Purpose: The purpose of this article is to provide further insight into the relationship between supervisor support and transfer of training, by taking into account the effects of other transfer‐influencing factors in a systemic approach of the transfer process.Design/methodology/approach: A review of studies on factors affecting transfer of training was conducted, with a specific focus on the effects of supervisor support, resulting in the development of a research model of the transfer process. All components of the model were measured by means of questionnaires for former trainees and their supervisors, and stepwise regression analyses were carried out to examine the relationships in the model.Findings: Results indicate indirect relationships between supervisor support and transfer of training, by means of both trainees' motivation to transfer and the transfer climate. The indirect effect of supervisor support on transfer of training is only slight, however. Learning results are shown to be the strongest predictor of transfer of training.Research limitations/implications: Owing to the small sample size structural equation modelling techniques could not be used, thus limiting the possibility to test the model as a single entity. The use of perceptional measures implies the risk of response tendencies from trainees and supervisors. Further research using different measures and different timing of measurement during the training and transfer process is recommended.Practical implications: Results of this study indicate that supervisor support that is intended to enhance transfer of training can best be directed at improvement of the transfer climate at the workplace.Originality/value: The paper provides both researchers and practitioners with a further insight into the complex effects of supervisor support on transfer of training, indicating the importance of taking into account the effects of other transfer‐influencing factors.

The case of adaptive testing under a multidimensional response model with large numbers of constraints on the content of the test is addressed. The items in the test are selected using a shadow test approach. The 0–1 linear programming model that assembles the shadow tests maximizes posterior expected Kullback-Leibler information in the test. The procedure is illustrated for five different cases of multidimensionality. These cases differ in (a) the numbers of ability dimensions that are intentional or should be considered as “nuisance dimensions” and (b) whether the test should or should not display a simple structure with respect to the intentional ability dimensions.

Item-exposure control in computerized adaptive testing is implemented by imposing item-ineligibility constraints on the assembly process of the shadow tests. The method resembles Sympson and Hetter’s (1985) method of item-exposure control in that the decisions to impose the constraints are probabilistic. The method does not, however, require time-consuming simulation studies to set values for control parameters before the operational use of the test. Instead, it can set the probabilities of item ineligibility adaptively during the test using the actual item-exposure rates. An empirical study using an item pool from the Law School Admission Test showed that application of the method yielded perfect control of the item-exposure rates and had negligible impact on the bias and mean-squared error functions of the ability estimator.