Research Proposal v3

<!-- markdown --> # Research Proposal v3 **TOPIC:** Organizational Agility Accelerating Product Innovation with AI Empowerment: A Case Study of Company Y ## 1. Executive Summary (0.5-1 page) This research proposal investigates how Artificial Intelligence (AI) empowerment enhances organizational agility to accelerate product innovation in Chinese start-ups, using Company Y as a comprehensive case study. The study employs a primarily qualitative research approach with minimal quantitative indicators for reference, focusing on understanding the mechanisms and experiences of AI-driven organizational transformation through a dual-cycle action research methodology. Company Y, an IT start-up based in Suzhou, China, represents a typical case of Chinese start-ups struggling with innovation acceleration due to limited AI integration, despite operating in a rapidly digitalizing market environment. The company faces critical challenges including absence of AI integration in development processes, prolonged development cycles, and competitive disadvantage against AI-empowered competitors. This study employs a dual-cycle action research methodology following the Plan-Act-Observe-Reflect paradigm. Cycle 1 focuses on organizational diagnosis to systematically identify and analyze Company Y's challenges, barriers, and readiness for AI empowerment. Cycle 2 implements comprehensive AI tool integration (Tongyi Lingma, Zhipu AI, Cursor, Jimeng AI, and company self-developed tools) and evaluates their impact on organizational transformation. The research emphasizes qualitative analysis through in-depth interviews, participant observations, and thematic analysis to understand stakeholder experiences and organizational transformation processes. Expected outcomes include deep qualitative insights into AI empowerment's impact on organizational agility dimensions and product innovation acceleration. The research aims to contribute two Scopus-indexed journal publications providing theoretical insights and practical frameworks for AI-driven organizational transformation in start-up contexts. ### a. Keywords AI Empowerment, Organizational Agility, Product Innovation, Chinese Start-ups, Action Research ## 2. Research Background The contemporary business landscape is characterized by rapid technological advancement, with Artificial Intelligence emerging as a transformative force that fundamentally alters organizational capabilities and innovation processes. In China's dynamic start-up ecosystem, AI empowerment has become a critical differentiator, enabling organizations to achieve unprecedented levels of agility and innovation acceleration. China's AI ecosystem presents unique characteristics that distinguish it from Western contexts. Major corporations like Alibaba (Tongyi Lingma), Tencent, and Baidu (Zhipu AI) have created comprehensive AI infrastructure supporting start-up innovation. Additionally, emerging AI platforms like Jimeng AI and the development of proprietary AI tools by start-ups themselves represent the democratization of AI capabilities. The "New Generation Artificial Intelligence Development Plan" positions China to become a global AI leader by 2030, creating favorable conditions for AI-empowered organizational transformation. Despite these advantages, many Chinese start-ups struggle to effectively integrate AI technologies into their operational processes and innovation workflows. Company Y exemplifies this challenge, operating in a competitive market while lacking systematic AI integration, resulting in prolonged development cycles and reduced competitive positioning. Understanding the root causes of these challenges and implementing targeted AI empowerment solutions requires a systematic action research approach that combines diagnostic analysis with intervention implementation. Current research on AI and organizational agility remains fragmented, with limited qualitative understanding of how specific AI tools and integration strategies drive organizational transformation. This research addresses this gap by providing comprehensive qualitative analysis of AI empowerment mechanisms and their impacts on organizational transformation through deep ethnographic study using a dual-cycle action research framework. ## 3. Problem Statement Chinese start-ups face significant challenges in leveraging AI technologies to enhance organizational agility and accelerate product innovation, despite operating within supportive policy environments and advanced digital infrastructure. The absence of systematic AI integration creates competitive disadvantages and limits innovation potential, but the underlying causes and effective intervention strategies remain poorly understood. Company Y specifically faces interconnected challenges that require systematic diagnosis and targeted intervention: **Diagnostic Challenges (Cycle 1 Focus)**: 1. **Unclear Problem Root Causes**: While symptoms of poor innovation performance are evident, the underlying organizational, cultural, and technical barriers to AI adoption require systematic investigation. 2. **Unassessed AI Readiness**: The company's current capabilities, infrastructure readiness, and cultural preparedness for AI integration have not been systematically evaluated. 3. **Undefined Stakeholder Perspectives**: Employee attitudes, concerns, and expectations regarding AI empowerment remain unexplored, creating potential resistance to change. **Implementation Challenges (Cycle 2 Focus)**: 4. **Absence of Comprehensive AI Integration**: The company lacks systematic integration of diverse AI tools including commercial platforms (Tongyi Lingma, Zhipu AI, Cursor, Jimeng AI) and self-developed AI capabilities. 5. **Limited AI-Enhanced Development Capabilities**: Traditional development processes lack AI augmentation for routine task automation, data analysis, and development acceleration. 6. **Insufficient AI-Driven Organizational Learning**: The company fails to leverage AI technologies for continuous organizational improvement and knowledge management. These challenges manifest in tangible business consequences including extended development cycles, suboptimal resource utilization, poor market responsiveness, and limited innovation quality. Without systematic diagnostic understanding followed by targeted AI empowerment interventions, Company Y risks continued erosion of competitive advantage and growth prospects. ## 4. Hypothesis **Primary Hypothesis (H1)**: A systematic dual-cycle action research approach—combining comprehensive organizational diagnosis with targeted AI tool integration—significantly enhances organizational agility dimensions through qualitatively observable transformations in sensing, responding, learning, and adaptive capabilities, leading to product innovation acceleration in Chinese start-up contexts. **Secondary Hypotheses**: - **H2**: Cycle 1 organizational diagnosis reveals specific barriers, readiness factors, and stakeholder perspectives that significantly influence the effectiveness of subsequent AI empowerment interventions. - **H3**: Cycle 2 AI tool integration (Tongyi Lingma, Zhipu AI, Cursor, Jimeng AI, self-developed tools) creates synergistic organizational learning effects that amplify individual tool benefits when implemented based on diagnostic insights. - **H4**: The dual-cycle action research approach produces superior organizational transformation outcomes compared to direct AI implementation without prior diagnostic understanding. ## 5. Research Questions **Primary Research Question (RQ1)**: How does a dual-cycle action research approach—combining organizational diagnosis with AI empowerment implementation—enhance organizational agility to accelerate product innovation in Chinese start-up contexts? **Secondary Research Questions**: - **RQ2**: What specific organizational challenges, barriers, and readiness factors are revealed through systematic diagnostic analysis of Company Y's current innovation processes and AI preparedness? - **RQ3**: How do stakeholder perspectives, concerns, and expectations identified in the diagnostic cycle influence the design and implementation of AI empowerment interventions? - **RQ4**: What AI tools and integration strategies most effectively enhance different dimensions of organizational agility when implemented based on diagnostic insights? - **RQ5**: How do the cyclical Plan-Act-Observe-Reflect processes contribute to organizational learning and sustainable AI-enhanced capabilities? ## 6. Literature Review ### 6.1 Theoretical Foundations **Action Research Theory** provides the primary methodological framework for this investigation. Developed by Lewin (1946) and refined by Kemmis and McTaggart (2005), action research emphasizes the cyclical integration of research and practice through Plan-Act-Observe-Reflect cycles. This approach is particularly suitable for organizational transformation studies where understanding and change must occur simultaneously. **Dynamic Capabilities Theory** provides the theoretical lens for understanding AI empowerment as a strategic capability enhancement mechanism. AI technologies augment organizational sensing capabilities through advanced data analytics, enhance seizing capabilities through automated decision-making, and strengthen transforming capabilities through adaptive learning systems. This theory guides the qualitative investigation of how AI tools reshape organizational capabilities. **Resource-Based View (RBV)** explains how AI technologies constitute valuable, rare, inimitable, and non-substitutable (VRIN) resources that create sustainable competitive advantage. The integration of both commercial AI platforms and self-developed AI tools transforms organizational capabilities from static to dynamic, enabling continuous adaptation and innovation acceleration. ### 6.2 Action Research in Organizational Contexts Action research has proven particularly effective for studying organizational transformation processes where researchers must balance scientific rigor with practical intervention needs. Contemporary applications in organizational studies emphasize the importance of systematic diagnosis before intervention implementation, ensuring that change initiatives address root causes rather than symptoms. The dual-cycle approach builds on established action research traditions while addressing specific challenges of AI integration in start-up contexts. Cycle 1's diagnostic focus aligns with Checkland's (2000) emphasis on problem situation analysis, while Cycle 2's implementation focus follows Reason and Bradbury's (2008) participatory action research principles. ### 6.3 AI Empowerment in Organizational Contexts Contemporary research demonstrates that AI integration significantly enhances organizational capabilities across multiple dimensions. However, most existing research focuses on quantitative outcomes rather than qualitative transformation experiences and lacks systematic diagnostic understanding of implementation prerequisites. In Chinese contexts, AI empowerment benefits from advanced digital infrastructure and government policy support. The accessibility of AI tools has dramatically increased, with platforms like Tongyi Lingma (intelligent code assistance), Zhipu AI (data analysis and insights), Cursor (collaborative development), Jimeng AI (creative AI solutions), and self-developed proprietary tools enabling systematic integration for resource-constrained start-ups. ### 6.4 Research Gaps Current literature lacks comprehensive qualitative evidence on: 1. Systematic diagnostic approaches for assessing organizational readiness for AI empowerment 2. Stakeholder experiences with dual-cycle action research approaches to AI integration 3. Qualitative mechanisms through which diagnostic insights inform effective AI implementation strategies 4. Contextual factors influencing the effectiveness of cyclical action research approaches in Chinese start-up environments ## 7. Research Objectives **Primary Objective (RO1)**: To investigate how a dual-cycle action research approach—combining systematic organizational diagnosis with comprehensive AI empowerment implementation—enhances organizational agility to accelerate product innovation in Chinese start-up contexts. **Secondary Objectives**: - **RO2**: To conduct comprehensive diagnostic analysis of Company Y's organizational challenges, AI readiness, and stakeholder perspectives regarding AI empowerment (Cycle 1) - **RO3**: To implement and evaluate comprehensive AI tool integration strategies based on diagnostic insights, measuring their impact on organizational agility and innovation processes (Cycle 2) - **RO4**: To develop practical frameworks and guidelines for dual-cycle action research approaches to AI empowerment in resource-constrained start-up environments - **RO5**: To contribute theoretical understanding of how cyclical action research processes enhance AI empowerment effectiveness and organizational transformation outcomes ## 8. Methodology ### 8.1 Research Design This study employs a **dual-cycle action research methodology** with **primarily qualitative approach** specifically designed to systematically diagnose and address AI empowerment challenges within Company Y's organizational context. The research follows the classical action research paradigm of **Plan-Act-Observe-Reflect** across two interconnected cycles, emphasizing deep understanding of diagnostic insights and transformation experiences. **Research Philosophy**: Critical realist approach emphasizing understanding of underlying mechanisms and structures **Research Approach**: Abductive reasoning integrating theoretical frameworks with empirical discoveries through cyclical learning **Research Strategy**: Single case study using Company Y as comprehensive investigation context with dual-cycle intervention design **Time Horizon**: Longitudinal design spanning 24 months (October 2025 - September 2027) ### 8.2 Dual-Cycle Action Research Framework **Cycle 1: Organizational Diagnosis and Readiness Assessment (Months 3-14)** *Plan Phase (Months 3-5)*: - Design comprehensive diagnostic framework for organizational challenges, AI readiness, and stakeholder perspectives - Develop data collection instruments for systematic problem analysis - Establish baseline understanding of current innovation processes and capabilities *Act Phase (Months 6-10)*: - Conduct systematic organizational diagnosis through interviews, observations, and document analysis - Assess AI readiness across technical, cultural, and strategic dimensions - Map stakeholder perspectives, concerns, and expectations regarding AI empowerment *Observe Phase (Months 11-12)*: - Analyze diagnostic findings to identify root causes of innovation challenges - Evaluate organizational readiness factors and potential barriers to AI adoption - Document stakeholder insights and change readiness indicators *Reflect Phase (Months 13-14)*: - Synthesize diagnostic insights to inform Cycle 2 intervention design - Develop targeted AI empowerment strategy based on identified needs and readiness factors - Plan Cycle 2 implementation approach incorporating stakeholder perspectives **Cycle 2: AI Tool Integration and Organizational Transformation (Months 15-24)** *Plan Phase (Months 15-16)*: - Design comprehensive AI tool integration strategy based on Cycle 1 diagnostic insights - Develop implementation roadmap for five AI tool categories: Tongyi Lingma, Zhipu AI, Cursor, Jimeng AI, and self-developed tools - Establish transformation measurement framework and stakeholder engagement protocols *Act Phase (Months 17-21)*: - Implement systematic AI tool integration across organizational functions - Deploy AI-enhanced development processes and collaborative platforms - Facilitate organizational learning and adaptation to AI-empowered workflows *Observe Phase (Months 22-23)*: - Monitor AI integration impacts on organizational agility dimensions - Document stakeholder experiences and transformation outcomes - Assess innovation acceleration and competitive positioning improvements *Reflect Phase (Month 24)*: - Evaluate overall transformation outcomes and sustainability factors - Synthesize learning from both cycles for theoretical and practical contributions - Develop guidelines for replicable dual-cycle AI empowerment approaches ### 8.3 Data Collection Methods (Primarily Qualitative) **Cycle 1: Diagnostic Data Collection (Plan-Act-Observe-Reflect)**: *Plan Phase Data Collection*: - **Diagnostic Framework Documentation**: Recording of assessment criteria, stakeholder mapping, and data collection protocols - **Baseline Organizational Documentation**: Current process documentation, performance metrics, and organizational structure analysis *Act Phase Data Collection*: - **Comprehensive Diagnostic Interviews**: In-depth interviews with all 30 stakeholders focusing on innovation challenges, organizational barriers, and AI readiness perceptions - **Systematic Process Observation**: Ethnographic observation of current development workflows, decision-making patterns, and collaboration mechanisms - **AI Readiness Assessment**: Structured evaluation of technical infrastructure, skills gaps, cultural preparedness, and strategic alignment - **Stakeholder Perspective Mapping**: Focus groups and individual sessions examining attitudes, concerns, expectations, and change readiness *Observe Phase Data Collection*: - **Diagnostic Data Analysis Documentation**: Systematic recording of pattern identification, root cause analysis, and readiness factor evaluation - **Stakeholder Validation Sessions**: Member checking interviews to validate diagnostic findings and interpretations *Reflect Phase Data Collection*: - **Cycle 1 Learning Documentation**: Comprehensive recording of diagnostic insights, intervention strategy development, and planning decisions - **Stakeholder Feedback on Diagnostic Process**: Evaluation of diagnostic methodology effectiveness and participant experiences **Cycle 2: AI Integration Data Collection (Plan-Act-Observe-Reflect)**: *Plan Phase Data Collection*: - **Implementation Strategy Documentation**: Recording of AI tool selection rationale, integration planning, and stakeholder preparation protocols - **Pre-Implementation Baseline**: Detailed documentation of organizational state before AI integration begins *Act Phase Data Collection*: - **AI Integration Process Documentation**: Systematic recording of tool implementation processes, technical challenges, and adaptation strategies - **Stakeholder Experience Tracking**: Weekly interviews and reflective journals documenting AI adoption experiences, learning processes, and behavioral changes - **Organizational Transformation Observation**: Continuous ethnographic observation of workflow changes, collaboration patterns, and cultural shifts - **Performance Monitoring**: Regular documentation of process improvements, efficiency gains, and innovation acceleration indicators *Observe Phase Data Collection*: - **Transformation Impact Assessment**: Comprehensive evaluation of AI integration outcomes across organizational agility dimensions - **Stakeholder Satisfaction and Experience Analysis**: Detailed assessment of user experiences, adoption success, and perceived benefits - **Competitive Positioning Analysis**: Documentation of market responsiveness improvements and innovation performance changes *Reflect Phase Data Collection*: - **Cycle 2 Learning Documentation**: Systematic recording of transformation insights, success factors, and sustainability considerations - **Cross-Cycle Integration Analysis**: Comparative analysis of diagnostic predictions versus implementation outcomes **Cross-Cycle Data Integration**: - **Longitudinal Transformation Tracking**: Continuous monitoring of key indicators across both cycles using consistent measurement approaches - **Cyclical Learning Documentation**: Systematic recording of how Cycle 1 insights informed Cycle 2 effectiveness and outcomes - **Stakeholder Journey Mapping**: Comprehensive documentation of participant experiences and perspectives throughout both cycles - **Action Research Process Reflection**: Meta-analysis of the dual-cycle methodology effectiveness and learning processes ### 8.4 Sampling and Participants **Comprehensive Organizational Coverage**: - **All Company Y Employees (30 individuals)**: Complete organizational participation across R&D, marketing, management, and customer service functions - **Key Informants (8 leaders)**: Intensive engagement with team leaders and managers throughout both cycles - **Customer Advisory Panel (10-30 members)**: External perspective providers for innovation impact assessment - **External Experts (5-8 specialists)**: AI technology and organizational transformation consultants for validation and comparative insights **Cycle-Specific Engagement**: - **Cycle 1 Focus**: Emphasis on diagnostic interviews, readiness assessment, and change preparation - **Cycle 2 Focus**: Emphasis on implementation experiences, transformation documentation, and outcome evaluation ### 8.5 Data Analysis (Primarily Qualitative) **Cycle 1: Diagnostic Analysis (Plan-Act-Observe-Reflect)**: *Plan Phase Analysis*: - **Diagnostic Framework Validation**: Analysis of assessment criteria effectiveness and stakeholder mapping accuracy - **Baseline Analysis**: Systematic analysis of current organizational state and capability documentation *Act Phase Analysis*: - **Real-time Diagnostic Analysis**: Ongoing analysis of interview data, observation notes, and assessment results during data collection - **Pattern Recognition**: Identification of emerging themes related to innovation challenges, barriers, and readiness factors - **Stakeholder Perspective Analysis**: Systematic coding and categorization of stakeholder attitudes, concerns, and expectations *Observe Phase Analysis*: - **Root Cause Analysis**: Comprehensive analysis identifying underlying causes of innovation challenges and organizational barriers - **AI Readiness Assessment**: Systematic evaluation of technical, cultural, and strategic readiness factors - **Barrier and Enabler Identification**: Analysis of factors that facilitate or hinder potential AI integration *Reflect Phase Analysis*: - **Diagnostic Synthesis**: Integration of all Cycle 1 findings to create comprehensive organizational understanding - **Intervention Strategy Development**: Analysis-informed design of Cycle 2 AI integration approach - **Learning Documentation**: Analysis of diagnostic process effectiveness and methodological insights **Cycle 2: AI Integration Analysis (Plan-Act-Observe-Reflect)**: *Plan Phase Analysis*: - **Implementation Strategy Analysis**: Analysis of AI tool selection rationale and integration planning effectiveness - **Readiness-Strategy Alignment**: Assessment of how Cycle 1 insights informed Cycle 2 planning decisions *Act Phase Analysis*: - **Implementation Process Analysis**: Real-time analysis of AI integration experiences, challenges, and adaptation strategies - **Transformation Tracking**: Ongoing analysis of organizational changes, workflow modifications, and behavioral shifts - **Stakeholder Experience Analysis**: Continuous analysis of user adoption patterns, learning processes, and satisfaction indicators *Observe Phase Analysis*: - **Impact Assessment**: Comprehensive analysis of AI integration outcomes across organizational agility dimensions - **Performance Analysis**: Systematic evaluation of innovation acceleration, efficiency improvements, and competitive positioning changes - **Success Factor Analysis**: Identification of factors contributing to successful AI integration and transformation *Reflect Phase Analysis*: - **Transformation Outcome Analysis**: Comprehensive evaluation of overall organizational transformation achieved - **Sustainability Analysis**: Assessment of transformation sustainability and continuous improvement potential - **Cross-Cycle Learning Integration**: Analysis of how diagnostic insights influenced implementation effectiveness **Cross-Cycle Analytical Integration**: - **Longitudinal Analysis**: Systematic analysis of transformation patterns and trends across both cycles - **Predictive Validity Analysis**: Comparison of Cycle 1 diagnostic predictions with Cycle 2 implementation outcomes - **Cyclical Learning Analysis**: Understanding of how Plan-Act-Observe-Reflect processes contributed to organizational learning - **Methodology Effectiveness Analysis**: Meta-analysis of dual-cycle action research approach effectiveness **Analytical Methods and Tools**: - **Action Research Analysis Framework**: Systematic analysis following Kemmis and McTaggart's (2005) cyclical analysis approach - **Thematic Analysis**: Following Braun and Clarke's (2006) framework for pattern identification across both cycles - **Narrative Analysis**: Understanding stakeholder transformation journeys and organizational change stories - **Grounded Theory Approach**: Building theoretical understanding from dual-cycle empirical experiences - **Comparative Analysis**: Systematic comparison of pre-intervention, inter-cycle, and post-intervention organizational states - **NVivo Software**: Comprehensive qualitative data management, coding, and analysis support for dual-cycle data integration ## 9. Flow Chart of Research Activities ``` Preparation Phase (Months 1-2) ├── Literature Review and Theoretical Framework Development ├── Research Design Finalization and Ethical Approval ├── Baseline Data Collection and Stakeholder Mapping └── Action Research Protocol Development CYCLE 1: ORGANIZATIONAL DIAGNOSIS (Months 3-14) │ ├── PLAN Phase (Months 3-5) │   ├── Diagnostic Framework Development │   ├── Data Collection Instrument Design │   ├── Stakeholder Engagement Protocol │   └── Baseline Assessment Planning │ ├── ACT Phase (Months 6-10) │   ├── Comprehensive Organizational Assessment │   │   ├── Individual Stakeholder Interviews (All 30 employees) │   │   ├── Process Observation and Documentation │   │   ├── Organizational Culture Analysis │   │   └── Innovation Capability Assessment │   ├── AI Readiness Evaluation │   │   ├── Technical Infrastructure Assessment │   │   ├── Skills and Knowledge Gap Analysis │   │   ├── Cultural Readiness Evaluation │   │   └── Strategic Alignment Assessment │   └── Stakeholder Perspective Mapping │       ├── AI Perception and Attitude Analysis │       ├── Change Readiness Assessment │       └── Expectation and Concern Documentation │ ├── OBSERVE Phase (Months 11-12) │   ├── Diagnostic Data Analysis and Pattern Identification │   ├── Root Cause Analysis of Innovation Challenges │   ├── Readiness Factor Evaluation and Barrier Assessment │   └── Stakeholder Insight Synthesis │ └── REFLECT Phase (Months 13-14)     ├── Diagnostic Findings Integration and Validation     ├── Cycle 2 Intervention Strategy Development     ├── AI Tool Selection and Implementation Planning     └── Stakeholder Engagement and Change Management Planning CYCLE 2: AI INTEGRATION AND TRANSFORMATION (Months 15-24) │ ├── PLAN Phase (Months 15-16) │   ├── AI Integration Strategy Finalization (Based on Cycle 1 Insights) │   ├── Tool Implementation Roadmap Development │   │   ├── Tongyi Lingma Integration Planning │   │   ├── Zhipu AI Implementation Design │   │   ├── Cursor Deployment Strategy │   │   ├── Jimeng AI Integration Approach │   │   └── Self-developed Tools Development Planning │   ├── Transformation Measurement Framework │   └── Stakeholder Training and Support Planning │ ├── ACT Phase (Months 17-21) │   ├── Sequential AI Tool Implementation │   │   ├── Months 17-18: Tongyi Lingma and Zhipu AI Integration │   │   ├── Months 19-20: Cursor and Jimeng AI Implementation │   │   └── Month 21: Self-developed Tools Integration │   ├── Organizational Process Transformation │   │   ├── AI-Enhanced Development Workflows │   │   ├── Collaborative Platform Implementation │   │   └── Knowledge Management System Development │   └── Continuous Stakeholder Support and Training │       ├── Weekly Training Sessions │       ├── Peer Learning Networks │       └── Expert Mentoring Programs │ ├── OBSERVE Phase (Months 22-23) │   ├── AI Integration Impact Assessment │   ├── Organizational Agility Improvement Documentation │   ├── Innovation Process Acceleration Analysis │   ├── Stakeholder Experience and Satisfaction Evaluation │   └── Competitive Positioning and Performance Analysis │ └── REFLECT Phase (Month 24)     ├── Comprehensive Transformation Outcome Evaluation     ├── Dual-Cycle Learning Integration and Synthesis     ├── Best Practices and Guidelines Development     ├── Sustainability and Continuous Improvement Planning     └── Theoretical Contribution and Practical Framework Development Final Phase: Dissemination and Knowledge Transfer (Months 24+) ├── Academic Publication Development (2 Scopus Papers) ├── Conference Presentation Preparation ├── Practical Guidelines and Framework Documentation └── Knowledge Transfer and Implementation Support ``` ## 10. Research Activities ### 10.1 Preparation Activities (Months 1-2) - Comprehensive literature review focusing on action research methodology and AI empowerment - Dual-cycle research framework development and validation - Ethical approval procedures and comprehensive informed consent protocols - Baseline organizational assessment and stakeholder relationship establishment - Research protocol finalization and data collection instrument development ### 10.2 Cycle 1: Diagnostic Activities (Months 3-14) **Plan Phase Activities**: - Diagnostic framework development incorporating organizational assessment, AI readiness evaluation, and stakeholder analysis - Interview protocols, observation frameworks, and assessment instrument design - Stakeholder engagement strategy and communication planning **Act Phase Activities**: - Comprehensive organizational diagnosis through systematic interviews with all 30 employees - Process observation and documentation of current innovation workflows - AI readiness assessment across technical, cultural, and strategic dimensions - Stakeholder perspective mapping including attitudes, concerns, and expectations **Observe Phase Activities**: - Thematic analysis of diagnostic data and pattern identification - Root cause analysis of innovation challenges and organizational barriers - Readiness factor evaluation and implementation barrier assessment **Reflect Phase Activities**: - Diagnostic findings synthesis and stakeholder validation - Cycle 2 intervention strategy development based on diagnostic insights - AI tool selection and implementation planning incorporating stakeholder perspectives ### 10.3 Cycle 2: Implementation Activities (Months 15-24) **Plan Phase Activities**: - Comprehensive AI integration strategy development based on Cycle 1 insights - Implementation roadmap creation for all five AI tool categories - Transformation measurement framework and stakeholder support planning **Act Phase Activities**: - Sequential AI tool implementation: Tongyi Lingma, Zhipu AI, Cursor, Jimeng AI, and self-developed tools - Organizational process transformation and AI-enhanced workflow development - Continuous stakeholder training, support, and collaborative learning facilitation **Observe Phase Activities**: - AI integration impact assessment across organizational agility dimensions - Innovation acceleration and competitive positioning improvement documentation - Stakeholder experience analysis and transformation outcome evaluation **Reflect Phase Activities**: - Comprehensive dual-cycle learning integration and outcome synthesis - Best practices documentation and replicable framework development - Sustainability planning and continuous improvement strategy development ### 10.4 Cross-Cycle Integration Activities (Ongoing) - Continuous comparative analysis between diagnostic predictions and implementation outcomes - Longitudinal tracking of transformation indicators and stakeholder perspectives - Regular validation and member checking with participants across both cycles - Theoretical development and practical framework refinement ## 11. Milestones and Dates | Milestone | Target Date | Deliverable | |-----------|-------------|-------------| | Research Proposal Approval | October 31, 2025 | Approved research proposal and ethical clearance | | Cycle 1 Plan Phase Complete | February 28, 2026 | Diagnostic framework and data collection instruments | | Cycle 1 Act Phase Complete | June 30, 2026 | Comprehensive organizational diagnosis report | | Cycle 1 Observe Phase Complete | August 31, 2026 | Root cause analysis and readiness assessment | | Cycle 1 Reflect Phase Complete | October 31, 2026 | AI integration strategy and Cycle 2 planning | | Cycle 2 Plan Phase Complete | December 31, 2026 | Implementation roadmap and measurement framework | | Cycle 2 Act Phase Complete | May 31, 2027 | AI tool integration and process transformation | | Cycle 2 Observe Phase Complete | July 31, 2027 | Transformation impact assessment | | Cycle 2 Reflect Phase Complete | August 31, 2027 | Dual-cycle learning synthesis and framework development | | First Academic Paper Submission | September 15, 2027 | Scopus journal submission (diagnostic methodology) | | Second Academic Paper Submission | September 30, 2027 | Scopus journal submission (transformation outcomes) | ### Key Deliverables Timeline **Year 1 (October 2025 - September 2026)**: - Month 5: Diagnostic framework and methodology development - Month 10: Comprehensive organizational diagnosis completion - Month 12: AI readiness assessment and barrier analysis - Month 14: Cycle 2 intervention strategy development **Year 2 (October 2026 - September 2027)**: - Month 16: AI integration implementation planning - Month 21: Complete AI tool integration and process transformation - Month 23: Transformation impact assessment and outcome evaluation - Month 24: Dual-cycle learning synthesis and academic publication preparation ## 12. Expected Results / Benefits ### 12.1 Theoretical Contributions **Dual-Cycle Action Research Methodology**: The research will contribute a validated dual-cycle action research framework specifically designed for AI empowerment in organizational contexts, demonstrating how systematic diagnosis enhances intervention effectiveness. **Diagnostic-Implementation Integration Theory**: Findings will develop theoretical understanding of how organizational diagnosis informs effective AI integration strategies, contributing to both action research methodology and AI adoption literature. **Organizational Transformation Process Model**: The study will generate a comprehensive process model explaining how cyclical action research approaches facilitate sustainable organizational transformation through AI empowerment. ### 12.2 Practical Benefits **Dual-Cycle Implementation Framework**: Creation of practical guidelines enabling start-ups to systematically diagnose organizational readiness before implementing AI empowerment initiatives, reducing implementation risks and improving success rates. **Diagnostic Assessment Tools**: Development of validated instruments for assessing organizational readiness, stakeholder perspectives, and AI integration prerequisites in start-up contexts. **AI Integration Best Practices**: Comprehensive documentation of effective AI tool integration strategies based on diagnostic insights, providing replicable approaches for similar organizations. ### 12.3 Organizational Impact for Company Y **Cycle 1 Diagnostic Outcomes**: - Comprehensive understanding of innovation challenges and root causes - Clear assessment of AI readiness and implementation prerequisites - Detailed stakeholder perspective mapping and change readiness evaluation - Evidence-based foundation for targeted AI empowerment strategies **Cycle 2 Transformation Outcomes**: - Systematic AI tool integration across five categories with measurable impact - Enhanced organizational agility through improved sensing, responding, learning, and adaptive capabilities - Accelerated innovation processes and improved competitive positioning - Sustainable AI-enhanced organizational capabilities and continuous improvement systems ### 12.4 Academic Contributions **Publication Outcomes**: - **Paper 1**: "Dual-Cycle Action Research for AI Empowerment: A Diagnostic-Implementation Framework for Organizational Transformation" (Target: Action Research or similar Scopus Q1 journal) - **Paper 2**: "From Diagnosis to Transformation: AI Empowerment Experiences in Chinese Start-ups Through Cyclical Action Research" (Target: Journal of Business Research or similar Scopus Q1 journal) **Conference Presentations**: - Action Learning, Action Research Association (ALARA) World Congress - Academy of Management Annual Meeting (Research Methods Division) - International Conference on Organizational Learning and Change ### 12.5 Broader Impact **Action Research Methodology**: Contribution to action research methodology through validated dual-cycle approaches applicable beyond AI empowerment contexts. **Chinese Start-up Ecosystem**: Evidence-based guidance for systematic AI adoption in Chinese start-ups, contributing to ecosystem competitiveness and innovation capacity. **Policy and Practice**: Insights informing government policies and support programs for AI adoption in start-up environments, particularly regarding diagnostic assessment and implementation support. --- ## Supporting Materials **Interactive Charts and Diagrams**: Comprehensive visual representations of the dual-cycle action research framework, diagnostic methodology, AI tools integration strategy, stakeholder analysis matrix, research timeline, and transformation model are available in the accompanying HTML file (`research_proposal_charts_v3.html`). These charts include export functionality for presentation and publication purposes. **Key Visual Components**: - Dual-Cycle Action Research Framework (Plan-Act-Observe-Reflect) - Cycle 1: Organizational Diagnosis Methodology - Cycle 2: AI Integration and Transformation Process - Comprehensive AI Tools Integration Strategy (5 categories) - Stakeholder Analysis and Engagement Framework - 24-month Research Timeline with cycle-specific milestones --- ## Research Team and Timeline **Research Team**: - **Principal Investigator**: Dr. Farah (Lead Researcher) - **Research Student**: Leo (Weng Yonggang) (PhD Candidate, Primary Implementer) **Research Period**: October 1, 2025 - September 30, 2027 (24 months) **Expected Deliverables**: - Two Scopus-indexed journal publications focusing on dual-cycle action research methodology and AI empowerment transformation outcomes - Comprehensive action research report with diagnostic tools and implementation guidelines - Validated frameworks for organizational diagnosis and AI integration assessment - Conference presentations at international action research and organizational studies venues - Best practices documentation for dual-cycle AI empowerment approaches in start-up contexts **Budget Considerations**: Research funding will support comprehensive diagnostic activities, AI tool licensing (5 categories), external consultation and validation, qualitative data collection infrastructure, transcription and analysis services, publication fees, and conference participation costs. **Success Criteria**: - Successful completion of dual-cycle action research with validated diagnostic insights and transformation outcomes - Demonstrated improvement in Company Y's organizational agility and innovation performance through systematic measurement - Development of replicable dual-cycle frameworks for AI empowerment in start-up contexts - Significant theoretical and practical contributions to action research methodology and AI adoption literature