Abstract

Addressing the dual challenges of lagging business opportunity identification and systemic risk early-warning failures in environments characterized by information overload and policy uncertainty, this study integrates dynamic capability theory, policy tool classification frameworks, and cognitive psychology to pioneer a three-dimensional dynamic coupling model for enterprise business intelligence mining. The model comprises: (1) a policy anchoring layer that quantifies capital flows and compliance costs using the function $C_{risk} = \alpha \cdot Fine_{max} + \beta \cdot CLV_{loss} + \gamma \cdot Repair_{cost}, R^2=0.82$; (2) a deep insight layer that integrates the Elaboration Likelihood Model (ELM) and Social Network Analysis (SNA) to develop a "three-stage penetration method" (misjudgment rate ≤12%); and (3) an action transformation layer that designs three major rules: policy grafting, demand translation, and intelligence puzzle. Through cross-case validation across medical, financial, and manufacturing sectors (N=3, 3-month cycle), the model significantly improved business opportunity response efficiency by 40.2% (SD=3.5%, p<0.01) and increased risk early-warning accuracy to 85.7%. The core theoretical contribution lies in establishing a vertical coupling mechanism that integrates macro-level policy deconstruction, meso-level demand insight, and micro-level decision-chain mapping. Its practical value resides in providing an actionable framework and methodological toolkit that enables enterprises to shift from passively responding to market changes to proactively anticipating strategic opportunities. Notably, the newly created "three-stage penetration method" and "business opportunity credibility scorecard" effectively reduce intelligence misjudgment risks. Based on pilot enterprise data, the study further proposes optimal resource allocation recommendations (policy guidance: 32.1% ± 2.4%, deep demand motivation analysis: 41.3% ± 3.1%, chain embedding: 18.5% ± 1.7%, intelligence weaving: 8.1% ± 0.9%) and an organizational safeguard mechanism through establishing a cross-functional "Intelligence War Room." Future research may explore integrating cutting-edge Generative AI technology for automated policy deconstruction and intelligence weaving, and expand cross-cultural validation of the model.

Keywords: Enterprise business intelligence mining; Policy anchoring; Decision chain mapping; Competitive intelligence; Dynamic capability

Author Biography: Yao Qing (1986–), male, from Sanmenxia, Henan, Senior Engineer. Research interests: Enterprise intelligent decision-making and informatization integration. Email: yaoqing.ha@chinatelecom.cn

Introduction

In the VUCA (Volatility, Uncertainty, Complexity, Ambiguity) era \cite{8}, enterprises face three interlocking challenges: accelerated policy iteration (e.g., 2024 AI regulatory updates) \cite{24}, implicit demand manifestation (with a divergence rate between expressed needs and true motivations ≥35%), and dynamic decision-chain restructuring (where informal power nodes exceed 40% of the total). Industry reports indicate that over 70% of enterprises \cite{11} miss strategic opportunities due to lagging business intelligence analysis \cite{11}. Traditional business intelligence mining methods suffer from three fundamental constraints: fragmented policy interpretation leading to strategic misjudgment and resource misallocation \cite{6,7}, superficial demand insight \cite{14} that overlooks deep decision-making motivations and causes solution deviation from core value \cite{14}, and static decision-chain cognition \cite{5} that significantly reduces resource deployment precision and conversion efficiency. Despite advances in competitive intelligence systems \cite{1} and multi-source information fusion technologies \cite{5}, existing research focuses on single-dimension optimization—policy tool effectiveness evaluation \cite{25}, multi-source information fusion, or static decision-chain mapping \cite{21}—while lacking dynamic coupling mechanism deconstruction of the "policy-demand-decision chain" nexus (Research Gap G1) and pathways for converting intelligence into competitive advantage (Gap G2).

To address these challenges, this study proposes and validates a three-dimensional enterprise business intelligence mining model integrating "policy anchoring, deep insight, and action rules," constructing a closed-loop analytical framework. Theoretically, it innovatively fuses policy tool classification \cite{3}, the Elaboration Likelihood Model (ELM) \cite{15}, and Social Network Analysis (SNA) \cite{21} to establish an interdisciplinary foundation. The research pioneers the "three-stage penetration method" demand analysis tool and a "business opportunity credibility scorecard" risk quantification mechanism.

1.1 Theoretical Evolution and Limitations of Business Intelligence Mining

Enterprise business intelligence mining theory is rooted in Competitive Intelligence Systems (CIS) research. Bao Changhuo and Xie Xinzhou \cite{1} proposed a four-module CIS architecture, but focused primarily on information collection with insufficient attention to intelligence value conversion. Wang Zhijin et al. \cite{5} constructed a multi-source information fusion model, yet exhibited limitations in designing dynamic coupling mechanisms between policy orientation and market demand, as well as in real-time decision-chain mapping \cite{2}. Industry analysis indicates that current business intelligence tools still face technical bottlenecks in processing unstructured data and dynamically mapping decision chains \cite{11}.

1.2 Theoretical Anchors of the Three-Dimensional Model

The model rests on three theoretical pillars: (1) The policy anchoring layer builds upon Rothwell & Zegveld's policy tool classification theory \cite{3}, categorizing policies into supply-oriented, environment-oriented, and demand-oriented types. This study innovatively focuses on policy fund flow analysis and quantitative modeling of compliance costs \cite{6,10}. (2) The deep insight layer applies Petty & Cacioppo's (1986) Elaboration Likelihood Model (ELM) \cite{15} to parse the cognitive path from "surface demands → business pain points → decision motivations," innovatively combining Social Network Analysis (SNA) \cite{21} to identify informal decision-influence nodes (e.g., retired expert consultants). (3) The action transformation layer draws upon the dynamic capabilities framework's core structure \cite{18}—"sensing-seizing-transforming" \cite{20}—to design three action rules: policy grafting, demand translation, and intelligence puzzle, aiming to achieve substantive transformation from information insight to business action.

[FIGURE:1]: Three-dimensional coupling mechanism diagram

1.3 Research Positioning and Core Innovations

The model's core innovations include: (1) Three-dimensional vertical coupling mechanism: First to propose and validate a vertical coupling mechanism integrating macro policy deconstruction, meso demand insight, and micro decision-chain mapping. (2) Dynamic closed-loop execution logic: Constructs a four-step iterative process of "policy guidance → deep demand motivation analysis → chain embedding → intelligence weaving" to enable continuous intelligence iteration and action optimization. (3) Risk quantification tool innovation: Pioneers the "business opportunity credibility scorecard" that quantifies intelligence reliability through multi-dimensional weighted scoring, significantly reducing misjudgment risks across three intelligence traps—policy arbitrage, false demand signals, and virtual decision-chain nodes. (4) Supporting methodological tools: Develops the "three-stage penetration method" structured interview tool for parsing demand cognitive hierarchies (see Section 2.3.1).

1.4 Theoretical Coupling Mechanisms

The theoretical integration operates through: (1) Dynamic capability theory providing the core framework—sensing layer maps to policy anchoring, seizing layer to deep insight, and transforming layer to action rules. (2) Policy tool classification theory supporting policy fund analysis. (3) Cognitive psychology (ELM) empowering demand cognition deepening.

2.1 Overall Framework Design

The model comprises three hierarchical layers: (1) Bottom layer—Policy Anchoring: Core tasks include policy fund flow analysis, compliance cost quantification modeling, and policy tool matrix application. (2) Middle layer—Deep Insight: Core methods encompass three-stage penetration listening (demand parsing), decision-chain dynamic mapping (SNA), and intelligence ecosystem cross-validation (multi-source fusion). (3) Top layer—Action Rules: Transformation strategies include policy grafting technique, demand translator, and intelligence puzzle technique.

2.2.1 Policy Fund Flow Analysis

Based on policy tool theory, the model parses how three policy types drive business opportunities: Supply-oriented policies (e.g., special funds) directly create rigid demand. A typical case: national smart healthcare special fund investment directly drives rigid demand for electronic medical record system upgrades in medical institutions. Environment-oriented policies (e.g., tax incentives) indirectly guide social capital flows. A typical case: new energy equipment VAT exemption policies significantly increase social capital investment in the sector. Demand-oriented policies (e.g., government procurement) launch emerging markets at scale. A typical case: large-scale smart city procurement projects effectively drive IoT technology adoption and application.

2023 Policy Cases: Demand-oriented: The Interim Measures for Generative AI Service Management drove a wave of government intelligent customer service procurement (a provincial fiscal allocation of ¥230 million). Environment-oriented: New ESG disclosure rules for the STAR Market (Q3 saw 12 new listed companies with compliance consulting needs).

2.2.2 Compliance Cost Quantification Model

Transforming abstract regulatory clauses into quantifiable and predictable enterprise risk costs is key to precise risk early-warning. Typical cases include: Data Security Law clauses on fines for failing to establish sound data security management systems (case: an e-commerce platform fined ¥2 million); Personal Information Protection Law clauses on compensation risks for illegal personal data processing (case: a company compensated ¥370,000); and Cybersecurity Law clauses on fines for failing to deploy necessary security measures (case: a financial institution penalized).

This study innovatively integrates the non-market strategy cost quantification framework \cite{7} with industry regression analysis to construct a dynamic compliance risk cost model:

$$C_{risk} = \alpha \cdot Fine_{max} + \beta \cdot CLV_{loss} + \gamma \cdot Repair_{cost}, \quad R^2=0.82$$

Where:
- $C_{risk}$ = total compliance risk cost
- $Fine_{max}$ = maximum expected fine, calculated as $Fine_{max} = \max{\text{statutory fixed penalty}, \text{annual revenue} \times \eta\%}$ (with $\eta$ ranging 2%-5% by industry)
- $CLV_{loss}$ = regression-predicted customer churn loss based on Customer Lifetime Value models
- $Repair_{cost}$ = mean predicted舆情 monitoring and PR investment based on historical data

Weight coefficients $(\alpha,\beta,\gamma)$ are calibrated through panel data fixed-effects regression (manufacturing sample N=217), yielding adjusted $R^2=0.82$ and F-test $p<0.01$. This method significantly outperforms traditional linear models \cite{24}. The model innovatively transforms abstract regulations into quantifiable risk costs, providing a basis for precise early-warning and distinguishing itself from policy tool effectiveness studies \cite{6}.

2.3.1 Three-Stage Penetration Method

Based on the ELM model, this study pioneers the "three-stage penetration method" structured interview tool to progressively excavate authentic customer needs [TABLE:1]. The method traces the cognitive path from surface demands to business pain points to decision motivations. For example, in military equipment procurement: surface demand "improve detection accuracy" → business pain point "unstable delivery cycles for imported equipment affect production" → decision motivation "respond to national supply chain security policy requirements and achieve domestic substitution of critical detection equipment."

[TABLE:2] compares the three-stage penetration method with traditional approaches like KANO, showing superior depth in motivation excavation and policy relevance. Industry data from Gartner (2023) \cite{11} indicates a misjudgment rate of ≤12% for this method, compared to significantly higher rates for conventional techniques.

2.3.2 Decision-Chain Dynamic Mapping

Using Social Network Analysis (SNA) \cite{21}, the model constructs a decision-chain power matrix [TABLE:3] to identify informal influence nodes (e.g., retired expert advisors) that wield high non-formal influence without official positions. Key steps include: (1) mapping informal decision-influence node diagrams to identify critical nodes; (2) training machine learning models (e.g., Random Forest) on historical procurement data to identify key veto factors and their weights (e.g., "system compatibility" factor weight = 0.73); (3) tracking critical veto factors to pinpoint each role's sensitivities (e.g., IT department heads prioritize new system compatibility with legacy platforms); and (4) establishing a decision-chain mapping database with dynamic monitoring and updating mechanisms.

2.4 Intelligence Ecosystem Cross-Validation

The model constructs a multi-source intelligence fusion analysis system to reduce single-source bias risks [TABLE:4]. The system integrates: (1) network舆情 analysis using web crawlers + NLP sentiment analysis + topic modeling to detect industry trends (e.g., a technology term frequency surge of 300%); (2) informal channels through deep interviews + industry relationship networks to track competitor abnormal movements (e.g., rival secret visits to customer production bases); and (3) customer interaction information via CRM system analysis + semantic analysis to assess demand urgency (e.g., customers repeatedly asking about delivery cycles).

2.5 Three-Dimensional Coupling Path

The coupling path flows sequentially: Policy Guidance → Deep Demand Motivation Analysis → Chain Embedding → Intelligence Weaving.

3.1 Policy Grafting Technique

This technique translates macro-policy clauses into specific business scenario language and customer value propositions through: (1) Clause mapping: converting abstract regulations into customer pain points (e.g., environmental regulation "requires real-time carbon emission monitoring" → pain point "reliance on manual carbon reporting causes delays and distortion, directly impacting carbon quota trading profits and increasing compliance costs"). (2) Quantified presentation: clearly calculating and displaying potential violation costs (e.g., 5%-10% of annual revenue) or policy dividend opportunities (e.g., special bond fund scale and investment direction forecasts).

Manufacturing case: "Existing production equipment lacks IoT data collection modules, failing to meet latest 'green factory' certification standards, resulting in missed government special capacity subsidies of up to 10% of annual output value."

Finance case: "Loan classification not strictly following regulatory 'green' standards leads to 30% higher financing costs for that category."

3.2 Demand Translator

This component decodes implicit needs identified through the three-stage penetration method and translates them into solution core functions: (1) Efficiency improvement needs (e.g., "internal approval process takes too long") → solution: "deploy mobile-enabled e-signature systems and automated workflow engines." (2) Compliance assurance needs (e.g., "frequent compliance check issues") → solution: "integrate real-time automated compliance monitoring rule engines and risk early-warning platforms." (3) Cost optimization needs (e.g., "equipment maintenance costs remain high") → solution: "introduce AI algorithm-based predictive maintenance modules."

Government case: "Long queues at citizen service halls" → solution: "develop integrated platform combining online appointment APP and offline intelligent self-service terminals."

Retail case: "Store inventory management chaos causing stockouts or overstock" → solution: "deploy intelligent ERP inventory management modules with AI sales forecasting algorithms \cite{16} for automated replenishment optimization."

3.3 Intelligence Puzzle

Based on information value density principles \cite{17} and pilot experience, the model designs multi-source intelligence fusion rules to maximize value: (1) High-value public sources (~60% weight): government bidding databases, patent databases, enterprise environmental assessment reports, listed company annual reports. (2) Medium-value semi-public sources (~30% weight): industry meeting minutes, supply chain logistics data, professional forum discussions, industry association reports. (3) Critical hidden clues (~10% weight): informal feedback from key decision-chain nodes, unpublished intelligence from deep interviews.

Application example: Cross-analyzing equipment demand scale disclosed in a target enterprise's new plant environmental assessment report with abnormal logistics data from specific equipment suppliers (e.g., frequent shipments to the region) enables precise prediction of customer procurement windows and potential demand scale.

3.4 Competitive Intelligence Verification Sandbox

The model designs a "red-blue confrontation推演" mechanism as a strategy pre-validation tool, simulating competitor reactions and market chain effects after key business actions (e.g., new product launches, price adjustments, major service upgrades) to verify strategy feasibility and robustness.

推演 example: Simulating when a major competitor announces a 5% price reduction across all products to evaluate the impact of one's own "core product + value-added service bundle" strategy on customer retention, market share, and overall revenue.

4.1.1 Case Selection Criteria

Using typical case sampling, selection criteria included: (1) industry policy sensitivity (medical > finance > manufacturing), (2) decision-chain complexity (finance ≥5 nodes, manufacturing ≥4 nodes), and (3) enterprise scale (revenue > ¥1 billion).

4.1.2 Data Triangulation Method

Cross-validation sources comprised: (1) enterprise risk control system logs (compliance early-warning records), (2) decision-chain role interview recordings (de-identified text analysis), and (3) business opportunity response ticket timestamps (exported from ERP systems). To validate model effectiveness, the study selected one representative enterprise from each sector (de-identified as Companies A, B, C) for a 3-month pilot. Innovatively adding "decision-chain dynamic mapping completeness" as a core validation metric compared to recent industrial chain risk early-warning research \cite{4}, primary evaluation indicators included: (1) business opportunity response speed (average days from initial intelligence identification to preliminary solution output), (2) risk early-warning accuracy (ratio of successfully warned major compliance risks to actual occurrences), and (3) decision-chain mapping completeness (ratio of identified key decision roles (including informal influence nodes) to verified actual participants).

[TABLE:5] summarizes differentiated application priorities across industries: medical sector emphasizes policy grafting > intelligence puzzle focusing on medical insurance payment reform and clinical path compliance; finance sector prioritizes demand translation > decision-chain mapping around穿透式regulatory requirements and risk control chains; manufacturing sector emphasizes intelligence puzzle > policy grafting for green supply chain policies and supplier ESG ratings.

4.2 Industry Differentiation Application

The model requires dimensional emphasis adjustments by industry [TABLE:6]. Results showed: Medical sector: response time improved from 15→9 days (-40%), risk accuracy 82%→89%, decision-chain completeness 70%→92%. Finance sector: 20→11 days (-45%), 78%→87%, 65%→90%. Manufacturing sector: 18→10 days (-44%), 80%→86%, 72%→94%. Data sourced from pilot enterprise internal systems (June-September 2023), with $p<0.05$, $p<0.01$, $p<0.001$ (paired samples t-test).

4.3 Typical Case (Automotive Manufacturing)

Policy guidance: Continuous tracking of national hydrogen energy industrial support policies predicted an extension of hydrogen fuel cell vehicle subsidies. Deep demand motivation analysis: Using the three-stage penetration method with a large logistics enterprise, the core concern was identified not as vehicle purchase price but Total Cost of Ownership (TCO). Chain embedding & intelligence weaving: Dynamically mapping the logistics enterprise's procurement decision chain identified the technical director and operations director as key veto-holders focused on TCO data validation. Providing trial vehicles and monitoring actual fuel consumption and maintenance data, cross-validated with public industry operating cost reports, enabled a customized TROI (Total Return on Investment) solution. Paired samples t-test confirmed significant customer operating cost reduction ($t=4.32, df=8, p<0.01$) and market share increase of 2.5 percentage points (95% CI: 1.3–3.7).

4.4.1 Three Types of Intelligence Traps

The model identifies: (1) Policy arbitrage risk: local incentive policies potentially conflicting with national regulations or industrial guidance catalogs (case: local subsidies encouraging capacity expansion later classified as restricted by new national rules). (2) False demand signals: customer needs stemming from temporary budget arrangements or non-core strategic projects lacking sustainability and strategic value. (3) Virtual decision-chain nodes: over-focusing on high-influence but low actual procurement authority roles (case: pre-retirement senior leaders with only recommendation rights).

4.4.2 Bias Correction Mechanism: Business Opportunity Credibility Scorecard

To overcome traditional binary intelligence value judgments \cite{1}, this study pioneers a "business opportunity credibility scorecard" risk quantification tool using three-dimensional weighted scoring (weights determined through expert interviews, historical case analysis, and pilot data):

• Policy stability (30% weight): assesses policy continuity, implementation clarity, and local execution certainty.
• Decision-chain validation (40% weight): confirms key decision-maker identities, core demands, actual influence, and veto factors through multi-source cross-validation.
• Funding availability (30% weight): evaluates customer project budget approval and implementation status, or reliability of policy-specific fund disbursement progress and scale.

An empirical threshold (e.g., 70 points) is set; opportunities scoring below require multi-round verification or strategic resource deferral.

5 Model Value and Execution Logic

The model's core value lies in systematically transforming massive, abstract business intelligence into executable sources of competitive advantage.

5.1 Closed-Loop Execution Logic

The model's vitality stems from its four-step dynamic cycle: Policy Guidance → Deep Demand Motivation Analysis → Chain Embedding → Intelligence Weaving. (1) Policy guidance systematically deconstructs the macro policy environment to identify policy dividends and compliance minefields, providing navigational basis for strategic direction and resource prioritization. (2) Deep demand motivation analysis uses deep insight methods (particularly the three-stage penetration method) to lock in target customer groups and penetrate surface-level expressions to identify core value demands and authentic decision motivations. (3) Chain embedding dynamically maps and continuously monitors target customers' project decision-chain structures, key roles, power distributions, and veto factors to enable precise resource deployment. (4) Intelligence weaving continuously collects multi-source intelligence during and after action execution for cross-validation, dynamic correction, and knowledge accumulation, optimizing subsequent strategies and initiating new iteration cycles.

5.2 Closed-Loop Value Manifestation

(1) Accelerated decision-making and enhanced effectiveness: The automotive manufacturing case (Section 4.3) quantitatively demonstrates how the closed-loop shortens opportunity conversion cycles and improves success rates. (2) Dynamic moat construction: Each cycle accumulates structured knowledge assets (decision-chain mapping databases, policy interpretation case libraries, segment-specific demand motivation models) that continuously feed back into model optimization. This systematic cognition and rapid response capability, formed through practice-based iteration, constitutes a dynamic barrier difficult for competitors to replicate.

5.3 Execution Essentials

(1) Optimal resource allocation: Based on ROI calculations of intelligence analyst work hours from pilot enterprises (N=152 person-days, $p<0.05$), recommended resource allocation is: policy guidance (32.1% ± 2.4%) < deep demand motivation analysis (41.3% ± 3.1%) > chain embedding (18.5% ± 1.7%) > intelligence weaving (8.1% ± 0.9%). Enterprises can dynamically adjust based on industry characteristics and development stages. (2) Organizational safeguard mechanism: Drawing on business intelligence best practices \cite{13}, establish a permanent cross-functional "Intelligence War Room" integrating strategic planning, market insight, sales, R&D, and compliance. This creates a normalized collaboration mechanism with fixed weekly or biweekly iteration agendas and output standards \cite{9}, transforming business intelligence from a "back-office support" role into a "core engine" driving strategic decision-making and business growth.

War Room Biweekly Sprint Mechanism:
- Input: Policy update summaries, decision-chain anomaly alerts, scorecard <70-point opportunities
- Process: Demand translation workshop → Red-blue confrontation推演 → Resource deployment decisions
- Output: Intelligence Action List containing priority (P0/P1/P2), TROI forecast (95% CI), and primary responsible person (department-name format, e.g., "Market Insight-Zhang Ming").

6.1 Theoretical Contributions

(1) Deepening and operationalizing dynamic capability theory: This study first systematically applies Teece's abstract dynamic capabilities framework—"Sensing-Seizing-Transforming" \cite{18,19,20}—to enterprise business intelligence mining, achieving concrete operationalization: sensing maps to policy anchoring, seizing to deep insight, and transforming to action rules. Empirically validating the complete transformation path enriches dynamic capability applications in intelligence-driven strategy.

(2) Bridging multi-disciplinary theoretical gaps: The study innovatively integrates macro-level Rothwell & Zegveld policy tool classification \cite{3} for fund flow deconstruction, meso/micro-level ELM \cite{15} for three-stage penetration analysis, SNA \cite{21} for decision-chain mapping, and micro/meso-level competitive intelligence foundations \cite{1} for value conversion. This vertical coupling mechanism effectively bridges traditional analytical-level and focus gaps, providing a unified theoretical framework.

(3) Original methodological tools: The study creates: (a) "Three-stage penetration method": ELM-based structured demand parsing tool reducing misjudgment rate to ≤12%; (b) "Business opportunity credibility scorecard": multi-dimensional weighted risk assessment tool; and (c) "Intelligence War Room" closed-loop mechanism integrating cross-functional collaboration. These tools constitute core competitive components.

6.2 Practical Significance

(1) Resource optimization guide: Provides actionable intelligence activity allocation recommendations based on empirical data. (2) Efficient organizational model: Proposes the cross-functional Intelligence War Room with weekly closed-loop iteration \cite{9}, offering a deployable solution to the long-standing intelligence-business disconnect problem.

6.3.1 Research Limitations

(1) Unstructured intelligence dependency: The model remains heavily reliant on analysts' ability to process unstructured information, posing subjectivity risks and efficiency bottlenecks. (2) Insufficient cross-cultural validation: Current validation focuses on domestic industries; the model's applicability across cultural contexts (e.g., Belt and Road countries' business-government relations, decision customs, communication patterns) requires further testing.

6.3.2 Future Outlook

(1) Technology integration: Explore fusing Generative AI \cite{12} and deep learning models \cite{27} for automated policy semantic deconstruction, informal intelligence extraction, and basic intelligence weaving to enhance efficiency and objectivity. (2) Tool commercialization: Develop a SaaS platform to serve enterprises of different scales and types, lowering application barriers. (3) Cross-cultural expansion: Conduct pilot studies in diverse cultural contexts to validate and optimize the model for global applicability.

Conclusion

This study successfully constructs and validates a three-dimensional enterprise business intelligence mining model integrating "policy anchoring-deep insight-action rules." Core innovations include: (1) a pioneering vertical coupling framework bridging macro-meso-micro analysis; (2) a dynamic closed-loop execution logic of "policy guidance → deep demand motivation analysis → chain embedding → intelligence weaving"; and (3) original methodological tools including the three-stage penetration method and business opportunity credibility scorecard. Multi-industry case evidence demonstrates significant improvements in opportunity response efficiency (average 40.2% increase, SD=3.5%) and risk early-warning accuracy (85.7%). The study provides actionable resource allocation ratios and an efficient Intelligence War Room organizational mechanism, offering a systematic framework and practical toolkit for enterprises to transform from passive market adaptation to proactive strategic opportunity shaping. Future research will focus on integrating frontier Generative AI technology, accelerating SaaS tool development, and expanding scaled application validation across broader industries and cultural contexts to enhance theoretical value and practical impact.

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① VUCA: Volatility, Uncertainty, Complexity, Ambiguity
② ELM: Elaboration Likelihood Model
③ SNA: Social Network Analysis
④ TCO: Total Cost of Ownership
⑤ TROI: Total Return on Investment
⑥ ESG: Environmental, Social, and Governance