Nudging innovation: The effect of salience
Last registered on January 19, 2020

Pre-Trial

Trial Information
General Information
Title
Nudging innovation: The effect of salience
RCT ID
AEARCTR-0005214
Initial registration date
December 27, 2019
Last updated
January 19, 2020 5:21 AM EST
Location(s)

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Primary Investigator
Affiliation
University of Fribourg
Other Primary Investigator(s)
PI Affiliation
University of Fribourg
PI Affiliation
University of Fribourg
Additional Trial Information
Status
In development
Start date
2020-01-24
End date
2021-04-30
Secondary IDs
Abstract
Innovation is a core component of a growing economy. Yet, it is still to be fully understood
how to best foster it. We study in the laboratory the effect of making strategies or profits
salient on the choice of participants to innovate using a management game. To do so, we use
participants’ generated reports and vary their content to be either strategy or profit focused,
keeping everything else, including a pay-for-performance incentives scheme, fixed. We expect
that participants who are asked to report their strategies will be more innovative than those
who report their profits.
External Link(s)
Registration Citation
Citation
Matthewes, Elisa, Anis Nassar and Christian Zihlmann. 2020. "Nudging innovation: The effect of salience." AEA RCT Registry. January 19. https://doi.org/10.1257/rct.5214-1.2000000000000002.
Experimental Details
Interventions
Intervention(s)
The laboratory task is adapted from Ederer&Manso (2013). We employ the pay-for-performance scheme and keep that fixed. Our treatments target an individual's perceived salient aspect of the game: We ask subjects to report a specific aspect of the game and we expect them, through this shift of attention, to focus on the very aspect we ask them to report. Concretely, the treatments are as follows:
1. Control
No reporting. Mimics 1-to-1 the pay-for-performance treatment (Ederer&Manso (2013)).
2. Profit treatment
After the decisions are made, in period 3,6,9 and 12, subjects are requested to report
their profit of the last three periods. Along with the wording
"Please report the profit
of the last three periods", subjects will face an entry mask, where they need to enter
the profits of the last three period.
3. Strategy treatment
After the decisions are made, in period 3,6,9 and 12, subjects are requested to report
their strategy of the last three periods.

For further details, refer to the pre-analysis plan.
Intervention Start Date
2020-01-24
Intervention End Date
2020-04-30
Primary Outcomes
Primary Outcomes (end points)
Exploration behavior, proxied by different variables (see Ederer & Manso 2013):
- maximal per-period profit
- profit in the final period
- location in the final period
- longest duration of an exploration phase
- standard deviation from three continuous variables
Primary Outcomes (explanation)
Exploration phase is constructed. Following Ederer & Manso (2013), we classify subjects as having entered an exploratory phase as soon as they chose a location
other than the default (business district). An explorative phase ends when i) a subject switches back to the
default location (business district) or ii) when a subject does not change location and color and simultaneously
does not change the three continuous variables (lemonade content, sugar content, price) by more than 0.25
units.
Secondary Outcomes
Secondary Outcomes (end points)
Attention (or Effort)
Secondary Outcomes (explanation)
To measure attention, we will construct a measure based on the effort sheet subjects fill out: We will compute the proportion of filled out fields for each subject, for the columns of the strategy choices as well as for for the profit columns.
Experimental Design
Experimental Design
Our experimental task is adapted from Ederer & Manso (2013). Subjects solve a task in which
they are facing a trade-off between exploration and exploitation: Participants manage a virtual
lemonade stand. Over 20 experimental periods, participants decide on multiple parameters
such as the recipe of the lemonade (sugar and lemonade content as well as color), the location
of the lemonade stand and the price of a cup of lemonade. The possible combinations of these
choice variables amounts to 23’522’994 combinations
. Participants are compensated accord-
ing to the realized profits. Thus, participants aim is to maximize the profit of the lemonade
stand, and with it, their own earnings. Participants do not know the profits associated with
each of the available choices. Participants receive a default strategy, i.e. the choices and the
associated profit of a fictitious previous manager. The default strategy is not the
most profitable strategy.
After each period, participants learn the profit for the implemented choices. Also, they
receive a brief customer feedback, implemented by having the computer randomly select one
of the three continuous choice variables (price, lemon or sugar content) to provide a binary
feedback. Consequently, the feedback is only informative for the location in which the subject
chose to sell in the current period.
The task is characterized by an exploration-exploitation trade-off with two main behaviors:
either fine-tuning the default strategy and yielding a profit similar to the previous manager
(exploitation), or experimenting with new strategies and taking the associated risk of failure
but also the chance of success (exploration). Parameters are designed in way that exploration
will increase chances to identify the strategy that leads to the global maximum while exploita-
tion rather leads to local maxima. The parameters to calculate the profits of the lemonade
stand are one-to-one adapted from Ederer & Manso (2013).
The payoff is determined by a standard pay-for-performance incentive scheme: participants
are paid 50% of the profits they make during all the 20 periods.
Then, we implement the treatments as describe in the intervention section.
After the lemonade stand task, we elicit the following individual characteristics
through a survey: demographics, risk preferences (Falk et al. 2018) and Big-5 (Lang et al.
2011).
Experimental Design Details
Not available
Randomization Method
Random cubicle/computer assignment in the laboratory through a random draw.
Randomization Unit
Individual
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
No clusters, all sessions conducted in the laboratory (Strasbourg).
Sample size: planned number of observations
We will follow a sequential analyses approach as proposed by Lakens (2014). For further details and full specification of the sequential analyses procedure, please refer to the pre-analysis plan. Expected sample size: between 90 to 180 subjects.
Sample size (or number of clusters) by treatment arms
We will follow a sequential analyses approach as proposed by Lakens (2014).
For further details and full specification of the sequential analyses procedure, please refer to the pre-analysis plan.
Expected sample size for each treatment arm: between 30 to 60 subjects, maximally 100 subjects per treatment arm.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
All details of the sequential analyses procedure are in the pre-analysis plan. Here a summary: Our sequential analysis plan will follow the following detailed procedure: Based on an expected effect size of Cohen’s d = 0.50, a power analysis indicated that for a two-sided test with an alpha of .05, a desired statistical power of .8, and two looks using a linear spending function, a total of 180 participants are needed (60 per group). If the expected difference is significant at the first interim analysis (after 90 participants or time = .50, with an alpha boundary of .025) the data collection will be terminated. The data collection will also be terminated when the observed effect size is smaller than the smallest effect size of interest, which is set at d = 0.3875 based on the researcher’s willingness to collect at most 300 participants for this study, and the fact that with one interim analysis 300 participants provide .8 power to detect an effect of d = 0.3875. If the interim analysis reveals an effect size larger than 0.5, but while p>.025, the data collection will be continued until 60 participants (per group) have been collected. If the effect size lies between the smallest effect size of interest (d = 0.3875) and the expected effect size (d = 0.5), the planned sample size will be increased based on a conditional power analysis to achieve a power of .9 (or to a maximum of 100 participants per group, or 300 participants in total). The second analysis is performed at an alpha boundary of .0358.
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
IRB of the Department of Psychology, University of Fribourg
IRB Approval Date
2019-07-30
IRB Approval Number
471-1
Analysis Plan

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