Tom Meyvis

Bio: Tom Meyvis is an academic researcher from New York University. The author has contributed to research in topics: Preference & Regret. The author has an hindex of 23, co-authored 59 publications receiving 5979 citations. Previous affiliations of Tom Meyvis include University of Florida.

Instructional Manipulation Checks: Detecting Satisficing to Increase Statistical Power

Abstract: Participants are not always as diligent in reading and following instructions as experimenters would like them to be. When participants fail to follow instructions, this increases noise and decreases the validity of their data. This paper presents and validates a new tool for detecting participants who are not following instructions – the Instructional manipulation check (IMC). We demonstrate how the inclusion of an IMC can increase statistical power and reliability of a dataset.

Effects of Brand Logo Complexity, Repetition, and Spacing on Processing Fluency and Judgment

Abstract: It is generally accepted that repeated exposure to an advertisement can influence liking for an advertisement and for the brand names and product packages included in the advertisement. Although it has often been assumed that repeated exposure leads to a direct affective response, more recent evidence suggests that prior exposure leads to processing fluency at the time of judgment. It is a misattribution about the source of this processing fluency that results in preference for the stimulus. To date, the majority of research on the processing fluency/attribution hypothesis has focused on when people will make fluency-based attributions, while assuming the amount of the processing fluency is a direct function of exposure. In this article, we propose that stimulus characteristics and presentation factors will interact with repetition to determine the amount of processing fluency associated with a stimulus at various levels of exposure. Four studies are used to test whether two-factor theory or dual-process theory provides a better account of the source of the processing fluency. Implications for logo design are discussed.

When Are Broader Brands Stronger Brands? An Accessibility Perspective on the Success of Brand Extensions

Abstract: It is common for brands to extend into additional product categories The most successful extensions involve brands that are associated with benefits that are valued in the extension category We propose that brand extension success also depends on the accessibility of these benefit associations and that accessibility, in turn, depends on the amount of interference by competing brand associations (eg, category associations) One implication of this proposition is that broad brands (ie, brands offering a portfolio of diverse products) will tend to have more accessible benefit associations than narrow brands (ie, brands offering a portfolio of similar products) and can therefore engage in more successful brand extensions than narrow brands, even when the narrow brands are more similar to the extension category However, when benefit associations are equally accessible and diagnostic, the evaluation of brand extensions will instead be dictated by the similarity between brand and extension category associations

Consumers’ Beliefs about Product Benefits: The Effect of Obviously Irrelevant Product Information

Abstract: When consumers try to assess the performance of a product on a key benefit, their information search often reveals both diagnostic information and irrelevant information. Although one would expect irrelevant information to have little impact on predictions of product performance, we present evidence that the irrelevant information systematically weakens consumers' beliefs that the product will provide the benefit. We show that this dilution effect persists after subjects have acknowledged the irrelevance of the additional information but that it does depend on whether the product information is processed with the desired benefit in mind. We conclude that consumers are selectively looking for information that suggests the product will deliver the desired benefit and that they categorize any additional evidence, be it irrelevant or disconfirming, as not confirming. As a consequence, irrelevant information weakens consumers' beliefs in the product's ability to deliver the benefit.

Running experiments on Amazon Mechanical Turk

Abstract: textAlthough Mechanical Turk has recently become popular among social scientists as a source of experimental data, doubts may linger about the quality of data provided by subjects recruited from online labor markets. We address these potential concerns by presenting new demographic data about the Mechanical Turk subject population, reviewing the strengths of Mechanical Turk relative to other online and offline methods of recruiting subjects, and comparing the magnitude of effects obtained using Mechanical Turk and traditional subject pools. We further discuss some additional benefits such as the possibility of longitudinal, cross cultural and prescreening designs, and offer some advice on how to best manage a common subject pool.

Running experiments on Amazon Mechanical Turk

Abstract: Although Mechanical Turk has recently become popular among social scientists as a source of experimental data, doubts may linger about the quality of data provided by subjects recruited from online labor markets. We address these potential concerns by presenting new demographic data about the Mechanical Turk subject population, reviewing the strengths of Mechanical Turk relative to other online and offline methods of recruiting subjects, and comparing the magnitude of effects obtained using Mechanical Turk and traditional subject pools. We further discuss some additional benefits such as the possibility of longitudinal, cross cultural and prescreening designs, and offer some advice on how to best manage a common subject pool.

Brands and Branding: Research Findings and Future Priorities

Abstract: Branding has emerged as a top management priority in the last decade due to the growing realization that brands are one of the most valuable intangible assets that firms have. Driven in part by this intense industry interest, academic researchers have explored a number of different brand-related topics in recent years, generating scores of papers, articles, research reports, and books. This paper identifies some of the influential work in the branding area, highlighting what has been learned from an academic perspective on important topics such as brand positioning, brand integration, brand-equity measurement, brand growth, and brand management. The paper also outlines some gaps that exist in the research of branding and brand equity and formulates a series of related research questions. Choice modeling implications of the branding concept and the challenges of incorporating main and interaction effects of branding as well as the impact of competition are discussed.

Data collection in a flat world: the strengths and weaknesses of mechanical turk samples

Abstract: Mechanical Turk (MTurk), an online labor system run by Amazon.com, provides quick, easy, and inexpensive access to online research participants. As use of MTurk has grown, so have questions from behavioral researchers about its participants, reliability, and low compensation. In this article, we review recent research about MTurk and compare MTurk participants with community and student samples on a set of personality dimensions and classic decision-making biases. Across two studies, we find many similarities between MTurk participants and traditional samples, but we also find important differences. For instance, MTurk participants are less likely to pay attention to experimental materials, reducing statistical power. They are more likely to use the Internet to find answers, even with no incentive for correct responses. MTurk participants have attitudes about money that are different from a community sample’s attitudes but similar to students’ attitudes. Finally, MTurk participants are less extraverted and have lower self-esteem than other participants, presenting challenges for some research domains. Despite these differences, MTurk participants produce reliable results consistent with standard decision-making biases: they are present biased, risk-averse for gains, risk-seeking for losses, show delay/expedite asymmetries, and show the certainty effect—with almost no significant differences in effect sizes from other samples. We conclude that MTurk offers a highly valuable opportunity for data collection and recommend that researchers using MTurk (1) include screening questions that gauge attention and language comprehension; (2) avoid questions with factual answers; and (3) consider how individual differences in financial and social domains may influence results. Copyright © 2012 John Wiley & Sons, Ltd.

Data Collection in a Flat World: Strengths and Weaknesses of Mechanical Turk Samples

Abstract: Mechanical Turk (MTurk), an online labor system run by Amazon.com, provides quick, easy, and inexpensive access to online research participants. As use of MTurk has grown, so have questions from behavioral researchers about its participants, reliability, and low compensation. In this paper we review recent research about MTurk and compare MTurk participants to community and student samples on a set of personality dimensions and classic decision-making biases. Across two studies, we find many similarities between MTurk participants and traditional samples, but we also find important differences. For instance, MTurk participants are less likely to pay attention to experimental materials, reducing statistical power. They are more likely to use the Internet to find answers, even with no incentive for correct responses. MTurk participants have attitudes about money that are different from a community sample’s attitudes, but similar to students’ attitudes. Finally, MTurk participants are less extraverted and have lower self-esteem than other participants, presenting challenges for some research domains. Despite these differences, MTurk participants produce reliable results consistent with standard decision-making biases: They are present biased, risk-averse for gains, risk-seeking for losses, show delay/expedite asymmetries, and show the certainty effect — with almost no significant differences in effect sizes from other samples. We conclude that MTurk offers a highly valuable opportunity for data collection, and recommend that researchers using MTurk 1) include screening questions that gauge attention and language comprehension, 2) avoid questions with factual answers, and 3) consider how individual differences in financial and social domains may influence results.