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Benchmarking: Field Evidence from Singapore
Last registered on September 18, 2020

Pre-Trial

Trial Information
General Information
Title
Benchmarking: Field Evidence from Singapore
RCT ID
AEARCTR-0003216
Initial registration date
October 08, 2018
Last updated
September 18, 2020 10:29 PM EDT
Location(s)

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Primary Investigator
Affiliation
NUS Business School
Other Primary Investigator(s)
PI Affiliation
NUS Business School
PI Affiliation
NUS Business School
Additional Trial Information
Status
On going
Start date
2019-06-01
End date
2021-09-30
Secondary IDs
Abstract
How to motivate the implementation of better management practices? Benchmarking -- comparing relative performance and practices -- is a widely advocated but under-studied strategy. Theoretically, managers fail to leverage existing information and do not implement better management practices, mainly due to two reasons. For one, managers might suffer from information frictions and do not know the existence of better management practices. For the other, managers might pay limited attention to some practices and hold false beliefs over how those practices contribute to performance. Benchmarking works under both scenarios and motivates managers to implement better management practice by directly providing information as well as demonstrating the relationship between practices and performance. Following this line of reasoning, benchmarking is more likely to facilitate practice implementation for businesses with lower performance. Also, benchmarking has a stronger effect on practices to which business owners are more inattentive. To investigate the causal impact of benchmarking, we carried out a randomized controlled experiment among small business owners operating cooked food stalls in Singapore. Every owner was informed of their own performance. Additionally, treatment owners were offered with their relative performance and practices of top performers. The experiment will conclude in 2021 with visits to observe the treatment effect on practices and performance.
External Link(s)
Registration Citation
Citation
Hou, Yun, Ivan Png and Charmaine Tan. 2020. "Benchmarking: Field Evidence from Singapore." AEA RCT Registry. September 18. https://doi.org/10.1257/rct.3216-4.1.
Former Citation
Hou, Yun, Ivan Png and Charmaine Tan. 2020. "Benchmarking: Field Evidence from Singapore." AEA RCT Registry. September 18. http://www.socialscienceregistry.org/trials/3216/history/76160.
Sponsors & Partners

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Experimental Details
Interventions
Intervention(s)
We will randomly assign the food courts to two groups, and only enroll individually-owned vendors into the experiment. All the enrolled vendors within each food centre would be assigned to the same manipulation condition.

A. Control group;
B. Benchmarking group

Stall owners in all groups will be given a report on four measures of performance -- sales volume (number of plates sold per week), average cost per plate, profit per plate, and profit per owner working hour (the latter calculated as sales revenue minus total cost divided by total hours worked by the stall owner). We would also offer all subjects with the same set of general business operation suggestions.

Apart from performance indicators, the report to stall owners in the benchmarking group will also include their performance relative to the first, second, and third quartiles of all stalls selling cooked food. Meanwhile, the benchmarking group will receive four practice suggestions: two front-of-house practices and two back-of-house practices, that are randomly selected from five front-of-house practices and back-of-house practices.
Intervention Start Date
2019-08-01
Intervention End Date
2019-08-15
Primary Outcomes
Primary Outcomes (end points)
Sales revenue; cost; owner productivity; management practice implementations; exit
Primary Outcomes (explanation)
1. Owner productivity: We measure food stall productivity by owner productivity, calculated as the sales revenue minus total cost divided by the number of owner-hours worked.
2. Management practice implementation
3. Change intiatives
4. Social aspirations
5. The decisions to exit from the market
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
Our experiment will enroll individual vendors in each food court to participate in our study. We would approach about 20 hawker centres across Singapore and ideally we can recruit 10-20 subjects at each hawker centre.

At Time 0, we will conduct a pre-intervention survey to collect baseline information: sales revenue, cost, working procedures, management practices, adoption of technology, personal background, and psychological profiles.

We will compile the information from the pre-intervention survey and then calculate performance indicators as well as benchmarking information for all vendors. We will then randomly assign the food courts into control and benchmarking group.

At Time 1, upon completing data collection and analysis, we will contact the four groups and provide the corresponding information (control or treatment respectively).

8 month (Time 2), 12 months (Time 3) and 18 months (Time 4) respectively after the control/intervention visit, we will return to interview all participating vendors to collect information about changes in productivity and management and technology practices.
Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer.
Randomization Unit
Randomization by each section of stalls within one food centre.
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
20 food centres, 69 different sections, 200 vendors
Sample size: planned number of observations
vendors.
Sample size (or number of clusters) by treatment arms
35% vendors control and 65% vendors treatment.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
NUS Institutional Review Board
IRB Approval Date
2018-10-03
IRB Approval Number
S-18-281E