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Knowledge Creation through Competitive Intelligence Practices
Research Guide

What is Knowledge Creation through Competitive Intelligence Practices?

Knowledge creation through competitive intelligence practices refers to the processes by which organizations convert competitive intelligence into new knowledge via SECI spirals, communities of practice, and innovation pipelines in intelligence teams.

This subtopic examines how competitive intelligence (CI) practices enable tacit-to-explicit knowledge conversion within firms. Key studies integrate CI with knowledge management architectures like data warehousing (Nemati et al., 2002, 402 citations). Over 10 provided papers span 1999-2023, linking CI to strategic decision-making and big data analytics.

Curated Papers

Key Challenges

Why It Matters

CI practices drive organizational learning by channeling intelligence into knowledge warehouses, supporting decision support systems (Nemati et al., 2002). They enhance board-level decisions amid big data volumes (Merendino et al., 2018) and foster ambidexterity through CEO cognitive flexibility (Kiss et al., 2020). In oil and gas, CI-big data interplay boosts knowledge management for performance (Sumbal et al., 2017; Shabbir & Gardezi, 2020). Strategic models combining CI and knowledge management guide enterprise planning (Shujahat et al., 2017).

Key Research Challenges

Tacit Knowledge Conversion

Converting tacit intelligence from CI teams into explicit forms remains difficult amid complex knowing paradoxes (Snowden, 2003). CI processes lack universal models for actionable knowledge spirals (Pellissier & Nenzhelele, 2013). Knowledge-based systems often conflict with organic KM practices (Hendriks & Vriens, 1999).

Big Data Integration Barriers

Firms struggle to link big data analytics from CI to knowledge creation due to infrastructure governance gaps (Bertello et al., 2020). Mediating KM practices are needed for analytics to impact performance (Shabbir & Gardezi, 2020). Exploratory studies highlight uncharted interrelationships in sectors like oil and gas (Sumbal et al., 2017).

Strategic Ambidexterity Gaps

Achieving exploration-exploitation balance via CI requires CEO cognitive flexibility and information search (Kiss et al., 2020). Incumbents face dynamic capability deficits in leveraging CI big data for marketing (Brewis et al., 2023). Visible knowledge mapping in professional firms aids but scales poorly (Criscuolo et al., 2007).

Essential Papers

Knowledge warehouse: an architectural integration of knowledge management, decision support, artificial intelligence and data warehousing

Hamid Nemati, David M. Steiger, Lakshmi Iyer et al. · 2002 · Decision Support Systems · 402 citations

Decision support systems (DSS) are becoming increasingly more critical to the daily operation of organizations. Data warehousing, an integral part of this, provides an infrastructure that enables b...

Big data, big decisions: The impact of big data on board level decision-making

Alessandro Merendino, Sally Dibb, Maureen Meadows et al. · 2018 · Journal of Business Research · 218 citations

Knowledge-based systems and knowledge management: Friends or foes?

Paul Hendriks, Dirk Vriens · 1999 · Information & Management · 181 citations

Complex Acts of Knowing: Paradox and Descriptive Self‐Awareness

Dave Snowden · 2003 · Bulletin of the American Society for Information Science and Technology · 180 citations

Editor' Note: This article has been extracted and condensed from one that first appeared in the Journal of Knowledge Management , v. 6, no2 (May 2002), p. 100-111. A copy of the original article al...

CEO cognitive flexibility, information search, and organizational ambidexterity

Andreea N. Kiss, Dirk Libaers, Pamela S. Barr et al. · 2020 · Strategic Management Journal · 148 citations

Abstract Research summary Although prior research highlights the organizational and cognitive challenges associated with achieving organizational ambidexterity, there has been comparatively less em...

Interrelationship between big data and knowledge management: an exploratory study in the oil and gas sector

Muhammad Saleem Sumbal, Eric Tsui, Eric W.K. See-To · 2017 · Journal of Knowledge Management · 125 citations

Purpose The purpose of this paper is to explore the relationship between big data and knowledge management (KM). Design/methodology/approach The study adopts a qualitative research methodology and ...

Application of big data analytics and organizational performance: the mediating role of knowledge management practices

Muhammad Qasim Shabbir, Syed Babar Waheed Gardezi · 2020 · Journal Of Big Data · 121 citations

Abstract Drawing from tenets of the resource-based theory, we propose and test a model that examines the relationship between the application of big data analytics (ABDA) and organizational perform...

Reading Guide

Foundational Papers

Start with Nemati et al. (2002) for knowledge warehouse architecture integrating CI and KM; Hendriks & Vriens (1999) for system-KM tensions; Snowden (2003) for tacit knowing paradoxes in intelligence practices.

Recent Advances

Study Kiss et al. (2020) for CEO cognition in CI ambidexterity; Shujahat et al. (2017) for strategic CI-KM models; Brewis et al. (2023) for big data dynamic capabilities.

Core Methods

Core techniques: CI process modeling (Pellissier & Nenzhelele, 2013), big data-KM mediation (Shabbir & Gardezi, 2020), knowledge visibility mapping (Criscuolo et al., 2007), and BDA governance (Bertello et al., 2020).

How PapersFlow Helps You Research Knowledge Creation through Competitive Intelligence Practices

Discover & Search

Research Agent uses searchPapers and exaSearch to find CI-KM integrations, revealing Nemati et al. (2002) as a hub via citationGraph. findSimilarPapers expands from Shujahat et al. (2017) to big data CI papers like Sumbal et al. (2017), mapping 250M+ OpenAlex papers for knowledge spirals.

Analyze & Verify

Analysis Agent employs readPaperContent on Pellissier & Nenzhelele (2013) to extract CI process models, then verifyResponse with CoVe checks SECI alignments against Snowden (2003). runPythonAnalysis with pandas networks citation patterns from 10 papers, GRADE-grading evidence strength for tacit conversion claims.

Synthesize & Write

Synthesis Agent detects gaps in CI-big data knowledge flows (e.g., post-2020), flagging contradictions between Hendriks & Vriens (1999) and modern BDA. Writing Agent uses latexEditText, latexSyncCitations for Shujahat et al. (2017)-integrated reports, latexCompile with exportMermaid for SECI spiral diagrams.

Use Cases

"Analyze citation networks in CI knowledge creation papers for SECI model influences."

Research Agent → citationGraph on Nemati et al. (2002) → Analysis Agent → runPythonAnalysis (NetworkX/pandas for centrality) → researcher gets CSV of key influencers and Gephi-ready graph.

"Draft LaTeX review synthesizing Shujahat et al. (2017) strategic CI model with big data cases."

Synthesis Agent → gap detection across 5 papers → Writing Agent → latexEditText + latexSyncCitations (10 papers) + latexCompile → researcher gets compiled PDF with auto-cited strategic model diagram.

"Find GitHub repos implementing knowledge warehouse from Nemati et al. (2002)."

Research Agent → paperExtractUrls on Nemati et al. (2002) → Code Discovery → paperFindGithubRepo + githubRepoInspect → researcher gets repo code summaries and DSS implementation forks.

Automated Workflows

Deep Research workflow conducts systematic review of 50+ CI-KM papers: searchPapers → citationGraph → GRADE reports on knowledge spirals. DeepScan's 7-steps verify big data-CI links (Sumbal et al., 2017) with CoVe checkpoints and Python centrality analysis. Theorizer generates CI process theory from Pellissier & Nenzhelele (2013) plus recent BDA papers.

Try Doxa for Knowledge Creation through Competitive Intelligence Practices Research

Frequently Asked Questions

What defines knowledge creation through competitive intelligence practices?

It involves converting CI data into organizational knowledge via SECI spirals and intelligence teams, linking tacit insights to explicit innovation pipelines (Shujahat et al., 2017).

What are core methods in this subtopic?

Methods include knowledge warehousing for DSS (Nemati et al., 2002), universal CI process models (Pellissier & Nenzhelele, 2013), and big data analytics mediated by KM practices (Shabbir & Gardezi, 2020).

What are key papers?

Foundational: Nemati et al. (2002, 402 citations), Hendriks & Vriens (1999, 181 citations), Snowden (2003, 180 citations). Recent: Kiss et al. (2020, 148 citations), Brewis et al. (2023, 90 citations).

What open problems exist?

Challenges include scaling tacit conversion in CI teams (Snowden, 2003), governing BDA infrastructure for knowledge flows (Bertello et al., 2020), and building dynamic capabilities for incumbent CI strategies (Brewis et al., 2023).