Benchmarking Productivity: Evidence from the Field
Last registered on October 10, 2018

Pre-Trial

Trial Information
General Information
Title
Benchmarking Productivity: Evidence from the Field
RCT ID
AEARCTR-0003216
Initial registration date
October 08, 2018
Last updated
October 10, 2018 2:23 AM 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
In development
Start date
2018-11-10
End date
2019-05-31
Secondary IDs
Abstract
Improving business productivity is an important issue for policymakers and management practitioners. Benchmarking has been advocated as a catalyst for remedial actions by businesses to increase productivity. However, there remains limited evidence on the effectiveness of benchmarking tools, and no studies exploiting randomized controlled trials. In this project, we aim to investigate whether and how benchmarking affects business productivity. We propose that benchmarking information acts as relative performance feedback and triggers a process of upward social comparison among business owners. We plan to conduct a randomized controlled trial in Singapore food courts. Food courts, which are owned by the government or commercial businesses, lease stalls to individual food and beverage vendors. This context is ideal for our research as those stalls compete in well-defined niches, use similar technologies, and do not suffer from any internal principal-agent problem. The aim of our experiment is to estimate the effect of benchmarking information offered to the stall vendors on the likelihood of adopting changes in management and technology, and labor productivity.
External Link(s)
Registration Citation
Citation
Hou, Yun, Ivan Png and Charmaine Tan. 2018. "Benchmarking Productivity: Evidence from the Field." AEA RCT Registry. October 10. https://www.socialscienceregistry.org/trials/3216/history/35566
Experimental Details
Interventions
Intervention(s)
We will randomly assign the food courts to two groups, and only enrol 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;

For the control group, we will provide vendors with their performance indicators. For the benchmarking group, apart from the performance indicators, we will advise those vendors their performance relative to food court benchmarks (25th, 50th and 75th percentile) and management and technology practices of the top quartile.
Intervention Start Date
2018-12-10
Intervention End Date
2018-12-31
Primary Outcomes
Primary Outcomes (end points)
Sales revenue; cost; and labor productivity; productivity mindset; management and technology practices.
Primary Outcomes (explanation)
1. Labor productivity: We measure food stall productivity by labor productivity, calculated as the contribution margin divided by the number of labor-hours worked. We define contribution margin as the sales revenue less the cost of raw materials and supplies.

2. Productivity mindset: Whether vendors seek for tips to improve their productivity.

3. Management practices: Include whether vendors keep systematic accounting records, adopt incentive-based salary schemes, get Facebook/Instagram accounts and so forth.

4. Technology practices: Include whether vendors use automatic kitchen equipment, install digital ordering systems and E-payment, adopt calling pager and so forth.
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
We plan to conduct the randomized control trial in about 100 food courts in Singapore. We plan to enrol 4-5 individual vendors in each food court to participate in our study.

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 for all vendors. We will then randomly assign the food courts into the control group and the benchmarking group, and calculate performance benchmarks for vendors in the benchmarking group.

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

1 month (Time 2) and 3 months (Time 3) 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 food court.
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
100 food courts, with 4-5 vendors from each food court.
Sample size: planned number of observations
400-500 vendors.
Sample size (or number of clusters) by treatment arms
40 food courts under control, and 60 food courts under benchmarking.
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