Title 1

Understanding Managerial Perceptions and Strategic Responses to Generative AI Integration in Manufacturing Firms: A Case Study

Methodology

Qualitative - case-based approach.

Data collection - via semi-structured interviews or focus groups across different firms. A thematic analysis will be used to analyse.

Description

Case study approach to explore how senior-level managers in Manufacturing Firms perceive the opportunities and challenges of Generative AI in operations and product development. Data can be collected via semi-structured interviews or focus groups across different firms. A thematic analysis will be used.

Key References:

1. Khan, S., Mehmood, S., & Khan, S. U. (2025). Navigating innovation in the age of AI: how generative AI and innovation influence organisational performance in the manufacturing sector. Journal of Manufacturing Technology Management, 36(3), 597-620.
2. Chowdhury, S., Budhwar, P., & Wood, G. (2024). Generative artificial intelligence in business: towards a strategic human resource management framework. British Journal of Management, 35(4), 1680-1691.
3. Doshi, A. R., Bell, J. J., Mirzayev, E., & Vanneste, B. S. (2025). Generative artificial intelligence and evaluating strategic decisions. Strategic Management Journal, 46(3), 583-610.

Title 2

Assessing the Determinants of Generative AI Adoption for Product Innovation: A Structural Equation Modelling Approach

Methodology

Quantitative - Structural Equation Modelling Approach

Description

This study will develop and validate a model based on Technology Acceptance Model, Unified Theory of Acceptance and Use of Technology, or Technology-Organisation-Environment frameworks to assess key enablers such as perceived usefulness, top management support, data infrastructure, and innovation culture in the manufacturing industry. Data will be gathered via structured questionnaires from tech managers.

Key References:

1. Kim, C. (2025). Understanding Factors Influencing Generative AI Use Intention: A Bayesian Network-Based Probabilistic Structural Equation Model Approach. Electronics, 14(3), 530.
2. Gupta, V. (2024). An empirical evaluation of a generative artificial intelligence technology adoption model from entrepreneurs' perspectives. Systems, 12(3), 103.
3. Wang, S., & Zhang, H. (2025). Leveraging generative artificial intelligence for sustainable business model innovation in production systems. International Journal of Production Research, 1-26.

Title 3

Bridging the Gap Between AI Capability and Innovation Outcomes: A Mixed-Methods Study of Generative AI Implementation in Tech-Driven Enterprises

Methodology

Mixed Methods - Exploratory Sequential

Qualitative data - through in-depth interviews 

Quantitative - through Self-administered questionnaire

Description

This research starts with qualitative interviews to explore how firms develop generative AI capabilities. Insights will inform a survey instrument for SEM analysis to examine relationships between AI capability, organisational learning, and innovation performance.

Key References:

1. Wamba, S. F., Queiroz, M. M., Randhawa, K., & Gupta, G. (2025). Generative artificial intelligence and the challenges to adding value ethically. Technovation, 144, 103235.
2. Xu, H., Xu, R., Lin, H., & He, X. (2024, January). The Impact of Generative Artificial Intelligence on Organisational Innovation Performance: Roles of AI Generated Content Quality, AI Experience, and AI Usage Environment. In 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS) (pp. 1802-1807). IEEE.
3. Corvello, V. (2025). Generative AI and the future of innovation management: A human centred perspective and an agenda for future research. Journal of Open Innovation: Technology, Market, and Complexity, 11(1), 100456.

Title 4

Exploring Strategic Perceptions of Generative AI for Competitive Advantage: A Qualitative Inquiry Among Industry Leaders

Methodology

Qualitative - Case Study using Semi-Structured Interviews

Data collection - In-depth interviews

Description

This study explores how senior executives and innovation managers perceive the role of Generative AI in reshaping business models, enhancing productivity, and driving sustainable competitive advantage. Data will be gathered through in-depth interviews and analysed thematically to capture emerging strategic narratives and industry-specific patterns.

Key References:

1. Cui, Y. G., van Esch, P., & Phelan, S. (2024). How to build a competitive advantage for your brand using generative AI. Business Horizons, 67(5), 583-594.
2. Cook, S., Hagiu, A., & Wright, J. (2024). Turn generative AI from an existential threat into a competitive advantage. Harvard Business Review, 102(1), 118-125.

Soni, V. (2023). Impact of generative AI on small and medium enterprises' revenue growth: the moderating role of human, technological, and market factors. Reviews of Contemporary Business Analytics, 6(1), 133-153.

Title 5

Assessing the Impact of Generative AI Capabilities on Competitive Advantage: A Structural Equation Modelling Approach

Methodology

Quantitative - Survey and SEM

Data collection - Questionnaire

Description

This research examines the relationship between Generative AI capabilities (e.g., content generation, process automation, customer interaction) and perceived competitive advantage in industry sectors. A structured questionnaire will be administered to professionals, and SEM will be used to model latent constructs like Technological Readiness, Innovation Capability, and Market Responsiveness.

Key References:

1. Wael AL-khatib, A. (2023). Drivers of generative artificial intelligence to fostering exploitative and exploratory innovation: A TOE framework. Technology in Society, 75, 102403.
2. Khan, S., Mehmood, S., & Khan, S. U. (2025). Navigating innovation in the age of AI: how generative AI and innovation influence organisational performance in the manufacturing sector. Journal of Manufacturing Technology Management, 36(3), 597-620.
3. Soni, V. (2023). Impact of generative AI on small and medium enterprises' revenue growth: the moderating role of human, technological, and market factors. Reviews of Contemporary Business Analytics, 6(1), 133-153.

Title 6

Integrating Human Expertise and Generative AI for Sustained Competitive Edge: A Mixed Methods Study

Methodology

Mixed Methods - Quantitative (SEM), Qualitative Interviews

Quantitative - Questionnaire

Qualitative - Semi-structured interviews

Description

This study investigates how the synergy between human expertise and Generative AI tools contributes to sustained competitive advantage. Phase 1 involves a survey analysed using SEM to identify key enablers of AI-human integration. Phase 2 involves follow-up interviews with high-performing firms to explore contextual and cultural dynamics behind successful integration strategies.

Key References:

1. Budhwar, P., Chowdhury, S., Wood, G., Aguinis, H., Bamber, G. J., Beltran, J. R., Boselie, P., Cooke, F. L., Decker, S., DeNisi, A., Dey, P. K., Guest, D., Knoblich, A. J., Malik, A., Paauwe, J., Papagiannidis, S., Patel, C., Pereira, V., Ren, S., Rogelberg, S., Saunders, M. N. K., Tung, R. L., & Varma, A. (2023). Human resource management in the age of generative artificial intelligence: Perspectives and research directions on ChatGPT. Human Resource Management Journal, 33(3), 606-659. 
2. Chowdhury, S., Budhwar, P., & Wood, G. (2024). Generative artificial intelligence in business: towards a strategic human resource management framework. British Journal of Management, 35(4), 1680-1691.
3. Wamba, S. F., Queiroz, M. M., Randhawa, K., & Gupta, G. (2025). Generative artificial intelligence and the challenges to adding value ethically. Technovation, 144, 103235.

Title 7

Exploring Managerial Perceptions on the Role of Generative AI in Crafting Hyper-Personalised Marketing Strategies

Methodology

Qualitative - Case-based; In-depth interviews/semi-structured

Description

This study explores how marketing managers across various industries perceive the impact of generative AI (like ChatGPT, DALL·E, etc.) on creating hyper-personalised customer experiences. It involves case-based qualitative inquiry using semi-structured interviews to uncover themes related to trust, creativity, data ethics, and strategic value. Ideal for professionals with access to senior marketers or AI specialists.

Key References:

1. Singh, B., & Kaunert, C. (2024). Future of Digital Marketing: Hyper-Personalised Customer Dynamic Experience with AI-Based Predictive Models. In Revolutionising the AI-Digital Landscape (pp. 189-203). Productivity Press.
2. Patil, D. (2024). Generative Artificial Intelligence in Marketing and Advertising: Advancing Customer Engagement Through Personalised Content. Journal of Marketing Automation, 8(2), 145-162.
3. Reddy, J. K. (2024). Leveraging Generative AI for Hyper Personalised Rewards and Benefits Programs: Analysing Consumer Behaviour in Financial Loyalty Systems. J. Electrical Systems, 20(11s), 3647-3657.

Title 8

Impact of Generative AI Capabilities on Customer Engagement through Personalisation: A Structural Equation Modelling Approach

Methodology

Quantitative - Survey-based; SEM

Description

This study investigates the relationship between key dimensions of generative AI capabilities (e.g., content generation, personalisation accuracy, real-time adaptability) and customer engagement outcomes in marketing. Data will be collected via structured questionnaires and analysed using Structural Equation Modelling (SEM). Designed for professionals in marketing or customer experience departments.

Key References:

1. Reddy, S. G., Sadhu, A. K. R., Muravev, M., Brazhenko, D., & Parfenov, M. (2023). Harnessing the power of generative artificial intelligence for dynamic content personalisation in customer relationship management systems: A data-driven framework for optimising customer engagement and experience. Journal of AI-Assisted Scientific Discovery, 3(2), 379-395.
2. Sriram, H. K. (2023). Harnessing AI Neural Networks and Generative AI for Advanced Customer Engagement: Insights into Loyalty Programs, Marketing Automation, and Real-Time Analytics. Educational Administration: Theory and Practice, 29(4), 4361-4374.
3. Wiputra, R., Rafindio, M., Kurniawan, A. R., & Rahardjo, A. R. (2024, October). Exploring the Impact of Generative AI on Customer Engagement in Digital Marketing. In 2024 Ninth International Conference on Informatics and Computing (ICIC) (pp. 1-6). IEEE.

Title 9

Evaluating the Integration of Generative AI for Personalised Marketing: A Mixed Methods Study of Strategic Impact and User Acceptance

Methodology

Mixed Methods - Sequential: Qual → Quan; Interviews + Survey/SEM

Description

This study begins with qualitative interviews of marketing heads to explore the strategic integration of generative AI tools. Insights will then guide a quantitative survey examining user acceptance (using models like Technology Acceptance Model) and perceived effectiveness. SEM will be used for the quantitative phase, making this an ideal approach for professionals who can access both strategic and operational staff.

Key References:

1. Lee, G. H., Lee, K. J., Jeong, B., & Kim, T. (2024). Developing personalised marketing service using generative AI. IEEE Access, 12, 22394-22402.
2. Soni, V. (2023). Adopting generative AI in digital marketing campaigns: An empirical study of drivers and barriers. Sage Science Review of Applied Machine Learning, 6(8), 1-15.
3. Yoo, S. C., & Piscarac, D. (2023). Generative AI and its implications for modern marketing: Analysing potential challenges and opportunities. The International Journal of Advanced Smart Convergence, 12(3), 175-185.

Title 10

Exploring Perceptions of Generative AI in HR Automation: A Qualitative Study Among Talent Acquisition Professionals

Methodology

Qualitative - Case-based; Semi-structured interviews

Description

This study investigates how HR professionals perceive the integration of Generative AI in talent acquisition processes. Using semi-structured interviews across diverse industries, the research explores opportunities, ethical concerns, and challenges related to AI-generated job descriptions, candidate matching, and onboarding communication. The findings will help build practical guidelines for AI-enabled HR adoption.

Key References:

1. Schecter, A., & Richardson, B. (2025, April). How the Role of Generative AI Shapes Perceptions of Value in Human-AI Collaborative Work. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (pp. 1-15).
2. Rane, N. (2024). Role and challenges of ChatGPT, Gemini, and similar generative artificial intelligence in human resource management. Studies in Economics and Business Relations, 5(1), 11-23.
3. Callari, T. C., & Puppione, L. (2025). Can generative artificial intelligence productivity tools support workplace learning? A qualitative study on employee perceptions in a multinational corporation. Journal of Workplace Learning.

Title 11

Human-AI Synergy in HR Automation: A Mixed Methods Study of Generative AI Implementation Outcomes

Methodology

Mixed Methods - Quantitative survey with SEM, Follow-up in-depth interviews

Description

This research assesses the organisational outcomes of Generative AI adoption in HR - such as efficiency, employee satisfaction, and decision quality. The quantitative phase uses SEM to model relationships among AI capabilities, HRM outcomes, and perceived risk. The qualitative phase adds depth by exploring insights through interviews with HR directors and technology officers, revealing context-specific best practices and limitations.

Key References:

1. Liu, Y., & Shen, L. (2025). Consolidating Human-AI Collaboration Research in Organisations: A Literature Review. Journal of Computer, Signal, and System Research, 2(1), 131-151.
2. Kabir, M. N. (2024). Unleashing Human Potential: A Framework for Augmenting Co-Creation with Generative AI. In Proceedings of the International Conference on AI Research. Academic Conferences and Publishing Limited.
3. Benabou, A., & Touhami, F. (2025). Integration of artificial intelligence in human resource management: analysing opportunities, challenges, and human-AI collaboration. Management & Accounting Review (MAR), 24(1), 405-437.

Title 12

Assessing the Impact of Ethical Governance on Organisational Trust and Innovation in the Era of Generative AI

Methodology

Quantitative - Survey-based using Structural Equation Modelling - SEM

Description

This study develops a conceptual model to measure the relationship between ethical governance mechanisms (transparency, accountability, data fairness) and organisational outcomes like trust, innovation capability, and employee perception of AI fairness. Using SEM, the study empirically validates the model based on survey data from professionals working in AI-focused roles.

Key References:

1. Rana, N. P., Pillai, R., Sivathanu, B., & Malik, N. (2024). Assessing the nexus of Generative AI adoption, ethical considerations and organisational performance. Technovation, 135, 103064.
2. Ibrahim, Y. (2024). Unravelling the Impact of Ethical Generative AI on Organisational Performance: A Hybrid Causal and Predictive Analysis Using SEM and Machine Learning. Available at SSRN 5251305.
3. Shahzad, M. F., Xu, S., & Zahid, H. (2025). Exploring the impact of generative AI-based technologies on learning performance through self-efficacy, fairness & ethics, creativity, and trust in higher education. Education and Information Technologies, 30(3), 3691-3716.