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Trial Title Benchmarking Productivity: Evidence from the Field Benchmarking: Field Evidence from Singapore
Trial Status in_development on_going
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. Benchmarking -- the provision of information on relative performance and best practices -- has been widely advocated as a tool to help businesses improve performance. To investigate the causal impact of benchmarking on business performance, we plan a randomized controlled trial among owners of cooked food stalls 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. Our experiment will subject stall owners to three benchmarking treatments: performance, practices, and both performance and practices. We will also investigate the channel(s) by which benchmarking motivates businesses to change management practices and adopt new technologies.
Trial Start Date November 10, 2018 June 01, 2019
Trial End Date May 31, 2019 January 31, 2021
Last Published October 10, 2018 02:23 AM June 27, 2019 12:55 AM
Intervention (Public) 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. 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. Performance benchmarking group; C. Practice benchmarking group; D. Performance with practice 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). Apart from performance indicators, the report to stall owners in the performance benchmarking group will also include their performance relative to the first, second, and third quartiles of all stalls selling cooked food. The report to stall owners in the practice benchmarking group will also include three managerial or technology best practices, randomly selected from the ten practices identified by the regression analysis. The report to the combined performance with practice benchmarking group will include both performance and practice benchmarking information.
Intervention Start Date December 10, 2018 August 01, 2019
Intervention End Date December 31, 2018 August 15, 2019
Primary Outcomes (End Points) Sales revenue; cost; and labor productivity; productivity mindset; management and technology practices. Sales revenue; cost; owner productivity; 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. 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 practices: Include whether vendors keep systematic accounting records, adopt incentive-based salary schemes, get Facebook/Instagram accounts and so forth. 3. Technology practices: Include whether vendors use automatic kitchen equipment, install digital ordering systems and E-payment, adopt calling pager and so forth.
Experimental Design (Public) 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. We plan to conduct the randomized control trial in about 15 food courts in Singapore. We plan to enroll 20-50 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 as well as benchmarking information for all vendors. We will then randomly assign the food courts into control group, performance benchmarking group, practice benchmarking group, and performance with practice 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). 3 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.
Randomization Unit Randomization by food court. Randomization by each row of stalls within one food court.
Planned Number of Clusters 100 food courts, with 4-5 vendors from each food court. 15 food courts, with 20-50 vendors from each food court.
Planned Number of Observations 400-500 vendors. vendors.
Sample size (or number of clusters) by treatment arms 40 food courts under control, and 60 food courts under benchmarking. 20% vendors control, 25% vendors performance benchmarking, 25% vendors under practice benchmarking, and 30% performance with statistics benchmarking.
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