Knowledge transmission in the second part of careers: does formal training matter?
Authors: Messe Pierre-Jean , Greenan Nathalie 
Published: April 2023

PurposeThis paper examines to what extent formal training targeted to workers aged 45 and over could enhance their knowledge transmission activities specifically in changing work environments. This is a key issue for human resources practitioners. Allowing older workers to keep on interacting with their colleagues and transmitting their knowledge acquired through experience reduces the risk for firms of losing critical knowledge assets.Design/methodology/approachThe authors use French-matched employer–employee data to estimate the effect of participation in training sessions intended to support change on the probability for workers aged 45–59 of frequently showing work practices to their co-workers. To account for selection bias in training, the authors reduce the group of untrained workers to those who wanted to attend a training session but had to cancel their participation for exogenous reasons. Leuven and Oosterbeek (2008) show that this is a valid approximation of a random assignment to training.FindingsTraining with the intention to support change for workers aged 45 and more significantly increases knowledge transmission for training participants. This effect is not strictly related to a supervising role as it is significant for workers without subordinates; it holds when the authors address the selection bias in training by narrowing down the comparison group. When training comes as a response to mitigate the potential negative effects of technological or organizational changes in the work environment, it helps workers aged 45–59 maintain their contribution to the knowledge base of the production.Research limitations/implicationsOur findings suggest that two main aspects have to be borne in mind when assessing the effectiveness of training for older workers. First, the reasons for training must be carefully considered, especially if it occurs in response to technological or organizational change in the workplace. Second, the continuation of interactions between older workers and their co-workers must be factored. If the public debate acknowledges that employee learning and development is critical in times of structural change and crisis, the outcomes of knowledge transmission within workplaces in terms of job satisfaction, turnover intentions, productivity or innovation, which the authors do not cover in this paper, deserve further investigations. In particular, the authors believe that studying how the training that supports technological and organizational change influences the relationship between age diversity and firm productivity is a promising avenue for future research.Practical implicationsThe implication of this article for human resource managers is that there may be a substantial cost to not updating the skills of older workers after technological or organizational change. Indeed, it is likely that a large proportion of jobs will only be partially automated, which implies that while some tasks will disappear, rendering the corresponding skills obsolete, others will persist and the skills associated with them will remain useful to organizations. If older workers are excluded from their work collectives after these changes, because their skills have not been updated through training, the knowledge from their accumulated experience that remains valuable will be irrevocably lost when they retire.Originality/valueThis study sheds a new light on the effectiveness of older workers’ training. Some contributions argue that training for older workers is not very effective because it has no significant effect on employment duration, earnings or relative productivity. The authors show that specific types of training to update skills after a technological or organizational change allow older workers to keep interacting with their co-workers and pass on their knowledge gained through experience, thereby reducing the risk for firms of losing critical knowledge assets.