Crossings: Embedding personal professional knowledge in a complex online community environment
Purpose ‐ This paper aims to investigate how online communities of practice facilitate the embedding of personal professional knowledge in a complex online environment. Design/methodology/approach ‐ This research consisted of exploratory, interpretivist case research, using qualitative methods. Forty-one individuals from five online communities in a national professional development programme were interviewed. Additional data were drawn from diverse online records. Data were coded via text analysis. A wiki was used for participant feedback. Findings ‐ Embedding of new knowledge was facilitated by individuals' crossings between different engagement spaces ‐ communication and sense-making contexts. Community members repeatedly crossed between online and offline, visible and invisible, formal and informal, and reflective and active engagement spaces as they sought to meet diverse needs. As they did this, they had to continually recontextualise knowledge, adapting, varying and personalising it to fit the function, genre and conventions of each engagement space. This promoted the embedding of professional knowledge. The complex online environment in which they operated can be seen as providing a situation of enhanced polycontextuality, within which multiple boundary crossings facilitated strong personalisation. At the community level, knowledge convergence was fostered by the recurrence of dominant, powerful mnemonic themes. Research limitations/implications ‐ An opportunity exists to investigate the applicability of these findings in other online professional contexts. Originality/value ‐ The paper extends the concept of boundary crossing to crossings in a polycontextual online environment. It updates literature on communities of practice by outlining the dynamics of a complex online community system. It provides an explanation for how personal knowledge evolves to fit emerging trends and considers how information systems can support deep knowledge transfer.
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