Subtopic Deep Dive

Knowledge Value Chain Models
Research Guide

What is Knowledge Value Chain Models?

Knowledge Value Chain Models conceptualize the sequential transformation of knowledge resources into organizational value through structured processes of creation, sharing, storage, and application.

Ching Chyi Lee and Jie Yang (2000) introduced the foundational knowledge value chain model, comprising knowledge infrastructure and processes, with 444 citations. This framework links knowledge management practices to business outcomes. Related works extend it to DIKW hierarchies (Baškarada and Koronios, 2013, 128 citations) and value exchanges (Vuong, 2021, 444 citations).

Curated Papers

Key Challenges

Why It Matters

Knowledge value chain models enable quantification of intangible assets' contributions to firm performance, as modeled by Lee and Yang (2000). They inform strategic decisions in disaster risk reduction by systematizing knowledge flows (Weichselgartner and Pigeon, 2015). Baškarada and Koronios (2013) apply DIKW to assess information quality impacts on decision-making.

Key Research Challenges

Metrics for Knowledge Valuation

Quantifying intangible knowledge assets remains difficult due to lack of standardized metrics. Lee and Yang (2000) outline infrastructure needs but lack empirical valuation formulas. Vuong (2021) proposes semiconducting principles yet requires validation across sectors.

Integration with DIKW Hierarchy

Aligning value chain models with data-information-knowledge-wisdom progressions faces definitional ambiguities. Baškarada and Koronios (2013) explore quality dimensions empirically but highlight inconsistencies. Bernstein (2011) critiques the hierarchy's filtration assumptions.

Scalability in Dynamic Contexts

Adapting models to rapid changes like Industry 4.0 challenges static frameworks. Wang and Wang (2016) discuss CPS integration but note gaps in knowledge flow scalability. Amanatidou et al. (2012) emphasize horizon scanning for emerging issues.

Essential Papers

Knowledge value chain

Ching Chyi Lee, Jie Yang · 2000 · Journal of Management Development · 444 citations

Introduces the knowledge value chain model as a knowledge management (KM) framework. The model consists of knowledge infrastructure (knowledge worker recruitment, knowledge storage capacity, custom...

The semiconducting principle of monetary and environmental values exchange

Quan‐Hoang Vuong · 2021 · Economics and Business Letters · 444 citations

This short article represents the first attempt to define a new core cultural value that will enable engaging the business sector in humankind’s mission to heal nature. First, I start with defining...

The Role of Knowledge in Disaster Risk Reduction

Juergen Weichselgartner, Patrick Pigeon · 2015 · International Journal of Disaster Risk Science · 286 citations

Disaster risk reduction policy and practice require knowledge for informed decision making and coordinated action. Although the knowledge production and implementation processes are critical for di...

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...

On concepts and methods in horizon scanning: Lessons from initiating policy dialogues on emerging issues

Effie Amanatidou, M. Butter, Vicente Carabias-Hütter et al. · 2012 · Science and Public Policy · 153 citations

Future-oriented technology analysis methods can play a significant role in enabling early warning signal detection and pro-active policy action which will help to better prepare policy- and decisio...

EM-DAT: the Emergency Events Database

Damien Delforge, Valentin Wathelet, Regina Below et al. · 2023 · 135 citations

<title>Abstract</title> The Emergency Events Database (EM-DAT) compiles global disaster data resulting from both technological and natural hazards. It details the human and economic impacts from 19...

Data, Information, Knowledge, Wisdom (DIKW): A Semiotic Theoretical and Empirical Exploration of the Hierarchy and its Quality Dimension

Saša Baškarada, Andy Koronios · 2013 · AJIS. Australasian journal of information systems/AJIS. Australian journal of information systems/Australian journal of information systems · 128 citations

What exactly is the difference between data and information? What is the difference between data quality and information quality; is there any difference between the two? And, what are knowledge an...

Reading Guide

Foundational Papers

Start with Lee and Yang (2000) for the core model definition and infrastructure components; follow with Baškarada and Koronios (2013) to understand DIKW linkages and quality dimensions.

Recent Advances

Study Vuong (2021) for novel value exchange principles; Weichselgartner and Pigeon (2015) for practical applications in risk reduction.

Core Methods

Core techniques: Hierarchical process modeling (Lee and Yang, 2000), semiotic analysis of DIKW (Baškarada and Koronios, 2013), horizon scanning for foresight (Amanatidou et al., 2012).

How PapersFlow Helps You Research Knowledge Value Chain Models

Discover & Search

Research Agent uses searchPapers and citationGraph to map the 444-citation foundational paper by Lee and Yang (2000), revealing extensions like Vuong (2021). exaSearch uncovers niche applications in DIKW (Baškarada and Koronios, 2013); findSimilarPapers links to Weichselgartner and Pigeon (2015) for risk reduction contexts.

Analyze & Verify

Analysis Agent applies readPaperContent to extract infrastructure components from Lee and Yang (2000), then verifyResponse with CoVe checks claims against Baškarada and Koronios (2013). runPythonAnalysis computes citation networks via pandas; GRADE grading scores model robustness (e.g., empirical validation levels).

Synthesize & Write

Synthesis Agent detects gaps in valuation metrics across Lee and Yang (2000) and Vuong (2021), flagging contradictions in DIKW applications. Writing Agent uses latexEditText and latexSyncCitations to draft model critiques, latexCompile for reports, exportMermaid for value chain diagrams.

Use Cases

"Extract and plot citation trends for knowledge value chain models from 2000-2023."

Research Agent → searchPapers('knowledge value chain') → Analysis Agent → runPythonAnalysis(pandas/matplotlib on citation data) → CSV export of trends graph.

"Draft a LaTeX review comparing Lee-Yang model to DIKW hierarchy."

Research Agent → citationGraph → Synthesis Agent → gap detection → Writing Agent → latexEditText + latexSyncCitations + latexCompile → PDF review document.

"Find GitHub repos implementing knowledge value chain simulations."

Research Agent → paperExtractUrls (Wang 2016 CPS paper) → Code Discovery → paperFindGithubRepo → githubRepoInspect → Python code for IoT knowledge flow sims.

Automated Workflows

Deep Research workflow conducts systematic reviews of 50+ papers on value chains, chaining searchPapers → citationGraph → GRADE reports for Lee and Yang (2000) descendants. DeepScan applies 7-step analysis with CoVe checkpoints to verify Vuong (2021) principles against empirical data. Theorizer generates extensions of DIKW models (Baškarada and Koronios, 2013) into scalable frameworks.

Try Doxa for Knowledge Value Chain Models Research

Frequently Asked Questions

What defines a knowledge value chain model?

Ching Chyi Lee and Jie Yang (2000) define it as a KM framework with knowledge infrastructure (workers, storage, relationships, CKO) and processes transforming inputs to value.

What are core methods in knowledge value chain research?

Methods include hierarchical modeling (DIKW by Baškarada and Koronios, 2013), semiconducting value exchange (Vuong, 2021), and horizon scanning (Amanatidou et al., 2012).

What are key papers on this subtopic?

Foundational: Lee and Yang (2000, 444 citations); Baškarada and Koronios (2013, 128 citations). Recent: Vuong (2021, 444 citations); Weichselgartner and Pigeon (2015, 286 citations).

What open problems exist?

Challenges include empirical metrics for knowledge valuation (Lee and Yang, 2000), DIKW quality integration (Baškarada and Koronios, 2013), and scalability to dynamic environments (Wang and Wang, 2016).