Learning transfer is the main goal that organizations look for, when they invest in training. Due to the difficulty to evaluate transfer directly, many authors have already created different models of transfer factors, which allow diagnosing the aspects that facilitate or hinder transfer. Nevertheless, there is little evidence that these factors are, in fact, transfer predictors. In this chapter, we present the FET (Factors for the Evaluation of Transfer) model as an instrument to measure indirectly and predict training transfer. We expose the theoretical background of the FET model, based on three transfer dimensions (trainee, training, and organization), and on training results (achieved learning, intent to transfer, and deferred transfer). Through Exploratory Factor Analysis, we did a first validation of all the scales that are included in the model; and through multiple regressions, we ascertained the impact of the different variables on the deferred transfer. Results indicate that transfer factors have a greater predictive power on transfer (R 2∈=∈0.33), rather than achieved learning (R 2∈=∈0.14) and intent to transfer (R 2∈=∈0.16); and that the orientation towards job needs is the factor with the strongest impact on transfer (β∈=∈0.33, p∈<∈0.05). Then, the FET model allows us to reliably assess transfer factors as well as predict them moderately.
|Original language||American English|
|Title of host publication||Transfer of Learning in Organizations|
|Number of pages||24|
|Publication status||Published - 1 Oct 2013|