Network-Biased Technical Change: How Modern Digital Collaboration Tools Overcome Some Biases but Exacerbate Others

收入 业务 人际交往 工作(物理) 社交网络(社会语言学) 多样性(政治) 计算机科学 社会资本 营销 知识管理 公共关系 社会学 社会化媒体 心理学 万维网 财务 工程类 机械工程 社会心理学 社会科学 人类学 政治学
作者
Lynn Wu,Gerald C. Kane
出处
期刊:Organization Science [Institute for Operations Research and the Management Sciences]
卷期号:32 (2): 273-292 被引量:20
标识
DOI:10.1287/orsc.2020.1368
摘要

Using three years’ data from more than 1,000 employees at a large professional services firm, we find that adopting an expertise search tool improves employee work performance in billable revenue, which results from improvements in network connections and information diversity. More importantly, we also find that adoption does not benefit all employees equally. Two types of employees benefit more from adoption of digital collaboration tools than others. First, junior employees and women benefit more from the adoption of digital collaboration tools than do senior employees and men, respectively. These tools help employees overcome the institutional barriers to resource access faced by these employees in their searches for expertise. Second, employees with greater social capital at the time of adoption also benefit more than others. The tools eliminate natural barriers associated with traditional offline interpersonal networks, enabling employees to network even more strategically than before. We explore the mechanisms for these differential benefits. Digital collaboration tools increase the volume of communication more for junior employees and women, indicating greater access to knowledge and expertise than they had before adoption. The tools also decrease the volume of communication for people with greater social capital, indicating more efficient access to knowledge and expertise. An important implication of our findings is that digital collaboration tools have the potential to overcome some of the demographic institutional biases that organizations have long sought to change. It does so, however, at the expense of potentially creating new biases toward network-based features—a characteristic we call “network-biased technical change.”
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