# More prizes are not always more attractive: factors increasing prospective sweepstakes participants’ sensitivity to the number of prizes

## Abstract

Sweepstakes that offer more identical prizes do not necessarily attract more participants. When deciding whether to participate in a sweepstakes presented in isolation (typical case), most consumers cannot evaluate if the number of prizes offered is “good” or “bad” within a certain range (1–10 prizes), because of the low evaluability of this attribute. Therefore, they do not perceive their odds of winning as better with more prizes, nor are they more likely to participate. Five studies detail this process and illustrate which individual and contextual factors (participation frequency in sweepstakes, availability of information about the usual number of prizes for comparable sweepstakes, visual reinforcement of the number of prizes by a consistent number of pictures) increase the evaluability of the number of prizes, which can reduce magnitude insensitivity. This study in turn provides managerial insights into how to design and advertise efficient sweepstakes.

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## Notes

1. The average probability estimate was 1.79 % (SD = .07). The answers did not follow a Gaussian distribution (skewness = 5.90 with a SE = .23; kurtosis = 37.50 with a SE = .45), so we performed a logarithmic transformation to support the parametric tests (after the transformation, skewness = .80, with a SE = .23, kurtosis = −.30 with a SE = .45).

2. The answers did not follow a Gaussian distribution (M = 1,763,523; SD = 11,672,458; skewness = 10.00 with a SE = .23; kurtosis = 102.03 with a SE = .46) so we performed a logarithm transformation (after the transformation, skewness = .30, with a SE = .23; kurtosis = −.18 with a SE = .46).

3. We also computed what should have been a “rational” probability of winning for each respondent, by dividing the number of prizes in the experimental condition to which they were assigned by their estimation of the number of participants. The distribution of these calculated probabilities was lognormal, so we took their natural logarithms. The values increased significantly with the number of prizes (F(3,103) = 34.06; p < .001). The contrast between 1 and 10 prizes was significant (d = 1.74, t(103) = 2.60, p = .01), as were the contrasts between 1 and 100 (d = 4.92, t(103) = 7.50, p < .001) and between 1 and 1000 (d = 5.88; t(103) = 8.87, p < .001). The contrast between 10 and 100 prizes was significant (d = 3.18, t(103) = 4.67, p < .001), but that between 100 and 1,000 prizes was not (d = .96, t(103) = 1.43, NS). Thus, the probability estimates by the respondents did not follow the same pattern as the “rational” probabilities calculated on the basis of their estimates of the number of entrants and the manipulated number of prizes. The former were magnitude insensitive within a certain range; the latter increased even for moderate variations of the number of prizes. This discrepancy helps rule out the idea that the magnitude insensitivity of probability estimates resulted from rational inferences about the number of participants.

4. The answers did not follow a Gaussian distribution (M = 723.22; SD = 1,839.51; skewness = 4.35 with a SE = .25; kurtosis = 19.09 with a SE = .49), so we performed a logarithm transformation (after the transformation, skewness = .11 with a SE = .25; kurtosis = 1.33 with a SE = .49).

5. After testing different values for c from .001 to 1, we adopted a value of .3, which generated a distribution of transformed probabilities that was close to Gaussian (after transformation, skewness = .84 with a SE = .23; kurtosis = −.00 with a SE = .46). Using smaller or larger values of c did not alter the key findings though.

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## Acknowledgments

The authors thank Yany Grégoire, Renaud Legoux, Christian Pinson, Bernard Pras, Marc Vanhuele, and Luk Warlop for their comments and suggestions on previous versions of this manuscript

## Author information

Authors

### Corresponding author

Correspondence to Sandra Laporte.

## Appendices

### Appendix A

Example ad for an actual sweepstakes, featuring as many pictures as prizes to win

### Appendix B

Visual analog scale (Woloshin et al. 2000) from Study 1 to measure estimated probability of winning

If you enter this lottery, what do you believe your chance of winning is?

Place an “X” in EITHER the magnifying glass OR the lower part of the scale to describe the chance you win if you participate to the random drawing.

### Appendix C

Example of a sweepstakes used in Study 3 for the evaluability manipulation

A cosmetics store gives some secret codes to its customers and invites them to come back 2 weeks later where six winning secret codes will be drawn. The customers who received a secret code have to visit the store 2 weeks later if they want to enter the drawing. Each of the [only in the high evaluability condition: six] winners will receive the women’s fragrance “Allure” de Chanel (100 ml, 3.4 fl. oz.).

How demanding are the requirements to enter the sweepstake?

(Not demanding at all 1 2 3 4 5 6 7 Very demanding)

Is the type of prize appropriate for 30-year old women?

(Not appropriate at all 1 2 3 4 5 6 7 Very appropriate)

Is the type of prize consistent with the organizer?

(Not at all consistent 1 2 3 4 5 6 7 Very consistent)

[High evaluability condition] What do you think about the number of winners (6)?

(It’s a low number 1 2 3 4 5 6 7 It’s a high number)

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Laporte, S., Laurent, G. More prizes are not always more attractive: factors increasing prospective sweepstakes participants’ sensitivity to the number of prizes. J. of the Acad. Mark. Sci. 43, 395–410 (2015). https://doi.org/10.1007/s11747-014-0389-2

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• DOI: https://doi.org/10.1007/s11747-014-0389-2

### Keywords

• Magnitude insensitivity
• Evaluability
• Sweepstakes
• Subjective winning odds