The influence of various learning transfer system factors on transfer motivation has been demonstrated in previous research. However, studies examining the influences of different factors simultaneously and at different points in time (i.e., before and after training programs) are missing. We seek to fill this gap by collecting data at two points of measurement to find out which Learning Transfer System Inventory ( LTSI ) factors influence IntroductionDeveloping employees' knowledge and skills through training is essential for organizations to successfully persist in today ' s challenging business world (Grossman & Salas, 2011 ). Regarding the amount of time and money that organizations invest in training, the consideration of why these training programs are success- ful, or why they fail, is crucial (e.g., Blume, Ford, Baldwin, & Huang, 2010 ;Holton, Bates, & Ruona, 2000 ). Motivation to transfer was found to be essential for training transfer (i.e., for the application of gained skills to the practice; e.g., Axtell, Maitlis, & Yearta, 1997 ;Burke & Hutchins, 2007 ;Devos, Dumay, Bonami, Bates, & Holton, 2007 ; Gegenfurtner, Festner, Gallenberger, Lehtinen & Gruber, 2009;Kauffeld, Bates, Holton, & Müller, 2008 ). Prior research has theoretically suggested (Gegenfurtner, Veermans, et al., 2009) and empirically begun to confirm the mediating function of motivation to transfer between factors in the learning transfer system and training transfer (Grohmann, Beller, & Kauffeld, 2014 ;Massenberg, Spurk, & Kauffeld, 2015 ;Nijman & Gelissen, 2011 ;van den Bossche, Segers, & Jansen, 2010 ). Therefore, if organizations know the antecedents of motivation to transfer, they have the chance to facilitate the training transfer (e.g., Gegenfurtner, Veermans, et al., 2009) and to optimize the benefit of training programs for both trainees and organizations (Laker & Powell, 2011 ).Based on the early typology of Baldwin and Ford ( 1988 ), factors in the learning transfer system are distinguished in factors inherent in the trainee, the training design, and the work environment. Holton ( 1996 ) has proposed a theoretical systematic model of the learning transfer system that contains 11 training-specific and 5 general factors. Despite the call to use all 16 learning transfer system factors simultaneously when studying influences within the system, researchers mostly have focused on single factors or factor groups (e.g. Moreover, Grossman and Salas ( 2011 ) have called for research to take a closer look at the factors in the learning transfer system by examining when (i.e., before or after training programs) the factors have the strongest impact on training outcomes. In addition, Holton et al. ( 2000 ) emphasized that learning transfer system factors should be measured in the pre-training stage to identify potential transfer problems before conducting a training program. Moreover, the consideration of motivation to transfer and its antecedents before the training is important because Gegenfurtner, Veermans, et al. (2009) assumed tha...

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While the notion of task–media fit is inherent to most theories on communication in virtual teams, past studies have largely concentrated on single, isolated tasks—hence neglecting sequential and contextual effects of media use. Building on project management frameworks, this study abstracts from the task level to the broader and more practical level of project phases, linking these to changes in media use. In particular, the study illustrates the weekly use of communication media within 34 student teams during a 3-month project. In line with team process phase models, results showed differences in media use between project phases: While face-to-face communication decreased, the use of leaner media (i.e., telephone and chat) increased in later project phases. Moreover, the variation of media use within the project phases increased over time, emphasizing the temporal dynamics of task–media fit. Finally, implications especially for project managers and recommendations for further research are discussed.

Purpose In competitive labor markets, promoting employees’ learning becomes a key challenge for companies. However, in small German craft companies, employee development is always connected with worries about employee turnover. This study aims to investigate the current informal learning strategies of craft workers and how they use the strategies, the effect of learning on employees’ internal and external marketability and beneficial workplace characteristics (autonomy, feedback). Design/methodology/approach An online prestudy (N = 131) explored current informal learning strategies. In the main study (N = 526), cluster analysis was applied to identify patterns of informal learning strategies. The relations of these patterns to workplace characteristics and marketability were investigated. Findings Four informal learning strategies were found (informal learning from oneself, from others, from other sources and from new media). Craft workers used combinations of the strategies (i.e. patterns): three learning patterns (balanced high, person-oriented and balanced low) differed in intensity and combination with the learning strategy use. More intense learning patterns were positively related to internal marketability but were not related to external marketability. Higher autonomy and feedback availability were related to higher learning engagement. Research limitations/implications Studies should have a broader view of informal learning strategies concerning different learning patterns. The use of new media is a learning strategy that might increase in the future. Practical implications Craft companies could promote different informal learning strategies without worries about employee turnover. Originality/value The study reveals how German small- and medium-sized enterprise employees use informal learning strategies in digitalized times and how human resources development can use informal learning strategies.