After decades of using mostly college students, researchers in psychology and social sciences gradually moved online to more cost-effective and flexible audiences. First, to samples of online convenience (CraigsList, Facebook), and then to sophisticated markets like Amazon’s Mechanical Turk (MTurk). Now, advanced platforms (such as CloudResearch and Prolific) promise researchers higher data quality using ex-ante vetting and controls on their participants’ pool. We systematically examine the impact of these ex-ante controls, and their effects on various measures of data quality, and compare them to standard ex-post approaches of attention checks (in randomized positions). We find that ex-ante controls outperform ex-post checks both on process measures of attention, honesty, and reliability, as well as on outcome measures of replicability. Additionally, while samples from platforms with ex-ante vetting show key differences in representation from the general population, they are still more representative than a major university lab pool, and no less representative than samples that are not ex-ante vetted. We conclude that pre-vetting of participants on the platform level should become the desired standard for online psychological and behavioral research, and could serve as a sufficient condition for ensuring a sample’s data quality ex-ante.  

Authorities and managers often rely on individuals’ and businesses’ self-reports, and employ various kinds of veracity statements, honesty pledges or oaths to ensure they do not over-claim payments, benefits, or other resources. While some research show honesty pledges can reduce dishonesty, others have provided mixed, and sometimes even contradictory, findings. We argue that understanding and predicting when honesty pledges are effective has been obstructed due to variations in the operationalizations of honesty pledges in previous research. Specifically, we identify that previous studies varied in whether and how the pledge asks individuals to identify (e.g., by ID, signature or name) and how much involvement, if at all, the pledge requires from the individual. In four pre-registered studies (N>5,000), we systematically examine these elements of a pledge to find that increasing involvement of pledgers (by having them copy the text of the pledge) is often more effective than those that only require identification. In contrast, pledges that only require individuals to read and agree are mostly ineffective. Moreover, we find that the effects of high-involvement pledges persist both over time and both after a short delay between the pledge and the opportunity to cheat. Together, these results contribute both theoretically to the understanding of the mechanisms underlying different honesty pledges as well as offer practical advice to managers and policymakers on how to effectively prevent or reduce dishonesty in self-reports.

Researchers are increasingly using online audiences to conduct studies and surveys, but there is still considerable uncertainty about the quality of the available audience options. We first engage with the research community to explore which attributes of data quality are most critical to researchers: comprehension, attention, honesty, and reliability. We then explore differences in these data quality aspects between online platforms (where participants choose tasks) and online panels (where respondents are assigned surveys). We find that most audiences suffer concerning deficits, especially in comprehension and attention, except for Prolific, which appears superior to the other options on these and other parameters. The disappointing results from the other platforms and panels should alarm researchers who may be aggregating over serious noise in the quality of their results. Additionally, we provide a framework for ongoing investigation into the ever-changing nature of what aspects of data quality are important to researchers, and how the evolving set of audiences performs on these key aspects.

A common regulatory dilemma is determining how much trust authorities can place in people’s self-reports, especially in contexts with an incentive to cheat. In such contexts, regulators are typically risk averse and do not readily confer trust, resulting in excessive requirements when applying for permits, licenses, etc. Studies in behavioral ethics have suggested that asking people to ex-ante pledge to behave ethically can reduce their level of dishonesty and noncompliance. However, pledges might also backfire by allowing more people to cheat with no real sanctions. Additionally, pledges’ effects have almost always been studied in one-shot decision making without sanctions. We explore pledges’ potential effects by manipulating whether pledges were accompanied by sanctions (fines) and testing their impact on sequential, repeated ethical decisions. We find that pledges can considerably and consistently reduce dishonesty, and this effect is not crowded out by the presence of fines. Furthermore, pledges also affect “brazen liars” who cheat to a large extent and also those who score low on tendency to follow rules and norms. We conclude that pledges could be an effective tool for the behavioral regulation of dishonesty, to reduce regulatory burden, and to build a more trusting relationship between government and the public.

Behaviorally informed policies of interventions in choice architecture are growingly used to nudge people towards socially desirable behaviors. While consumers are usually the target of those nudges, businesses often serve as “nudging agents" on behalf of government regulation, or may be the target of governmental nudges themselves. Businesses’ support of such behavioral policies might be critical for their implementation, but the perceptions of managers towards nudges has never been directly assessed. We distinguish between government-to-business (G2B) nudges vs. government-to-business-to-consumer (G2B2C) nudges and provide first evidence of business managers’ attitudes towards such interventions. We discover an overall high level of support for nudges, and in particular for G2B nudges, with variations between types of nudges, the domain that they operate in, and whether they benefit the business or the consumer.

Nudges are simple and effective means to help people make decisions that could benefit themselves or society. However, effects of nudges are limited to local maxima, as they are almost always designed with the “average” person in mind, instead of being customized to different individuals. Such “nudge personalization” has been advocated before, but its actual potency and feasibility has never been systematically investigated. Using the ubiquitous domain of online password nudges as our testbed, we present a novel approach that utilizes people’s decision-making style to personalize the online nudge they receive. In two large-scale studies, we show how and when personalized nudges can lead to considerably stronger and more secure passwords, compared to administering “one-size-fits-all” nudges. We discuss the implications of this findings and how more efforts by researchers and policy-makers should and could be made to guarantee that each individual is nudged in a way most right for them. 

Attitudes of public groups towards behavioral policy interventions (or nudges) can be important for both the policy makers who design and deploy nudges, and to researchers who try to understand when and why some nudges are supported while others are not. Until now, research on public attitudes towards nudges has focused on either state- or country-level comparisons, or on correlations with individual-level traits, and has neglected to study how different social groups (such as minorities) might view nudges. Using a large and representative sample, we tested the attitudes of two distinct minority groups in Israel (Israeli Arabs and Ultra-Orthodox Jews), and discovered that nudges that operated against a minority group’s held social norms, promoting a more general societal goal not aligned with the group’s norms, were often less supported by minorities. Contrary to expectations, these differences could not be explained by differences in trust in the government applying these nudges. We discuss implications for public policy and for the research and applications of behavioral interventions.

Malleability of preferences is a central tenet of behavioral decision theory. How malleable preferences really are, however, is a topic of debate. Do preference reversals imply preference construction? We argue that to claim preferences are construed, a demonstration of more extreme preference malleability than simple preference reversals is required: absolute preference sign changes within participants. If respondents value a prospect positively in 1 condition but negatively in a different condition, preferences cannot be considered stable. Such absolute preference sign changes are possible under uncertainty. In 2 incentive‐compatible experiments, we found participants were willing to pay to take part in a gamble and also demanded to be compensated to take part in a subsequent gamble with identical outcomes and probabilities. Such absolute preference sign changes within participants led to simultaneous risk aversion and risk seeking for the same risky prospect, suggesting that, at least in the domain of risky decisions, consumers' preferences are indeed malleable and construed.

Self-images are among the most prevalent forms of content shared on social media streams. Face-morphs are images digitally created by combining facial pictures of different individuals. In the case of self-morphs, a person’s own picture is combined with that of another individual. Prior research has shown that even when individuals do not recognize themselves in self-morphs, they tend to trust self-morphed faces more, and judge them more favorably. Thus, self-morphs may be used online as covert forms of targeted marketing–for instance, using consumers’ pictures from social media streams to create self-morphs, and inserting the resulting self-morphs in promotional campaigns targeted at those consumers. The usage of this type of personal data for highly targeted influence without individuals' awareness, and the type of opaque effect such artifacts may have on individuals' attitudes and behaviors, raise potential issues of consumer privacy and autonomy. However, no research to date has examined the feasibility of using self-morphs for such applications. Research on self-morphs has focused on artificial laboratory settings, raising questions regarding the practical, in-the-wild applicability of reported self-morph effects. In three experiments, we examine whether self-morphs could affect individuals' attitudes or even promote products/services, using a combination of experimental designs and dependent variables. Across the experiments, we test both designs and variables that had been used in previous research in this area and new ones that had not. Questioning prior research, however, we find no evidence that end-users react more …

The Security Behavior Intentions Scale (SeBIS) measures the computer security attitudes of end-users. Because intentions are a prerequisite for planned behavior, the scale could therefore be useful for predicting users' computer security behaviors. We performed three experiments to identify correlations between each of SeBIS's four sub-scales and relevant computer security behaviors. We found that testing high on the awareness sub-scale correlated with correctly identifying a phishing website; testing high on the passwords sub-scale correlated with creating passwords that could not be quickly cracked; testing high on the updating sub-scale correlated with applying software updates; and testing high on the securement sub-scale correlated with smartphone lock screen usage (e.g., PINs). Our results indicate that SeBIS predicts certain computer security behaviors and that it is a reliable and valid tool that should be used in future research.

This paper aims to examine how reversibility in disclosing personal information – that is, having (vs not having) to option to later revise or retract personal information – can impact consumers’ willingness to divulge personal information. Three studies examined how informing consumers they may (reversible condition) or may not (irreversible condition) revise their personal information in the future affected their propensity to disclose personal information, compared to a control condition. Study 1 (which included three experiments with different time intervals between initial and revised disclosure) showed that consumers disclose less in both the reversible and irreversible conditions, compared to the control condition. Studies 2 and 3 showed that this is because consumers treat reversibility as a cue to the sensitivity of the information they are asked to divulge, and that leads them to disclose less when reversibility or irreversibility is made explicitly salient beforehand. As many marketers are interested in hoarding consumers’ personal information, privacy advocates call for methods that would ensure careful and well-informed disclosure. Offering reversibility to a decision to disclose personal information, or merely pointing out the irreversibility of that decision, can make consumers reevaluate the sensitivity of the situation, leading to more careful disclosures. Although previous research on reversibility in consumer behavior focused on product return policies and showed that reversibility increases purchases, none have studied how reversibility affects self-disclosure and how it can decrease it.

While individual differences in decision-making have been examined within the social sciences for several decades, they have only recently begun to be applied by computer scientists to examine privacy and security attitudes (and ultimately behaviors). Specifically, several researchers have shown how different online privacy decisions are correlated with the "Big Five" personality traits. In this paper, we show that the five factor model is actually a weak predictor of privacy attitudes, and that other well-studied individual differences in the psychology literature are much stronger predictors. Based on this result, we introduce the new paradigm of psychographic targeting of privacy and security mitigations: we believe that the next frontier in privacy and security research will be to tailor mitigations to users' individual differences. We explore the extensive work on choice architecture and "nudges," and discuss the possible ways it could be leveraged to improve security outcomes by personalizing privacy and security mitigations to specific user traits.