Knowledge seeking and anonymity in digital work settings

STRATEGIC MANAGEMENT JOURNAL(2023)

引用 0|浏览9
暂无评分
摘要
Research Summary Employees often need knowledge from colleagues to complete tasks successfully. With distributed and remote work becoming more common, organizations increasingly rely on digital technologies, such as organizational platforms, to support members' knowledge exchange. We study factors that hinder employees from seeking knowledge from others on such platforms. We argue that individuals' seeking decisions depend on expected social-psychological costs and economic considerations and posit that both can be muted by anonymizing seekers. In two experiments, we test our conjectures and find that both types of expected costs reduce knowledge seeking. Social-psychological costs decrease individuals' knowledge seeking, while adding economic costs further reduces seeking. Moreover, in digital settings, female knowledge seekers are more sensitive to their identity being known than males and thus benefit more from anonymity.Managerial Summary Distributed and remote work arrangements, often subsumed under the label "new work", often rely on digital technologies to enable the exchange of relevant knowledge among colleagues. For example, in the US, two-thirds of S&P 500 firms already maintain some form of digital platform for knowledge exchange, although with mixed success. Employees may avoid seeking knowledge on these platforms both for social-psychological (a fear of appearing incompetent to their peers) and economic (fear of suffering career consequences) reasons. In a series of (lab and vignette) experiments, we show that both can reduce knowledge seeking and that these implicit costs can be minimized especially in digital contexts through anonymity (to minimize social-psychological consequences) and separating knowledge seeking platforms by hierarchical levels (to minimize potential economic consequences).
更多
查看译文
关键词
anonymity, knowledge exchange platforms, knowledge seeking, knowledge work, lab experiment, search costs, survey experiment, virtual work
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要