Eyal Peer

Reputation as a sufficient condition for data quality on Amazon Mechanical Turk

Data quality is one of the major concerns of using crowdsourcing websites such as Amazon Mechanical Turk (MTurk) to recruit participants for online behavioral studies. We compared two methods for ensuring data quality on MTurk: attention check questions (ACQs) and restricting participation to MTurk workers with high reputation (above 95% approval ratings). In Experiment 1, we found that high-reputation workers rarely failed ACQs and provided higher-quality data than did low-reputation workers; ACQs improved data quality only for low-reputation workers, and only in some cases. Experiment 2 corroborated these findings and also showed that more productive high-reputation workers produce the highest-quality data. We concluded that sampling high-reputation workers can ensure high-quality data without having to resort to using ACQs, which may lead to selection bias if participants who fail ACQs are excluded post-hoc.

Scaling the Security Wall: Developing a Security Behavior Intentions Scale (SeBIS)

Despite the plethora of security advice and online education materials offered to end-users, there exists no standard measurement tool for end-user security behaviors. We present the creation of such a tool. We surveyed the most common computer security advice that experts offer to end-users in order to construct a set of Likert scale questions to probe the extent to which respondents claim to follow this advice. Using these questions, we iteratively surveyed a pool of 3,619 computer users to refine our question set such that each question was applicable to a large percentage of the population, exhibited adequate variance between respondents, and had high reliability (i.e., desirable psychometric properties). After performing both exploratory and confirmatory factor analysis, we identified a 16-item scale consisting of four sub-scales that measures attitudes towards choosing passwords, device securement, staying up-to-date, and proactive awareness.

'I Cheated, But Only a Little' – Partial Confessions to Unethical Behavior

Confessions are peoples way of coming clean, sharing unethical acts with others. Although confessions are traditionally viewed as categorical-one either comes clean or not-people often confess to only part of their transgression. Such partial confessions may seem attractive, because they offer an opportunity to relieve ones guilt without having to own up to the full consequences of the transgression. In this article, we explored the occurrence, antecedents, consequences, and everyday prevalence of partial confessions. Using a novel experimental design, we found a high frequency of partial confessions, especially among people cheating to the full extent possible. People found partial confessions attractive because they (correctly) expected partial confessions to be more believable than not confessing. People failed, however, to anticipate the emotional costs associated with partially confessing. In fact, partial confessions made people feel worse than not confessing or fully confessing, a finding corroborated in a laboratory setting as well as in a study assessing peoples everyday confessions. It seems that although partial confessions seem attractive, they come at an emotional cost.

Beyond the Turk: An Empirical Comparison of Alternative Platforms for Crowdsourcing Online Behavioral Research

The success of Amazon Mechanical Turk (MTurk) as an online research platform has come at a price: MTurk exhibits slowing rates of population replenishment, and growing participants’ non-naivety. Recently, a number of alternative platforms have emerged, offering capabilities similar to MTurk while providing access to new and more naïve populations. We examined two such platforms, CrowdFlower (CF) and Prolific Academic (ProA). We found that both platforms’ participants were more naïve and less dishonest compared to MTurk. CF showed the best response rate, but CF participants failed more attention-check questions and did not reproduce known effects replicated on ProA and MTurk. Moreover, ProA participants produced data quality that was higher than CF’s and comparable to MTurk’s. We also found important demographic differences between the platforms. We discuss how researchers can use these findings to better plan online research, and their implications for the study of crowdsourcing research platforms.

Beyond the Privacy Paradox: Objective versus Relative Risk in Privacy Decision Making

Privacy decision making has been examined from various perspectives. A dominant “normative” perspective has focused on rational processes by which consumers with stable preferences for privacy weigh the expected benefits of privacy choices against their potential costs. More recently, an alternate “behavioral” perspective has leveraged theories from behavioral decision research to construe privacy decision making as a process in which cognitive heuristics and biases predictably occur. In a series of experiments, we compare the predictive power of these two perspectives by evaluating the impact of changes in objective risk of disclosure and the impact of changes in relative perceptions of risk of disclosure on both hypothetical and actual consumer privacy choices. We find that both relative and objective risks can, in fact, impact consumer privacy decisions. However, and surprisingly, the impact of objective changes in risk diminishes between hypothetical and actual choice settings. Vice versa, the impact of relative risk is more pronounced going from hypothetical to actual choice settings. Our results suggest a way to integrate diverse streams of IS literature on privacy decision making: consumers may both over-estimate their response to normative factors and under-estimate their response to behavioral factors in hypothetical choice contexts relative to actual choice contexts.

The impact of reversibility on the decision to disclose personal information

Purpose 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. Design/methodology/approach 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. Findings 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. Practical implications 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. Originality/value 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.

Pay More – Save Less: The Paradoxical Time-Saving Bias in Consumers’ Choice

Some products and services (e.g., toll roads, Internet speed, electronics, etc.) offer consumers higher operating speeds that save time for completing a task (e.g., a journey or downloading files on the Internet). Consumers interested in such products have to judge the benefit of obtaining a higher speed product, in terms of time-savings, to decide whether or not to purchase it. However, previous research has shown that people’s intuitions about time-savings are biased, suggesting that consumers’ judgments and preferences for products with different speeds could be biased as well. In a series of studies, I find that consumers indeed err in such judgments and overestimate increases from higher speeds while underestimating increases from lower speeds. Moreover, these faulty judgments lead consumers to over-pay for increases that are made from higher speeds and under-pay for increases from lower speeds. Consumers seem to base their preferences on the simple differences between the speeds, disregarding the high impact of the initial speed. This Difference Heuristic leads consumers to be willing to pay more (money) to save less (time).