Too Big to Innovate? Theory and Experiment

Last registered on November 16, 2024
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Pre-Trial

Trial Information

General Information

Title
Too Big to Innovate? Theory and Experiment
RCT ID
AEARCTR-0014778
Initial registration date
November 07, 2024

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
November 15, 2024, 1:40 PM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Last updated
November 16, 2024, 1:00 AM EST

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Country
China
Region
Shanghai

Primary Investigator

Name
Mike Zhiren Wu
Affiliation
Monash University
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Other Primary Investigator(s)

PI Name
Xiangdong Qin
PI Affiliation
Shanghai Jiao Tong University
Contact Investigator
PI Name
Shicheng Jiang
PI Affiliation
East China University of Science and Technology
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Additional Trial Information

Status
On going
Start date
2021-12-04
End date
2024-12-31
Keywords
Behavior, Firms & Productivity, Lab
Additional Keywords
Firm Innovation Path Selection
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
The evidence on whether small or large firms hold an advantage in innovation remains mixed. This paper revisits this question by combining lab experiments with theory to explain the "firm size effect" on inovation. We consider a setting where the principal chooses between a rigid, risk-free project, providing all agents with a uniform payoff, and a flexible, riskier project (with an option to revert to the risk-free project after observing outcomes), which offers potentially higher payoffs for each agent with a certain probability. Some agents (who fail the risky project or suffer a loss due to the principal) may perceive the principal as unfair, leading them to engage in costly punishment or "shading", direted at the principal and other agents. This act cannot be contractually prohibited ex-ante. We hypothesize that shading is more likely to occur in larger firms, generating more excellent resistance to innovation. To reduce the potential shading, we test whether screening out agents with strong shading motives ex ante can foster firm innovation.
External Link(s)

Registration Citation

Citation
Jiang, Shicheng, Xiangdong Qin and Mike Zhiren Wu. 2024. "Too Big to Innovate? Theory and Experiment." AEA RCT Registry. November 16. https://doi.org/10.1257/rct.14778-1.1
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Intervention (Hidden)
Intervention Start Date
2021-12-04
Intervention End Date
2024-12-31

Primary Outcomes

Primary Outcomes (end points)
project carried out by the principal - initial choice (rigid, risk-free project / flexible, risky project)
adjustment made by the principal following the outcome of the risky project
ex-post punishment decision ("shading") of the agents
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Before the main experiment, subjects complete a real-effort task to earn initial endowments (200 ECUs) to ensure they have non-negative payoffs.

The main experiment includes three different treatments, each with a different group size (N=4, 6, or 8). Each group consists of one principal (P) and multiple agents (A), with roles randomly assigned at the start and maintained throughout the experiment.

The timeline is as follows: At the beginning each round, the principal must choose between a risk-free project (project 1) and a risky project (project 2). If the project 1 is chosen, both the principal and the agents can secure a payoff of 100 experiment currency units (ECUs, henceforth). If the project 2 is chosen, with two-thirds of the probability, an agent may succeed in the project and gets a payoff of 200 ECUs. With one-third of the probability, the agent may failure in the project and gets nothing. The principal’s payoff will be the average payoff of the agents. After checking the performance of each agent, the principal can decide whether to revert to the risk-free option by switching back to the project 1. Regardless of the choice of the principal (whether it is project 1/project 2/ project 2 → project 1), each agent is given the opportunity to impose costly punishment on others (“shading”). An agent can spend 5 ECUs to reduce the payoff of the principal or all other agents (with positive payoff) by 50 ECUs, or spend 10 ECUs to reduce the payoff of both the principal and all other agents (with positive payoff) by 50 ECUs. The game is repeated for 10 rounds and only one round is randomly selected to calculate the final payoff.

In addition to studying whether larger firms may face greater challenges in pursuing risky investment or innovation, we explore mechanisms to address this issue. To achieve this, we include an additional treatment (group size = 6), where a simple game is used to differentiate agents by testing whether they are willing to incur costs to reduce income inequality when treated unfairly. We then create groups by pairing a randomly selected principal with five agents of the same type. This pre-screening allows us to form groups with agents who have either weak or strong shading motives. Our aim is to test whether recruiting agents with weaker shading motives (or, conversely, screening out those with strong shading motives) is beneficial for fostering firm innovation.
Experimental Design Details
Randomization Method
Randomization done by the computer. We randomly assign students into different treatments.
Randomization Unit
individual level randomization
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
Around 180 groups in total
Sample size: planned number of observations
around 980 subjects (university students)
Sample size (or number of clusters) by treatment arms
Around 40 groups for treatment with group size = 4, 6, 8
Around 60 groups for treatment with group size = 6 (with type classification)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials