workexperiments

Work experiments are controlled engagements conducted in real work environments to evaluate the effects of changes to processes, tools, or organizational practices. They aim to generate evidence about what works in actual operating conditions, rather than in laboratory simulations.

Common designs include randomized controlled trials, A/B testing, factorial experiments, and quasi-experimental methods such as interrupted time series and difference-in-differences. The typical workflow involves defining a clear objective and outcome metrics, selecting a suitable sample, assigning treatment and control groups (randomly when possible), implementing the intervention, and collecting and analyzing data to estimate causal effects.

Applications span operations, product development, human resources, and service delivery. Examples include testing a new scheduling algorithm, piloting a software feature with a subset of users, reorganizing a shop floor layout, or evaluating a revised training program. Work experiments can also evaluate safety protocols and customer-facing processes in real time.

Benefits include evidence-based decision making, faster learning loops, and more efficient scaling of successful changes. Challenges include cost and disruption to workflows, potential bias and confounding factors, ethical considerations, and limited external validity if the sample is not representative. Controlling for spillovers and ensuring data privacy are common concerns.

Best practices emphasize clear hypotheses and metrics, preplanning of analysis, appropriate control conditions, pilot testing before broad rollout, stakeholder buy-in, and adherence to organizational ethics and data protection guidelines. Transparent reporting and, when feasible, preregistration of methods can improve credibility.

See also: field experiments, randomized controlled trial, A/B testing, implementation science, evidence-based management.