book chapter · 2023
Transactive Memory Systems and Learning
Leila Bighash, Michael Farzinpour
Cited by 0Abstract and details only
Abstract
This chapter introduces readers to the concept of a transactive memory system (TMS) or a group-level information-processing system that divides the labor among group members for information storage and task completion. The metaphor of a computer network is explained as an illustration of the working of a TMS among groups of people (and perhaps, people and their technologies if we consider them group members). The next section discusses the development of TMSs for groups and goes into detail about several factors that can influence whether a TMS emerges. The processes that group members engage in are then discussed, particularly focusing on the terminology that TMS scholars use to describe the functioning of a TMS, specifically transactive encoding, information allocation, information storage/manipulation, directory updating information retrieval, and retrieval coordination. Finally, the various outcomes that TMSs can foster are discussed, as well as some practical suggestions for those building and managing groups.
Cite this paper
Bighash, L., & Farzinpour, M. (2023). Transactive Memory Systems and Learning. https://doi.org/10.4324/9781003227458-16
Bighash, Leila, and Michael Farzinpour. “Transactive Memory Systems and Learning.” 2023, pp. 187–99, https://doi.org/10.4324/9781003227458-16.
Bighash, Leila, and Michael Farzinpour. 2023. “Transactive Memory Systems and Learning.” https://doi.org/10.4324/9781003227458-16.
Bighash, L. and Farzinpour, M. (2023) ‘Transactive Memory Systems and Learning’. Available at: https://doi.org/10.4324/9781003227458-16.
@incollection{bighash2023transactive,
title = {{Transactive Memory Systems and Learning}},
author = {Bighash, Leila and Farzinpour, Michael},
pages = {187--199},
year = {2023},
doi = {10.4324/9781003227458-16}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.