亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

Prismatic Trust: How Structural and Behavioral Signals in Networks Explain Trust Accumulation

业务 产业组织 计算机科学 营销 微观经济学 经济
作者
Giuseppe Soda,Aks Zaheer,Michael Park,Bill McEvily,Mani Subramani
出处
期刊:Management Science [Institute for Operations Research and the Management Sciences]
卷期号:71 (5): 3966-3982 被引量:8
标识
DOI:10.1287/mnsc.2021.02810
摘要

The predominant focus of the organizational literature on trust has been on direct interactions between actors. Whereas this emphasis has solidified our understanding of the dyadic foundations of trust, we know relatively little about the mechanisms of trust creation in network contexts. In this paper, we introduce the network mechanism of prismatic trust to explain why some actors are more trusted than others. Specifically, we posit that networks act as prisms that generate signals of trustworthiness based on not only actors’ positions in the social structure, but also their networking behavior. Moreover, we also theorize that the combination of signals from network structure and behavior amplifies trust accumulation in network actors. We test our predictions using data from an online social trading platform with more than 28,000 traders across 38 weeks. We find that traders who occupy positions of higher status in the network and those who express positive sentiments in the content of their communications (networking behaviors), accumulate more trustors. Furthermore, the positive effects of network status and the expression of positive sentiments on trust accumulation are mutually reinforcing. In sum, we contribute to the organizational literature on trust by proposing the role of a prismatic view in explaining how trust accumulates in network actors as a function of their position in social structure, their networking behavior, and a combination of the two. This paper was accepted by Isabel Fernandez-Mateo, organizations. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.02810 .
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
3秒前
热情的修哥完成签到 ,获得积分10
6秒前
芊芊墨客发布了新的文献求助10
8秒前
荼蘼发布了新的文献求助10
9秒前
16秒前
芊芊墨客完成签到,获得积分10
17秒前
情怀应助Hedy采纳,获得10
18秒前
30秒前
Jasper应助聪聪采纳,获得10
34秒前
zz发布了新的文献求助10
35秒前
zachary009完成签到 ,获得积分10
38秒前
41秒前
彭于晏应助大口吃榴莲采纳,获得10
46秒前
楚狂接舆完成签到,获得积分10
46秒前
54秒前
59秒前
科研通AI6.1应助zz采纳,获得10
1分钟前
1分钟前
wuyun9653发布了新的文献求助10
1分钟前
1分钟前
1分钟前
1分钟前
1分钟前
聪聪发布了新的文献求助10
1分钟前
1分钟前
高挑的涛发布了新的文献求助10
1分钟前
桐桐应助dqbhxwx采纳,获得10
1分钟前
Akim应助元力采纳,获得10
1分钟前
Hedy关注了科研通微信公众号
1分钟前
1分钟前
科研通AI6.2应助碘塞罗宁采纳,获得10
1分钟前
awang完成签到,获得积分10
1分钟前
1分钟前
2分钟前
科研通AI2S应助科研通管家采纳,获得10
2分钟前
思源应助科研通管家采纳,获得10
2分钟前
2分钟前
大胆的碧菡完成签到,获得积分10
2分钟前
dqbhxwx发布了新的文献求助10
2分钟前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
PowerCascade: A Synthetic Dataset for Cascading Failure Analysis in Power Systems 2000
Picture this! Including first nations fiction picture books in school library collections 1500
Signals, Systems, and Signal Processing 610
Unlocking Chemical Thinking: Reimagining Chemistry Teaching and Learning 555
CLSI M100 Performance Standards for Antimicrobial Susceptibility Testing 36th edition 400
Cancer Targets: Novel Therapies and Emerging Research Directions (Part 1) 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6362063
求助须知:如何正确求助?哪些是违规求助? 8175716
关于积分的说明 17223995
捐赠科研通 5416769
什么是DOI,文献DOI怎么找? 2866561
邀请新用户注册赠送积分活动 1843771
关于科研通互助平台的介绍 1691516