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A Scientific Approach to SME Productivity
Last registered on February 12, 2019

Pre-Trial

Trial Information
General Information
Title
A Scientific Approach to SME Productivity
RCT ID
AEARCTR-0003875
Initial registration date
February 12, 2019
Last updated
February 12, 2019 4:57 PM EST
Location(s)
Primary Investigator
Affiliation
ICRIOS, Bocconi University
Other Primary Investigator(s)
PI Affiliation
City, University of London
PI Affiliation
ICRIOS-Bocconi University
PI Affiliation
ICRIOS-Bocconi University
PI Affiliation
Oxford University
Additional Trial Information
Status
In development
Start date
2018-12-01
End date
2019-11-30
Secondary IDs
Abstract
SMEs are a vital part of the economy of almost all Countries (including the UK). However, their contribution to wealth creation, GDP and employment is lower than expected since the share of high-productivity SMEs remains substantially low (especially in the UK). The UK Government has recognised that SME productivity is a concern and has worked since 2011 on an action plan to improve it. Recent evidence shows that a key barrier to SMEs development is the shortage of programmes that educate and support businesses. Other studies also suggest that the lack of systematic managerial approaches and evidence based decision-making also hinder the growth and productivity of SMEs.
It is therefore important to ascertain what practices might improve SMEs’ decision-making processes - and, therefore, result in higher productivity. Addressing this research question would allow to better focus national and local government policies supporting SMEs, and more specifically to offer management training programs with proven ability to enhance small business owners and managers decision making. This project aims to address this question by testing the effect on SMEs’ productivity and growth of a set of novel business practices labelled ‘a scientific approach to decision making’. Extant studies converge in suggesting that SMEs, and especially microbusinesses, need to improve their ability to make rigorous strategic decisions.
This suggests that there is considerable room for improvement in the way they are managed and that there is a clear need to have them learn effective decision making tools.
The project will focus on micro-businesses, which represent the lion share of all businesses in most Countries (including UK), but account for only a small fraction of turnover. This suggests that there is considerable room for improvement in the way they are managed. Moreover, identifying solutions to improve these firms’ productivity could lead to particularly impactful results and a substantial return on investment. A focus on micro-businesses is also consistent with our experimental research design, with the intervention targeted to business owners or managers whom, in microbusinesses make most decisions (and especially strategic ones) with direct impact on performance.
More specifically we want to know if firms adopting a scientific approach to decision making are more productive than non adopters. Using an experimental research design we will compare two samples of firms, similar -on average- on all covariates. We will intervene on the two groups offering management training and then test if the pre/post-intervention productivity increase of firms treated with the scientific approach is larger than the pre/post productivity increase in the control group (which gets standard management training).

External Link(s)
Registration Citation
Citation
Camuffo, Arnaldo et al. 2019. "A Scientific Approach to SME Productivity." AEA RCT Registry. February 12. https://doi.org/10.1257/rct.3875-1.0.
Former Citation
Camuffo, Arnaldo et al. 2019. "A Scientific Approach to SME Productivity." AEA RCT Registry. February 12. http://www.socialscienceregistry.org/trials/3875/history/41425.
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Experimental Details
Interventions
Intervention(s)
The intervention consists in a management training and mentorship program comprising 8 sessions (3 hours each) plus homeworks and reviews.
The training program offers to all participants state-of-the-art tools (like the business model canvas, customer validation and development, the balanced scorecard and strategy maps) to diagnose their business, assess their strategy/business model and, if necessary, re-orient or change it. However, the treatment group will also be instructed about how to do this following a scientific approach. The Scientific Approach consists of making decisions following a set of behavioural routines – similar to those used by scientists- comprising four key components:
1. Articulation of a “theory” to design a business model grounded on a well-defined framework of the targeted customer problem;
2. Formulation of explicit/testable/falsifiable hypotheses about the consequences of actions;
3. Collection and analysis of evidence and data or design and execution of experiments to test these hypotheses;
4. Open and critical analysis, evaluation and reflection of the evidence.
Our ‘theory of change’ builds on the intuition, supported by prior research, that, under conditions of uncertainty, many business owners overestimate (false positives) or underestimate (false negatives) the potential of the strategies they follow. Instead, microbusinesses that use a scientific approach make evidence-informed decisions and reduce the number of ‘incorrect’ decisions made. This is expected to have a positive impact on their productivity.
Intervention Start Date
2019-02-13
Intervention End Date
2019-04-13
Primary Outcomes
Primary Outcomes (end points)
1. Level of adoption of the scientific approach. More specifically we will measure the pre-post intervention average level of adoption of the scientific approach in both the treatment and control groups using a purposely designed instrument (interview and survey) which allows to calculate an additive measure of adoption of the scientific approach broken down into its 4 components. We expect to detect a significant difference in the increase in the level of adoption of the scientific approach between treated and control microbusinesses.
2. Number, type and timing of strategic iterations -significant business changes- (short-term, 3 months since the start of the intervention). We expect to detect no difference or a slight difference in the number of strategic iterations during and after the intervention between the treated and the control group, with better adopters iterating earlier and more radically.
3. Productivity, measured according to the guidelines of the ONS productivity handbook
Primary Outcomes (explanation)
1. Level of adoption of the scientific approach. Measured using a purposely designed instrument (interview and survey) which allows to calculate an additive measure of adoption of the scientific approach broken down into its 4 components.
2. Strategic iterations. Measured as a count (and the timing) of the strategic re-orientations (e.g. change in customer segments, product/ service offered etc.)
3. Labor, capital, materials, energy and multiple factor productivity as defined in the ONS productivity handbook
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
Parallel experimental design, with two arms, pure randomization at the microbusiness level and a 50%/50% allocation ratio.
Experimental Design Details
We will randomize participants into treatment and control. Once assigned to the treatment or control group, participants will be randomly assigned to instructors/classes. We will run randomization checks on a wide set of covariates (individual and microbusiness characteristics) to ensure balance. Randomization checks will be conducted into treatment (between treatment and control groups) and within and across class/instructors. Further randomization checks will be conducted across intervention calendar dates and between and within registered and non registered microbusinesses.
Randomization Method
Participants randomized into treatment and control using STATA randomization procedure.
Randomization Unit
Firm (microbusiness)
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
240 firms
Sample size: planned number of observations
2400 (240 firms X 10 monthly observation)
Sample size (or number of clusters) by treatment arms
120 microbusinesses control, 120 microbusinesses treatment
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Our power calculations uses a target impact approach and tries to calculate the sample size using as impact estimates the outcome of previous studies in similar contexts (Camuffo et al. 2019) and participants data. However, we also conducted sensitivity analysis simulating how the smallest true impact varies as a function of the sample size. Our target sample size of 240 microbusinesses stems from these analyses. These calculations will be refined in the piloting stage of the project and based on the actual size and past performance of the microbusinesses that will be recruited as participants. Nonetheless, assuming that at the beginning of the RCT the average revenue of the microbusinesses in the treatment and control groups is the same and equal to £70,000, with a standard deviation of £100,000, a sample size of 240 would more than suffice to confidently detect a 0.35 effect size difference, i.e. an increase in the average revenues of the treated microbusinesses of 50% relative to the control group. We plan to re-run our experimental power calculations using both G-Power and STATA as soon as we have the final information about the participants and possibly adjust our numbers. At any rate, our experimental design incudes repeated data collection over time, which will help to increase the power of our tests. We will use ANCOVA and multiple measures to improve power (MacKenzie, 2012).
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Cass Research Ethics Committee
IRB Approval Date
2018-12-15
IRB Approval Number
Ethics ETH1819-0351: Prof Elena Novelli (Low risk)
Post-Trial
Post Trial Information
Study Withdrawal
Intervention
Is the intervention completed?
No
Is 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