Impact of Alignment between Leadership Style and Incentive Systems

Last registered on January 18, 2022

Pre-Trial

Trial Information

General Information

Title
Impact of Alignment between Leadership Style and Incentive Systems
RCT ID
AEARCTR-0008801
Initial registration date
January 14, 2022

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
January 18, 2022, 4:04 PM EST

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

Locations

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Primary Investigator

Affiliation
The Hotel School, Cornell SC Johnson College of Business, Cornell University

Other Primary Investigator(s)

PI Affiliation
Arizona State University
PI Affiliation
The Wharton School, University of Pennsylvania

Additional Trial Information

Status
In development
Start date
2022-01-12
End date
2022-12-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
In partnership with a US apparel retailer with over 10,000 employees and 800 stores, we plan a randomized control trial to study the role of leadership style in mediating the effectiveness of different types of incentives. The proposed study involves the collection of detailed socio-demographic and performance information about the sales associates and store managers, including the administration of a survey instrument to capture the leadership style of the latter. In addition, we will implement a field experiment that will vary the prize that stores can win in a series of tournaments we plan to organize. The prizes will be either monetary or non-monetary. We expect to show that servant leaders, that instill a team spirit among their subordinates, respond more to non-monetary incentives, while non-servant leaders respond more to monetary ones.
External Link(s)

Registration Citation

Citation
Casas-Arce, Pablo, Francisco de Asis Martinez Jerez and Joseph Moran. 2022. "Impact of Alignment between Leadership Style and Incentive Systems ." AEA RCT Registry. January 18. https://doi.org/10.1257/rct.8801
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
Each region will be assigned to one of three treatments: sales contest with monetary prize, sales contest with non-monetary prize, and control group without any contest.
Intervention Start Date
2022-01-17
Intervention End Date
2022-01-31

Primary Outcomes

Primary Outcomes (end points)
Sales per employee
Growth in the average number of items per transaction.
Average number of items per transaction
Primary Outcomes (explanation)
Number of items per transaction: number of items in each sale ticket. For instance, if the ticket (transaction) only includes one shirt, then the number of items is 1. If the ticket includes one shirt, a pair of socks, a cap and a pair of gloves, then the number of items in that transaction is 4.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We instructed the partner company to measure the leadership style of the store managers. The measurement took place in the first two weeks of December.
We will assign regions to each of the three experimental groups (monetary incentive, non-monetary incentive, and control) and within each region we will pair stores randomizing the pairs using a two-by-two blocking. The two dimensions of the blocking are product offering (fashion versus sport) and store sales volume (sales above and below the median of the region).
The contests will last for two weeks. We will test our hypotheses using sales and points earned during the contest in a Diff-in-Diffs framework.
Experimental Design Details
Not available
Randomization Method
We will have the regions assigned to treatments using the synthetic control method described in Abadie, A., & Zhao, J. (2021). Synthetic Controls for Experimental Design (Unpublished Working Paper).
Then we will pair stores randomizing within four blocks in each region. The blocks will be formed using a 2-by-2 classification of the stores: one dimension being type of product offering (fashion vs. sport) and the other by sales volume of the store. The randomization will be done in the office by a computer.
Randomization Unit
We will assign regions to treatments (monetary incentive, non-monetary incentive, and control) using the synthetic control method.
We will pair stores in tournaments using a randomized routine that blocks stores by product offering and sales volume.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
About 800 stores
Sample size: planned number of observations
About 10,000 employees.
Sample size (or number of clusters) by treatment arms
About 275 stores in each of the groups (monetary incentives, non-monetary incentives, control)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Using the sales data from past contests, we conducted power analyses to determine whether our planned intervention could identify effect sizes similar in magnitude to past changes in sales activity due to contests. The results may be found in more detail in the analysis plan. We utilized the multiple regression partial correlation framework per Cohen (1988), which identifies the minimum identifiable change in regression model R2 and minimum identifiable squared partial correlation between a hypothetical independent variable and an outcome variable as a function of sample size, desired power level, statistical significance level, the number of independent variables included in the regression model, and the number of control variables included in the regression model. As a baseline multiple regression model for the power analysis, we utilize a regression of an outcome variable (for brevity, we utilize sales revenue only) measured at the store-day or employee-day level on a vector of store, day of week, and month fixed effects. For store-level analyses, we calculate the number of control variables in the baseline regression model as 1,061 (the total number of store, day of week, and month fixed effects); for employee-level analyses, we calculate the number of control variables in the baseline regression model as 1,065 (the above plus four potential employee-level controls). We calculate minimum detectable effect size and partial squared correlation in models designed to test the hypotheses of the project, for several conventional levels of power (0.80, 0.85, and 0.90) and sample sizes corresponding to a two-, three-, and four-week contest in that order. For comparative purposes, we calculate partial squared correlations obtained from a multiple regression of sales revenue on a contest indicator and store, day of week, and month fixed effects at the store-day and employee-day levels. In all cases, the squared partial correlations of the contest indicator presented in the latter, of 0.0058 and 0.00034 (corresponding to partial correlations of 0.076 and 0.018, respectively), at the store-day and employee day levels, respectively, are well in excess of the minimum detectable squared partial correlations. This indicates that our study has sufficient power to detect a change in sales similar in magnitude to that observed in historical contests run by the company.
IRB

Institutional Review Boards (IRBs)

IRB Name
Cornell University Institutional Review Board
IRB Approval Date
2022-01-14
IRB Approval Number
2112010745
Analysis Plan

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