Designing and Conducting Laboratory Experiments

Operations management (OM) is a field with strong tradition of analytical modeling. Most of the early analytical work in OM was primarily optimization based and dealt with central planning for such problems as job‐shop scheduling, lot sizing, and queuing. Starting in the 1980s, OM researchers became interested in modeling strategic settings that involve interactions between firms. Today, OM models tackle problems that deal with supply chain coordination, competition, and cooperation, which examine incentives and objectives of firms as well as individual decision makers. This type of work requires a model of decision‐making at individual and/or firm level. Supply chains are not centralized, but consist of individual self‐interested firms – original equipment manufacturers (OEMs), different tiers of suppliers, transportation vendors, and retailers. These firms face uncertainty from the environment, such as production yield, processing times, and customer demand, as well as strategic uncertainty, which comes from the uncertainty about the actions of the other supply chain members. Traditionally, OM models assumed that firms are expected profit maximizers and are fully rational, meaning that they correctly anticipate the actions of the other supply chain members. Behavioral operations management (BOM) started in order to first test, and then improve, modeling assumptions about decision‐making. Schweitzer and Cachon (2000) is the seminal BOM paper that tested how individuals solve the “newsvendor problem.” It turned out that individuals generally do not solve the problem correctly, but are rather systematic and predictable in how their Designing and Conducting Laboratory Experiments