The effect of transactive memory systems on supply chain network collaboration

供应链 业务 事务性记忆 独创性 供应网络 供应链管理 知识管理 可靠性 过程管理 价值(数学) 利用 过程(计算) 产业组织 营销 计算机科学 定性研究 法学 社会学 功率(物理) 物理 机器学习 操作系统 量子力学 计算机安全 社会科学 政治学
作者
Kevin P. Scheibe,Prabhjot S. Mukandwal,Scott J. Grawe
出处
期刊:International Journal of Physical Distribution & Logistics Management [Emerald (MCB UP)]
卷期号:52 (9/10): 791-812 被引量:5
标识
DOI:10.1108/ijpdlm-07-2021-0288
摘要

Purpose This research is aimed at understanding how inter-organizational team members' ability to encode, interpret, retain and recall knowledge can lead to effective supply chain collaboration, resulting in improved firm performance. Using the lens of transactive memory systems (TMS), this research demonstrates the value of knowing who knows what (specialization), is it trustworthy (credibility) and how to retrieve it (coordination) on supply chain firm performance through network collaboration. Design/methodology/approach The authors used a multi-method approach that includes quantitative survey methodology and a qualitative methodology using semi-structured interviews. In total, 207 survey responses and six semi-structured interviews provided valuable insights into the use of TMS in supply chain relationships. Findings This study shows that TMS can enable firms to exploit potential benefits of collaboration on network optimization, thus improving the overall efficiency and process innovations. Practical implications To maintain the efficient use of a firm's assets while suppliers get added or removed from the network, this study’s findings suggest that managers should be more knowledgeable of supply chain partners carrying codified knowledge, which can contribute to superior firm performance. Recognizing that when two or more firms collaborate, there are multiple supply chains affected by each decision, it is important that managers carefully assign the specific role of each firm within the supply chain. Originality/value This research takes a new approach to network optimization by specifically considering how firms work together to share information about their changing networks to allow firms throughout the supply chain to gain greater levels of asset efficiency and process improvement.
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
刚刚
嘉悦发布了新的文献求助10
3秒前
LHW发布了新的文献求助10
3秒前
oxs发布了新的文献求助10
4秒前
6秒前
追寻清完成签到,获得积分10
6秒前
shuqi完成签到 ,获得积分10
6秒前
7秒前
SciGPT应助乔伊伊伊采纳,获得30
8秒前
9秒前
李付清完成签到,获得积分10
10秒前
高贵的雅山完成签到,获得积分10
11秒前
11秒前
乐乐应助人间自在仙采纳,获得10
13秒前
完美世界应助皮卡丘采纳,获得20
15秒前
共享精神应助行7采纳,获得10
15秒前
11发布了新的文献求助10
16秒前
浮游应助化学采纳,获得10
18秒前
lyx完成签到,获得积分10
18秒前
Casf完成签到,获得积分10
19秒前
斯文败类应助xiaoxiang采纳,获得10
20秒前
情怀应助yb采纳,获得10
21秒前
英俊的铭应助ZHT采纳,获得10
22秒前
火星上的宝马完成签到,获得积分10
22秒前
23秒前
23秒前
朱剑洪完成签到,获得积分10
25秒前
ttt完成签到,获得积分10
26秒前
26秒前
清风发布了新的文献求助10
27秒前
20240901完成签到,获得积分10
28秒前
30秒前
qiuxia发布了新的文献求助10
30秒前
xiaoxiang完成签到,获得积分10
31秒前
32秒前
li完成签到,获得积分10
32秒前
LLL完成签到,获得积分10
34秒前
vvcat发布了新的文献求助10
34秒前
太阳完成签到,获得积分10
36秒前
qiuxia完成签到,获得积分20
37秒前
高分求助中
Aerospace Standards Index - 2025 10000
(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
Treatise on Geochemistry (Third edition) 1600
Clinical Microbiology Procedures Handbook, Multi-Volume, 5th Edition 1000
List of 1,091 Public Pension Profiles by Region 981
On the application of advanced modeling tools to the SLB analysis in NuScale. Part I: TRACE/PARCS, TRACE/PANTHER and ATHLET/DYN3D 500
L-Arginine Encapsulated Mesoporous MCM-41 Nanoparticles: A Study on In Vitro Release as Well as Kinetics 500
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 纳米技术 计算机科学 内科学 化学工程 复合材料 物理化学 基因 遗传学 催化作用 冶金 量子力学 光电子学
热门帖子
关注 科研通微信公众号,转发送积分 5456751
求助须知:如何正确求助?哪些是违规求助? 4563362
关于积分的说明 14289575
捐赠科研通 4487973
什么是DOI,文献DOI怎么找? 2458113
邀请新用户注册赠送积分活动 1448473
关于科研通互助平台的介绍 1424128