Causal Models Drive Preference between Drugs that Treat a Focal versus Multiple Symptoms

Saporta-Sorozon, Kelly; Danziger, Shai; Sloman, Steven A.

doi:10.1002/bdm.1999

Cited by 3 publications

(4 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results of the present study join with other studies that focus on the effects of causal-reasoning principles on judgments and inferences concerning artifacts (products) (e.g., Saporta-Sorozon, Danziger, & Sloman, 2017;Saporta-Sorozon & Bar-Eli, 2017;Saporta-Sorozon, 2018). Thus, it encourages studying the artifacts' (products') effects using the causal-reasoning framework.…”

Section: Theoretical Implicationssupporting

confidence: 87%

The Effect of Similarity Between a Product’s Shape Properties and the Benefit Offered on Judgments and Preferences

Sorozon¹

2019

BMS

View full text Add to dashboard Cite

Studies show a product's shape affects consumer judgments. Yet, the basic principle that governs shape effects is unclear. In this study, we fill this gap by demonstrating that causal-reasoning considerations govern shape effects. Specifically, people expect cause (e.g., an energy drink) and its effect (e.g., fat burning) to exhibit some degree of resemblance or congruency (“like causes like”). This expectation leads them to use the “law of similarity” heuristic when judging artifacts. In two studies, we focused on a product's shape. For half of the products, the shape was an intrinsic property of the product (i.e., had the causal power to produce the effect), and for the other half, the shape was an extrinsic property of the product. For both kinds of properties (extrinsic and intrinsic), we demonstrate that the same ad (e.g., an energy drink that "claims" to produce fat burning) is more persuasive (willingness to purchase the product, and choice between products) when the product's shape is congruent rather than incongruent (e.g., a “tall” can vs. a “short” can) with the effect promised.We strengthen the notion that leaning on a cause-effect-similarity heuristic is very basic, by showing that choice situations accelerate the effect of congruency more for products for which the focal property is extrinsic than for products for which it is intrinsic.In line with other studies that show causal reasoning considerations govern judgment and choice on artifacts (products), the present study demonstrates causal-reasoning considerations govern judgment and choice concerning the cause-effect-similarity heuristic as well.

show abstract

Section: Theoretical Implicationssupporting

confidence: 87%

The Effect of Similarity Between a Product’s Shape Properties and the Benefit Offered on Judgments and Preferences

Sorozon¹

2019

BMS

View full text Add to dashboard Cite

show abstract

“…We focus on gambles for two reasons: First, consumers face many risky choices that share similar attributes with gambles. For example, when consumers choose between investments or grocers, they need to trade off investment returns (or discount magnitude) with investment risks (or discount frequency), or when they choose between medical treatments, they need to trade off the positive (partial or full treatment) and negative outcomes (side effects) of each treatment with the likelihood of these events (Saporta‐Sorozon, Danziger, & Sloman, ). Second, much of the literature examining differences between description‐based and experience‐based choice examined choice between gambles (Barron & Erev, ; Hertwig, ; Hertwig et al, ; Wulff, Mergenthaler Canseco, & Hertwig, in press).…”

mentioning

confidence: 99%

The Attraction Effect in Experience‐based Decisions

Hadar

Danziger

Hertwig

2018

Behavioral Decision Making

Self Cite

View full text Add to dashboard Cite

Previous demonstrations of the attraction effect were limited to explicitly described attribute values (including numerically indexed attributes, such as gambles' outcomes and their likelihoods, or perceptual attributes, such as rectangles' height and width). However, in many real‐life decisions, such as the choice of a preferred grocer, people decide based on their past experience with the options' attributes (e.g., the frequency and magnitude of product discounts over time). We examine whether the attraction effect extends from description‐based to experience‐based choice between gambles. Our results demonstrate that the attraction effect exists but is less prevalent in experience‐based than in description‐based choice. This is because the dominance relationship, necessary for the attraction effect to occur, is more difficult to recognize in experience‐based choice. Yet, when dominance is recognized, people may use it to guide difficult trade‐off choices. We discuss practical implications. Copyright © 2018 John Wiley & Sons, Ltd.

show abstract

“…We focused on numerical data because the majority of the studies that examined the question of how data and preexisting causal knowledge are reconciled used numerical data (see, e.g., Fugelsang & Thompson, ; Perales et al, ; Waldmann & Hagmayer, ; for a summary, see also Rottman & Hastie, ). Yet, the few studies that used nonnumerical covariation data when examining the same question show the same pattern (see, e.g., Bes et al, ; Saporta‐Sorozon et al, ). Thus, we have a basis to assume that the pattern of our results will be similar for other ways of data representation.…”

Section: Discussionmentioning

confidence: 91%

“…Perales, Catena, and Maldonado (), for example, show that people expect a higher covariation between two cues when they believe they are effects of the same cause (e.g., a disease that causes secretion of two substances into the blood) than when they believe they are two causes for the same effect (e.g., two substances in the blood that cause a disease). Similarly, Saporta‐Sorozon, Danziger, and Sloman () recently found that the causal model held by people concerning the cause of two symptoms—same cause (e.g., nausea and vomiting) or different causes (e.g., insomnia and nausea) and not the covariation between the symptoms—affects participants' preference for a drug that treats the two symptoms rather than one. Lagnado and Sloman () demonstrate that people use temporal‐order and intervention cues to infer causal structure when the cues dominate the statistical information available.…”

Section: Conceptual Frameworkmentioning

confidence: 94%

The effect of previous causal knowledge on the persuasive strength of confirming covariation data

Saporta-Sorozon

Bar‐Eli

2017

J of Consumer Behaviour

Self Cite

View full text Add to dashboard Cite

Consumers are often exposed to causal claims (e.g., a new pill that claims to treat acne) that are occasionally accompanied by data indicating that the product (target) performed better than another product (referent). In this study, we examined the effect of such data on persuasion as a function of target-referent similarity in causal features. Consistent with current theorizing suggesting that structural and specific preexisting causal knowledge affects data interpretation, we propose that data that are consistent with expectations will be more persuasive than data that are inconsistent with expectations. Specifically, we contend that the structural schema "control of variables" we use leads us to expect that two categories (products) that are similar in features will perform the same and two categories that differ in features will differ in performance. In addition, our specific knowledge on causal powers leads us to expect the target to perform better than the referent only if it has more causal features. Thus, when confronted with data indicating a target performed better than a referent with fewer causal features, the reasoner will find it easier to explain the data, and hence, the difference in performance will be perceived as larger, and the message will be more persuasive (e.g., belief in the causal claim and willingness to purchase the product) than when the target has the same causal features as the referent. The results of three studies revealed the expected pattern for different products, promising different effects in different communication contexts. | INTRODUCTIONIn marketing communication, consumers often are exposed to causal claims stating that consuming a certain product will produce a desired effect. For example, consumers may be exposed to an ad for a new pill (the cause) that claims to treat acne (the effect).Occasionally, such claims are accompanied by data regarding cause-effect covariation, which typically compare the target product's performance to that of another product (hereafter, the referent) that aims to provide the same benefit. For example, the acne pill ad may describe a study performed on 60 teenagers who suffer from acne and provide data in which 25 out of the 30 participants who took the pill (83%) and 10 out of the 30 participants who took the referent (33%) were cured. These data indicate that there is a covariation between the cause (the pill use) and the effect (acne cure), in which the target outperformed the referent in producing the effect. This is because on the delta-p statistics (the normative measure of the degree of covariation between dichotomous variables [Cheng, 1997;Perales & Shanks, 2007]), there is a difference of 50% between the two conditional probabilities: the probability of the effect occurrence in the cause's presence and in the cause's absence (83−33%). Importantly, for the same data, the referent may vary; it could be a referent that has the same or fewer causal features than the target-features people believe have the causal power to produ...

show abstract

Causal Models Drive Preference between Drugs that Treat a Focal versus Multiple Symptoms

Cited by 3 publications

References 32 publications

The Effect of Similarity Between a Product’s Shape Properties and the Benefit Offered on Judgments and Preferences

The Effect of Similarity Between a Product’s Shape Properties and the Benefit Offered on Judgments and Preferences

The Attraction Effect in Experience‐based Decisions

The effect of previous causal knowledge on the persuasive strength of confirming covariation data

Contact Info

Product

Resources

About