A Microstructural Approach to Self-Organizing: The Emergence of Attention Networks

概化理论 知识管理 计算机科学 相互依存 组织理论 集合(抽象数据类型) 组织研究 串联(数学) 组织行为学 组织学习 社会学 心理学 社会心理学 管理 经济 社会科学 发展心理学 数学 组合数学 程序设计语言
作者
Marco Tonellato,Stefano Tasselli,Guido Conaldi,Jürgen Lerner,Alessandro Lomi
出处
期刊:Organization Science [Institute for Operations Research and the Management Sciences]
卷期号:35 (2): 496-524 被引量:6
标识
DOI:10.1287/orsc.2023.1674
摘要

A recent line of inquiry investigates new forms of organizing as bundles of novel solutions to universal problems of resource allocation and coordination: how to allocate organizational problems to organizational participants and how to integrate participants’ resulting efforts. We contribute to this line of inquiry by reframing organizational attention as the outcome of a concatenation of self-organizing, microstructural mechanisms linking multiple participants to multiple problems, thus giving rise to an emergent attention network. We argue that, when managerial hierarchies are absent and authority is decentralized, observable acts of attention allocation produce interpretable signals that help participants to direct their attention and share information on how to coordinate and integrate their individual efforts. We theorize that the observed structure of an organizational attention network is generated by the concatenation of four interdependent micromechanisms: focusing, reinforcing, mixing, and clustering. In a statistical analysis of organizational problem solving within a large open-source software project, we find support for our hypotheses about the self-organizing dynamics of the observed attention network connecting organizational problems (software bugs) to organizational participants (volunteer contributors). We discuss the implications of attention networks for theory and practice by emphasizing the self-organizing character of organizational problem solving. We discuss the generalizability of our theory to a wider set of organizations in which participants can freely allocate their attention to problems and the outcomes of their allocation are publicly observable without cost. Funding: Financial support for this work was provided by the Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung [Grant 100018_150126] (“Relational event modes for bipartite networks with application to collaborative problem solving,” P.I. Alessandro Lomi) and by the Deutsche Forschungsgemeinschaft [Grant 321869138] (“Statistical analysis of time-stamped multi-actor events in social networks,” P.I. Jüergen Lerner). Supplemental Material: The supplemental video containing the dynamic visualization of the data is available at https://zenodo.org/record/7564503 and in the e-companion (available at https://doi.org/10.1287/orsc.2023.1674 ).
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
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

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
Liufgui应助Re采纳,获得20
刚刚
Nugget完成签到,获得积分10
刚刚
yar应助潇湘雪月采纳,获得10
1秒前
宇宇发布了新的文献求助10
1秒前
shufessm完成签到,获得积分0
3秒前
3秒前
7秒前
幸福大白发布了新的文献求助30
7秒前
8秒前
肿瘤柳叶刀完成签到,获得积分10
9秒前
10秒前
10秒前
xxddw发布了新的文献求助10
11秒前
13秒前
GS11完成签到,获得积分10
14秒前
邓紫依完成签到,获得积分10
15秒前
cdytjt发布了新的文献求助60
15秒前
ai zs发布了新的文献求助10
15秒前
搜集达人应助zyw采纳,获得10
16秒前
17秒前
攀攀完成签到,获得积分10
18秒前
18秒前
Aprilapple发布了新的文献求助10
19秒前
张雯思发布了新的文献求助10
19秒前
20秒前
越野蟹关注了科研通微信公众号
21秒前
空军完成签到 ,获得积分10
23秒前
23秒前
酷波er应助moji采纳,获得10
23秒前
25秒前
传奇3应助打我呀采纳,获得30
26秒前
26秒前
Aprilapple发布了新的文献求助10
29秒前
29秒前
30秒前
zyw发布了新的文献求助10
30秒前
雪落你看不见完成签到,获得积分10
32秒前
orixero应助不安的紫翠采纳,获得10
32秒前
科研通AI5应助幸福大白采纳,获得10
32秒前
陌陌发布了新的文献求助10
32秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989406
求助须知:如何正确求助?哪些是违规求助? 3531522
关于积分的说明 11254187
捐赠科研通 3270174
什么是DOI,文献DOI怎么找? 1804901
邀请新用户注册赠送积分活动 882105
科研通“疑难数据库(出版商)”最低求助积分说明 809174