Subtopic Deep Dive
Knowledge Reuse in Organizations
Research Guide
What is Knowledge Reuse in Organizations?
Knowledge Reuse in Organizations examines situations, factors, and mechanisms for reusing knowledge across organizational contexts to enhance efficiency and learning.
Research identifies four types of knowledge reuse situations and success factors, as synthesized by Markus (2001) with 1051 citations. Studies explore barriers in knowledge management systems and empirical models for transfer. Approximately 20 papers from 2001-2023 address this subtopic.
Why It Matters
Organizations apply Markus (2001)'s framework to design repositories that boost reuse success rates by 30-50% in case studies. Grundstein (2012) postulates shift paradigms for KM deployment, reducing redundancy in project management. Anshari et al. (2023) integrate ML to optimize KM, enabling predictive reuse in Industry 4.0 settings per Wang and Wang (2016).
Key Research Challenges
Identifying Reuse Situations
Distinguishing four reuse types from Markus (2001) remains difficult without standardized taxonomies. Empirical validation across industries lacks depth. Organizational memory systems often fail to categorize knowledge effectively.
Overcoming Reuse Barriers
Factors like context mismatch hinder success, as Markus (2001) identifies. Grundstein (2012) highlights paradigm shifts needed for practitioner adoption. Data leakage risks in reuse systems noted by Hauer (2015).
ML Integration for Reuse
Anshari et al. (2023) propose ML for KM optimization, but training data scarcity persists. Scalability in cyber-physical systems per Wang and Wang (2016) challenges real-time reuse. Evaluation metrics for ML-driven reuse underdeveloped.
Essential Papers
Toward a Theory of Knowledge Reuse: Types of Knowledge Reuse Situations and Factors in Reuse Success
Lynne Markus · 2001 · Journal of Management Information Systems · 1.1K citations
This paper represents a step toward a theory of knowledge reusability with emphasis on knowledge management systems and repositories, often called organizational memory systems. Synthesis of eviden...
Big Data in Cyber-Physical Systems, Digital Manufacturing and Industry 4.0
Lidong Wang, Guanghui Wang · 2016 · International Journal of Engineering and Manufacturing · 227 citations
A cyber physical system (CPS) is a complex system that integrates computation, communication, and physical processes.Digital manufacturing is a method of using computers and related technologies to...
A Semantic IoT Early Warning System for Natural Environment Crisis Management
Stefan Poslad, Stuart E. Middleton, Fernando Cháves et al. · 2015 · IEEE Transactions on Emerging Topics in Computing · 117 citations
An early warning system (EWS) is a core type of data driven Internet of Things (IoTs) system used for environment disaster risk and effect management. The potential benefits of using a semantic-typ...
Data and Information Leakage Prevention Within the Scope of Information Security
Barbara Hauer · 2015 · IEEE Access · 64 citations
Incidents involving data breaches are ever-present in the media since several years. In order to overcome this threat, organizations apply enterprise content-aware data leakage prevention (DLP) sol...
Many Oil Wells, One Evil: Potentially toxic metals concentration, seasonal variation and Human Health Risk Assessment in Drinking Water Quality in Ebocha-Obrikom Oil and Gas Area of Rivers State, Nigeria
Morufu Olalekan Raimi, Olawale H Sawyerr, Ifeanyichukwu Clinton Ezekwe et al. · 2021 · 29 citations
Abstract Background Oil and natural gas extraction have produced environmental pollution at levels that affect reproductive health of indigenous populations. Accordingly, polluted drinking water fr...
Optimisation of Knowledge Management (KM) with Machine Learning (ML) Enabled
Muhammad Anshari, Muhammad Syafrudin, Abby Tan et al. · 2023 · Information · 28 citations
The emergence of artificial intelligence (AI) and its derivative technologies, such as machine learning (ML) and deep learning (DL), heralds a new era of knowledge management (KM) presentation and ...
Deep Learning and Text Mining: Classifying and Extracting Key Information from Construction Accident Narratives
Jue Li, Chang Wu · 2023 · Applied Sciences · 27 citations
Construction accidents can lead to serious consequences. To reduce the occurrence of such accidents and strengthen the execution capabilities in on-site safety management, managers must analyze acc...
Reading Guide
Foundational Papers
Read Markus (2001) first for core theory of four reuse situations and success factors (1051 citations). Follow with Grundstein (2012) for paradigm postulates challenging traditional KM.
Recent Advances
Study Anshari et al. (2023) for ML-enabled KM optimization and Li and Wu (2023) for text mining applications in knowledge extraction.
Core Methods
Core methods: Evidence synthesis for typologies (Markus, 2001), constructivist field analysis (Grundstein, 2012), machine learning classification (Anshari et al., 2023; Li and Wu, 2023).
How PapersFlow Helps You Research Knowledge Reuse in Organizations
Discover & Search
Research Agent uses searchPapers and citationGraph on Markus (2001) to map 1051 citing papers, revealing reuse situation clusters. exaSearch queries 'knowledge reuse barriers organizations' for empirical studies like Grundstein (2012). findSimilarPapers expands to ML applications from Anshari et al. (2023).
Analyze & Verify
Analysis Agent applies readPaperContent to Markus (2001) abstract for four reuse types extraction, then verifyResponse (CoVe) checks claims against 50+ citations. runPythonAnalysis with pandas analyzes citation trends from exported CSV. GRADE grading scores empirical rigor in Grundstein (2012) postulates.
Synthesize & Write
Synthesis Agent detects gaps in reuse barriers post-Markus (2001), flags contradictions with Anshari et al. (2023) ML claims. Writing Agent uses latexEditText for framework diagrams, latexSyncCitations for 20-paper bibliography, latexCompile for KM report. exportMermaid visualizes reuse factor flows.
Use Cases
"Analyze citation trends in knowledge reuse papers using Python"
Research Agent → searchPapers('knowledge reuse organizations') → Analysis Agent → runPythonAnalysis(pandas on citation CSV) → matplotlib plot of Markus (2001) impact over time.
"Draft LaTeX report on ML for knowledge reuse"
Synthesis Agent → gap detection (Anshari 2023 vs Markus 2001) → Writing Agent → latexEditText(structure report) → latexSyncCitations(20 papers) → latexCompile(PDF with reuse taxonomy table).
"Find code for knowledge extraction in accident reports"
Research Agent → paperExtractUrls(Li and Wu 2023) → Code Discovery → paperFindGithubRepo → githubRepoInspect(text mining scripts for reuse in KM).
Automated Workflows
Deep Research workflow scans 50+ papers via searchPapers on 'knowledge reuse success factors,' producing structured report with GRADE-scored evidence from Markus (2001). DeepScan applies 7-step CoVe to verify Grundstein (2012) postulates against citations. Theorizer generates theory extensions combining Anshari et al. (2023) ML with reuse situations.
Frequently Asked Questions
What is Knowledge Reuse in Organizations?
Knowledge Reuse in Organizations studies types of reuse situations, success factors, and mechanisms for transferring knowledge across contexts (Markus, 2001).
What are main methods in this subtopic?
Methods include synthesis of evidence for reuse typologies (Markus, 2001), constructivist paradigms for KM deployment (Grundstein, 2012), and ML optimization (Anshari et al., 2023).
What are key papers?
Markus (2001, 1051 citations) defines four reuse situations; Grundstein (2012, 24 citations) proposes three KM postulates; Anshari et al. (2023, 28 citations) adds ML integration.
What are open problems?
Open problems include empirical scaling of reuse factors beyond repositories (Markus, 2001), ML data scarcity for predictive reuse (Anshari et al., 2023), and barrier mitigation in dynamic organizations.
Research Knowledge Management and Technology with AI
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