Summary to come.

Despite continued calls for data sharing and replication in management and social science research, there remains a gap between espoused values (sharing/replication is ‘good’) and revealed preference (we don’t share/replicate). Why is this the case, and can anything be done about it? We identify and address incentive issues by adapting and extending algorithms for synthetic data generation for use in management and social science research. Simulation results and application to actual data sets demonstrate the potential of these methods to enable researchers to produce and share synthetic data, thereby promoting replication, extension, and ultimately, knowledge generation, while removing constraints and disincentives of sharing authentic data.