services across every realm of human activity. At SCAD, students learn how to research and analyze human behavior, societal needs, business models, and competitive environments to transform those insights into strategy. Equipped with a solid foundation in enterprise, innovation, and problem-solving, SCAD students are prepared to take leadership roles in the private and public sectors.

Social strategy and management

SCAD students are poised to launch brands to the top of the social media feed via a curriculum that merges advertising, branding, graphic design, marketing, photography, film, motion graphics, television, and writing. Students coordinate online brand advocacy and cross-promotion and become adept at creating compelling campaigns, from brand storytelling and strategy to analytics and audience engagement. Professionally, they become the creative directors, content creators, and community managers who orchestrate videos, photography, graphics, and copywriting across digital channels to launch authentic conversations and accelerate growth.

For more information on the SCAD School of Business Innovation, visit scad.edu/innovation.

SCAD: The University for Creative Careers

SCAD is a private, nonprofit, accredited university, offering more than 100 graduate and undergraduate degree programs across locations in Atlanta and Savannah, Georgia; Lacoste, France; and online via SCADnow. SCAD enrolls more than 15,700 undergraduate and graduate students from more than 120 countries. The future-minded SCAD curriculum engages professional-level technology and myriad advanced learning resources, affording students opportunities for internships, professional certifications, and real-world assignments with corporate partners through SCADpro, the universitys renowned research lab and prototype generator. SCAD is No. 1 in the U.S., according to Art & Objects 2021 Best Art Schools ranking, with additional top rankings for degree programs in interior design, architecture, film, fashion, digital media, and more. Career success is woven into every fiber of the university, resulting in a superior alumni employment rate. For the past four years, 99% of SCAD graduates were employed, pursuing further education, or both within 10 months of graduation. SCAD provides students and alumni with ongoing career support through personal coaching, alumni programs, a professional presentation studio, and more. Visit scad.edu.

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SCAD | School of Business Innovation - Business Wire

Human Behavior

We Must Consider Benefits of Sending Life Outside of Solar System, Researchers Say – Sci-News.com

University of California, Santa Barbaras professors Philip Lubin and Joel Rothman and their colleagues contemplate launching small cryptobiotic lifeforms into interstellar space.

Our ability to explore the cosmos by direct contact has been limited to a small number of lunar and interplanetary missions. However, NASAs Starlight program points a path forward to send small, relativistic spacecraft far outside our Solar System via standoff directed-energy propulsion. These miniaturized spacecraft are capable of robotic exploration but can also transport seeds and organisms, marking a profound change in our ability to both characterize and expand the reach of known life. Lantin et al. explore the biological and technological challenges of interstellar space biology, focusing on radiation-tolerant microorganisms capable of cryptobiosis. Image credit: University of California, Santa Barbara.

I think its our destiny to keep exploring, said Professor Rothman, a researcher in the Department of Molecular, Cellular and Developmental Biology at the University of California, Santa Barbara.

Look at the history of the human species. We explore at smaller and smaller levels down to subatomic levels and we also explore at increasingly larger scales.

Such drive toward ceaseless exploration lies at the core of who we are as a species.

The biggest challenge to human-scale interstellar travel is the enormous distance between Earth and the nearest stars.

NASAs Voyager missions have proven that we can send objects across the 19.3 billion km (12 billion miles) it takes to exit the bubble surrounding our Solar System, the heliosphere.

But the car-sized probes, traveling at speeds of more than 56,000 kmh (35,000 mph), took 40 years to reach there and their distance from Earth is only a tiny fraction of that to the next star. If they were headed to the closest star, it would take them over 80,000 years to reach it.

That challenge is a major focus of the teams work, in which they reimagine the technology it would take to reach the next Solar System in human terms.

Traditional onboard chemical propulsion is out; it cant provide enough energy to move the craft fast enough, and the weight of it and current systems needed to propel the ship are not viable for the relativistic speeds the craft needs to achieve.

New propulsion technologies are required and this is where the University of California, Santa Barbaras directed energy research program of using light as the propellant comes in.

This has never been done before, to push macroscopic objects at speeds approaching the speed of light, said Professor Lubin, a researcher in the Department of Physics at the University of California, Santa Barbara.

Mass is such a huge barrier, in fact, that it rules out any human missions for the foreseeable future.

As a result, the team turned to robots and photonics. Small probes with onboard instrumentation that sense, collect and transmit data back to Earth will be propelled up to 20-30% of the speed of light by light itself using a laser array stationed on Earth, or possibly the Moon.

We dont leave home with it. The primary propulsion system stays at home while spacecraft are shot out at relativistic speeds, Professor Lubin said.

The main propulsion laser is turned on for a short period of time and then the next probe is readied to be launched.

As the program evolves the spacecraft become larger with enhanced capability.

The core technology can also be used in a modified mode to propel much larger spacecraft within our Solar System at slower speeds, potentially enabling human missions to Mars in as little as one month, stopping included. This is another way of spreading life, but in our Solar System.

At these relativistic speeds roughly161 million kmh (100 million mph) the wafercraft would reach the next solar system, Proxima Centauri, in roughly 20 years.

Getting to that level of technology will require continuous innovation and improvement of both the space wafer, as well the photonics.

The basic project to develop a roadmap to achieve relativistic flight via directed energy propulsion is supported by NASA and private foundations such as the Starlight program and by the Breakthrough Initiatives as the Starshot program.

When I learned that the mass of these craft could reach gram levels or larger, it became clear that they could accommodate living animals, Professor Rothman said.

We realized that Caenorhabditis elegans could be the first Earthlings to travel between the stars. These intensively studied roundworms may be small and plain, but they are experimentally accomplished creatures.

Research on this little animal has led to Nobel prizes to six researchers thus far.

Caenorhabditis elegans are already veterans of space travel, as the subject of experiments conducted on the International Space Station and aboard the space shuttle, even surviving the tragic disintegration of the Columbia shuttle.

Among their special powers, which they share with other potential interstellar travelers that the authors study, tardigrades can be placed in suspended animation in which virtually all metabolic function is arrested.

Thousands of these tiny creatures could be placed on a wafer, put in suspended animation, and flown in that state until reaching the desired destination.

They could then be wakened in their tiny StarChip and precisely monitored for any detectable effects of interstellar travel on their biology, with the observations relayed to Earth by photonic communication.

We can ask how well they remember trained behavior when theyre flying away from their earthly origin at near the speed of light, and examine their metabolism, physiology, neurological function, reproduction and aging, Professor Rothman said.

Most experiments that can be conducted on these animals in a lab can be performed onboard the StarChips as they whiz through the cosmos.

The effects of such long odysseys on animal biology could allow the scientists to extrapolate to potential effects on humans.

We could start thinking about the design of interstellar transporters, whatever they may be, in a way that could ameliorate the issues that are detected in these diminutive animals.

The teams paper was published in the journal Acta Astronautica.

_____

Stephen Lantin et al. 2022. Interstellar space biology via Project Starlight. Acta Astronautica 190: 261-272; doi: 10.1016/j.actaastro.2021.10.009

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We Must Consider Benefits of Sending Life Outside of Solar System, Researchers Say - Sci-News.com

Human Behavior

TokenSociety Bridging the Way to a Bright Future with Metaverse Project Times Square Chronicles – Times Square Chronicles

In 2022, the world has collectively recognized that we are joined through forces more powerful than what was provided through old-fashioned methods. No longer are we simply connected through finance with one mighty American dollar. No longer is there just one singular way to communicate. We are now all living on a planet that has bridged itself over murky waters and onto a bright and radiating land. Borders have dissipated only to create powerful entities such as TokenSociety.io

The company is an NFT and metaverse project launchpad for startups in the Web 3.0 space. It is an exciting concept through and through. The infinite possibilities are explosively compelling, as well as an opportunity to develop an incredible future.

Its a one-stop shop specializing in NFT promotion, NFT drops, NFT auctions, NFT Authentication andMetaverse-related NFTs and events. TokenSociety is the ideal platform to list NFT projects and to gaintraction for events in the Metaverse, offering exclusive drops and some of the worlds first TV shows financed and supported with NFTs which will be viewable in thevirtual world.

The California-based production and distribution company will develop, produce, and distribute theanimated sitcoms. This show is based on an NFT project which was launched on the TokenSociety.ioplatform. TokenSociety is also developing a version of the TV show that will be viewable in the metaverse allowing the viewers to interact and feel they are part of the show.The NFT market growth and recent mainstream interest in the metaverse is unique in that it iscurrently driven by entertainment and leisure as opposed to functional utility.We believe that entertainment is one of the most viable solutions for enhancing the metaverseexperience, stated Scott H. Weissman, Co-Founder and CEO of TokenSociety.io.

TokenSociety and Archstone Entertainment are now set to produce Gay Aliens in Metaverse, which is a new sitcom starting avatars from the Gay Aliens Society NFT Project.This is the second show to be financed and produced by TokenSociety. The project launchpad is currentlyco-producing Men of the House, the first TV show financed exclusively through the sale of NFTs, whichthey term Snippetz.Gay Aliens Society is a collection of 10,000 hand drawn avatars created by artist, Tima Marso. GayAliens NFT owners who wish to participate in the new sitcom will be given the chance to register their NFT via the projects official Discord server and participate in a community-driven casting. There will also be an open casting call in the official Discord for those who wish to participate in the show as a voice-over actor.Twenty avatars from the Gay Aliens Society NFT Collection will be selected for starring, co-starring,and supporting recurring roles in the show for the entire season of 10 episodes. Additional roles will bemade available for each episode giving all NFT owners a chance to take part as a guest star or extra.Each avatar has randomly generated traits including sun, moon, rising sun, and Venus zodiac signs which will be used to create the personalities of each character on the show.Owners of NFTs selected for the show will enter licensing agreements with the production, allowingthem to earn royalties from the show as well as anyancillary revenue streams.Bringing companies with an established track record of success onto new platforms is a great way toencourage mainstream adoption. Its also a very practical step because workable models can still beutilized on new architectures, as marketing psychology and human behavior are often independent of technological innovation.

Game on. This is the future and it is going to be amazing for all involved when it comes to TokenSociety.

#tokensociety#gayaliens#GayAliensSociety#NFT#ScottHWeissman#ScottWeissman

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TokenSociety Bridging the Way to a Bright Future with Metaverse Project Times Square Chronicles - Times Square Chronicles

Human Behavior

How Do I Get My Moneys Worth at an All-Inclusive Resort? – Cond Nast Traveler

All-inclusive resorts can get a bad rap. The business model is associated with warm-weather locations and the type of vacation, like honeymoons or college spring breaks, where a guest travels from afar to lie still on a beach chair. In this stereotype, the food is typically uninspired and served buffet-style, and the drinks are fruity, frozen, and possibly watered-down. Even as the offerings changeand die-hard travelers can find all-inclusives that allow them to safari in southern Africa, ride horses in Patagonia, helicopter tour across Alaskathe prospect of choosing the right one, and feeling like youre getting your moneys worth, has just gotten more daunting.

A stay at Tordrillo Mountain Lodge in Alaska includes glacier hiking and heli-skiing.

It helps to grasp how an all-inclusive makes money. Any business is based around predictions of human behavior, and these resorts survive by making calculations and adjustments. It's trial and error, and you just change to accommodate people's needs wherever you can, says Michael Overcast, the owner of the Tordrillo Mountain Lodge in Alaska. What's important to us is that people feel that they get value, and that really comes out with the comments at the end of the trip, and the tip out for the employees. Keyboard warriors can have a wild impact across the industry, but particularly in this sector, says Elizabeth Fettes, chief marketing and sales officer at Karisma Hotels & Resorts. All-inclusives rely heavily on reviews, she says. [With] the higher review, you're naturally going to have a higher premium rate, and that's going to affect your bottom line.

In some ways, these venues operate like any other hotel. You have your overheads and you have your calculations and you know roughly what your annual percentage of food is going to be for x, y, and z, and that's what you base your rates on and you work from there, says Rebecca Platt, corporate director of sales and marketing for BodyHoliday and Rendezvous resorts in St. Lucia. Guests just need to beware of hidden charges. Although it doesn't apply to my resort here in St. Lucia, I have worked with resorts in previous lives where you don't see where there are different levels of all-inclusive packages, she continues. You will look at a fantastic rate to start but then when you actually get to the resortwell, yes, you can include that, for the extra x amount of dollars.

In addition to tiers of inclusivity, food and beverage upgrades are another spot where resorts can cash in. When they buy some special food or a bottle of wine, this is where the all-inclusives make money, because they don't consume what [they were] supposed to consume. Or when they go out for dinner in town, says Claudia Perez, corporate director of sales and marketing at Marquis Los Cabos Resort & Spa. Her colleague Casandra Luna, associate director of sales at the resort, points to one-time promotions, which encourage guests to return at full price and packages where hotels can make extra money. Packages with transportation, romantic services incentivizing going to the spaeverything that is not part of the all-inclusive, that's where we receive the revenue, she says.

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How Do I Get My Moneys Worth at an All-Inclusive Resort? - Cond Nast Traveler

Human Behavior

Steve King: The freedom of want is still a dream – Canton Repository

Steve King| Suburbanite correspondent

It was 79 years ago Thursday, on Jan. 6, 1941, that President Franklin Roosevelt delivered his famous Four Freedoms speech to Congress.

It was the main theme the overriding and the only theme, really of his 1941 State of the Union speech.

In future days, which we seek to make secure, we look forward to a world founded upon four essential freedoms, FDR said. As he saw and described them:

The first is freedom of speech and expression everywhere in the world.

The second is freedom of every person to worship God in his own way everywhere in the world.

The third is freedom of want which, translated into world terms, means economic understandings which will secure to every nation a healthy peacetime life for its inhabitants everywhere in the world.

The fourth is freedom from fear which, translated into world terms, means a worldwide reduction of armaments to such a point and in such a thorough fashion that no nation will be in position to commit an act of physical aggression against any neighbor anywhere in the world.

Then FDR added, That is no vision of a distant millennium. It is a definite basis for a kind of world attainable in our own time and generation.

At the same time, Hitler was waging war on Europe and Japan was doing the same in the Pacific, and then almost exactly 11 months afterward, the Japanese attacked Pearl Harbor to drag FDR and the United States, which had been staunchly isolationist, into World War II.

But though FDRs words were swallowed up then, they survived the war and in 1948, the United Nations used the Four Freedoms as its guideline in the Universal Declaration of Human Rights it adopted.

Its the third freedom the freedom of want that is of particular interest, at least to me, as it applies to hunger, especially children. I think hunger is the biggest need of the four freedoms. You cant live if theres not enough to eat.

I have mentioned on a number of occasions in this space that I work a side job as a clerk at a convenient store. It is a great study in human behavior, most times in a positive fashion that warms your heart and soul, but still too many times that make you grit your teeth, bite your tongue and shake your head in disgust.

The store is located in a generally well-to-do city of 60,000 people situated near a much larger city. As with any big city, there are areas of economic difficulties within, and the biggest one in this particular city sets just four miles from the store.

Customers think nothing of dropping $100, and even $200 and more, on cigarettes, beer and lottery tickets. To each their own, and this is not a general diatribe against those expenditures, but only in the sense that that money could do so much to feed the hungry kids just down the road.

More specifically, if just 10 percent of the sales of the aforementioned items in any convenience store in the area, including ours, were to go instead to the local foodbank, it would fill a lot of empty stomachs.

That those people would, in my opinion, never consider taking that money and donating it, let alone actually doing it, is numbingly disappointing and disheartening.

All these years later, those needs and those resulting emotions persist.

Franklin Roosevelt would be saddened.

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Steve King: The freedom of want is still a dream - Canton Repository

Human Behavior

WW NAMED #1 "BEST DIET FOR WEIGHT LOSS" AND "BEST DIET PROGRAM" FOR TWELFTH CONSECUTIVE YEAR – PRNewswire

NEW YORK, Jan. 4, 2022 /PRNewswire/ --WW International, Inc. (NASDAQ: WW) - a human-centric technology company powered by the world's leading commercial weight management program - has been recognized once again by health experts in the 2022 Best Diets rankings, released today by U.S. News & World Report. WW retained the #1 spot for both "Best Diet for Weight Loss" and "Best Diet Program" for the twelfth consecutive year since the rankings were first introduced in 2010. These rankings reflect decades of WW's scientific expertise and clinical evidence showcasing the program's effectiveness for sustainable weight loss and weight management.

WW also received high marks in the categories of "Best Diet Overall," "Easiest Diet to Follow," "Best Diet for Healthy Eating" and "Best Diet for Fast Weight Loss."

"This recognition is a testament of our unwavering focus on helping people develop healthy habits, rooted in science," said Gary Foster, PhD, Chief Scientific Officer, WW. "As part of that commitment, our team is constantly advancing our clinical research and innovating our program based on consumer needs and the latest nutrition and behavior change science. That way, we can meet people where they are on their wellness journeys and provide science-based tools and techniques."

WW most recently launched its PersonalPoints weight-loss programin November 2021, the latest advancement in helping members integrate positive lifestyle changes into their everyday lives. The new program makes losing weight and reaching wellness goals simpler and more attainable, all while helping members continue to live their best, fullest lives. It delivers a trifecta of game-changing new elements that work together to yield weight and wellness benefits, including: individualized plans, custom-built for each member; WW's most advanced food algorithm to date; and the ability to add Points for healthy behaviors such as eating non-starchy vegetables (like carrots, tomatoes, broccoli and spinach), reaching a daily water goal, and moving more. WW PersonalPoints has been proven to deliver clinically significant weight loss; decreases in hunger and food cravings; increases in physical activity and healthy habit formation; as well as improvements in overall well-being and quality of life.1

"We are honored to retain this distinguished ranking for over a decade, as a proven leader in weight management," said Mindy Grossman, President and CEO at WW. "As the world's partner in weight loss and wellness, we are constantly innovating on behalf of our members' needs to personalize the WW experience. This recognition further builds on the growing evidence that WW works: delivering solutions that fit members' lives and providing a livable path to sustainable weight management and healthy living."

WW members have the opportunity to further personalize how they follow the program through the award-winning mobile app; an on-demand wellness experience with Digital 360 (D360); or by attending Workshops (in-person or virtual). WW's world-class product and technology team is dedicated to enhancing the WW app experience every day.

The U.S. News & World Report panel of health experts includes nutritionists, physicians and others specializing in diabetes, heart health, human behavior and weight loss. For more information about the rankings, visit Best Diets 2022. For more information about WW, visit http://www.ww.com.

About WW International, Inc. WW (formerly Weight Watchers) is a human-centric technology company powered by the world's leading commercial weight management program. As a global wellness company, we inspire millions of people to adopt healthy habits for real life. Through our comprehensive digital app, expert Coaches and engaging experiences, members follow our proven, sustainable, science-based program focused on food, activity, mindset and sleep. Leveraging nearly six decades of expertise in nutritional and behavioral change science, providing real human connection and building inspired communities, our purpose is to democratize and deliver holistic wellness for all. To learn more about the WW approach to healthy living, please visit ww.com. For more information about our global business, visit our corporate website corporate.ww.com.

1Six-month pre-post study on 153 participants, conducted by Sherry Pagoto, PhD, and colleagues at the University of Connecticut. Study funded by WW.

For more information, contact:Jenny Zimmerman, WW[emailprotected]

SOURCE WW International, Inc.

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WW NAMED #1 "BEST DIET FOR WEIGHT LOSS" AND "BEST DIET PROGRAM" FOR TWELFTH CONSECUTIVE YEAR - PRNewswire