Title 1

Managerial Perceptions of Data-Driven Decision-Making in Technology-Intensive Industries

Methodology

Qualitative - Case-study

Data collection - Semi-Structured Interviews

Description

This study explores how senior level managers in technology-intensive sectors perceive the value, challenges, and cultural implications of implementing data-driven decision-making. Through in-depth interviews across multiple firms, the research uncovers organisational narratives around data governance, trust in analytics, and leadership adaptation in tech-enabled environments.

Key References:

1. Mahabub, S., Hossain, M. R., & Snigdha, E. Z. (2025). Data-Driven Decision-Making and Strategic Leadership: AI-Powered Business Operations for Competitive Advantage and Sustainable Growth. Journal of Computer Science and Technology Studies, 7(1), 326-336.
2. Davis, R., Vochozka, M., Vrbka, J., & Neguriţă, O. (2020). Industrial artificial intelligence, smart connected sensors, and big data-driven decision-making processes in Internet of Things-based real-time production logistics. Economics, Management and Financial Markets, 15(3), 9-15.
3. Nisar, Q. A., Nasir, N., Jamshed, S., Naz, S., Ali, M., & Ali, S. (2021). Big data management and environmental performance: role of big data decision-making capabilities and decision-making quality. Journal of Enterprise Information Management, 34(4), 1061-1096.

Title 2

Examining the Impact of Data Analytics Capabilities on Technology Innovation Performance in Manufacturing Firms

Methodology

Quantitative – Survey-based, Structural Equation Modelling (SEM)

Data collection – Self-administered Questionnaire

Description

This research investigates how different dimensions of data analytics capabilities (e.g., data infrastructure, analytical skills, and data-driven culture) influence technology innovation outcomes. Using a structured questionnaire distributed across manufacturing firms, the model employs SEM to test hypothesised relationships between analytics capability constructs and innovation performance.

Key References:

1. Chen, C. H. (2024). Influence of big data analytical capability on new product performance--the effects of collaboration capability and team collaboration in high-tech firm. Chinese Management Studies, 18(1), 1-23.
2. Lozada, N., Arias-Pérez, J., & Henao-García, E. A. (2023). Unveiling the effects of big data analytics capability on innovation capability through absorptive capacity: why more and better insights matter. Journal of Enterprise Information Management, 36(2), 680-701.
3. Arias-Pérez, J., Coronado-Medina, A., & Perdomo-Charry, G. (2022). Big data analytics capability as a mediator in the impact of open innovation on firm performance. Journal of Strategy and Management, 15(1), 1-15.

Title 3

The Role of Organisational Readiness and Data Culture in Successful Technology Adoption: A Mixed Methods Study

Methodology

Mixed Methods (Quantitative SEM + Qualitative Case Studies)

Explanatory Sequential Design

Qualitative data - Semi-structured interviews

Quantitative data - Questionnaire

Description

This study combines a survey to assess the impact of organisational readiness, data literacy, and culture on the success of advanced technology adoption (e.g., AI, IoT) with follow-up case studies to understand implementation nuances. The quantitative phase uses SEM to model relationships, while qualitative insights from key stakeholders validate and contextualise the findings.

Key References:

1. Ghaleb, E. A., Dominic, P. D. D., Fati, S. M., Muneer, A., & Ali, R. F. (2021). The assessment of big data adoption readiness with a technology-organisation–environment framework: a perspective towards healthcare employees. Sustainability, 13(15), 8379.
2. Kollmann, T., Kuckertz, A., & Breugst, N. (2009). Organisational readiness and the adoption of electronic business: The moderating role of national culture in 29 European countries. ACM SIGMIS Database: The DATABASE for Advances in Information Systems, 40(4), 117-131.
3. Nasrollahi, M., & Ramezani, J. (2020). A model to evaluate the organisational readiness for big data adoption. International Journal of Computers Communications & Control, 15(3).

Title 4

Interpreting Organisational Sensemaking through AI-Powered Business Intelligence Dashboards: A Multi-Case Industry Perspective

Methodology

Qualitative - Case-Based with In-Depth Interviews and Thematic Analysis

Description

This study explores how industry professionals across sectors interpret, contextualise, and act upon real-time AI-generated business intelligence insights. It investigates how decision-makers in different industries perceive the usability, credibility, and integration challenges of AI dashboards, using in-depth interviews with managers and data scientists.

Key References:

1. Basole, R. C., Park, H., & Seuss, C. D. (2024). Complex business ecosystem intelligence using AI-powered visual analytics. Decision Support Systems, 178, 114133.
2. Wilhelmová, V., & Shin, W. S. (2021). Leveraging and Being Leveraged by Big Data and AI: An Exploration of Tech Strategists' Sensemaking of Human--Machine Dynamics in Strategic Decision-Making.

Edge, D., Larson, J., & White, C. (2018, April). Bringing AI to BI: enabling visual analytics of unstructured data in a modern Business Intelligence platform. In Extended abstract

Title 5

Modelling the Antecedents of Strategic Decision-Making Effectiveness through AI-Driven Business Intelligence Systems: An SEM Approach

Methodology

Quantitative - Survey-Based, using Structural Equation Modelling (SEM)

Description

This study develops and tests a structural model identifying key antecedents (such as data literacy, system trust, AI explainability, and organisational readiness) that influence the effectiveness of strategic decision-making when using AI-based BI tools. Data will be collected via structured questionnaires from professionals using BI platforms across manufacturing, finance, and retail sectors.

Key References:

1. Oraif, G. (2024). AI-Driven Business Analytics: Its Impact on Strategic Decision-Making (An Empirical Study on Educational Institutions in the Kingdom of Saudi Arabia). Journal of Ecohumanism, 3(8), 9712-9732.
2. Siddiqui, N. A. (2025). Optimising Business Decision-Making Through AI-Enhanced Business Intelligence Systems: A Systematic Review of Data-Driven Insights in Financial and Strategic Planning. Strategic Data Management and Innovation, 2(01), 202-223.
3. Badmus, O., Rajput, S. A., Arogundade, J. B., & Williams, M. (2024). AI-driven business analytics and decision making. World Journal of Advanced Research and Reviews, 24(1), 616-633.

Title 6

Bridging Cognitive Trust and Analytical Rigour: A Mixed Methods Exploration of AI-Driven Business Intelligence Adoption in Competitive Environments

Methodology

Mixed Methods - Sequential Explanatory Design (Quant → Qual)

Description

This research first quantitatively examines the impact of AI capability, analytical culture, and perceived BI usefulness on organisational trust and adoption using SEM. It then qualitatively explores, through focus groups, how these factors translate into real-world decision practices under market competition, adding depth to the survey findings.

Key References:

1. Eboigbe, E. O., Farayola, O. A., Olatoye, F. O., Nnabugwu, O. C., & Daraojimba, C. (2023). Business intelligence transformation through AI and data analytics. Engineering Science & Technology Journal, 4(5), 285-307.
2. Michael, C. I., Ipede, O. J., Adejumo, A. D., Adenekan, I. O., Adebayo, D., Ojo, A. S., & Ayodele, P. A. (2024). Data-driven decision making in IT: Leveraging AI and data science for business intelligence. World Journal of Advanced Research and Reviews, 23(1), 472-480.
3. Adeleke, A. B., Oladele, T. O., Adeniyan, E. A., & Bamidele, O. F. (2023). Integrating business intelligence frameworks with AI capabilities for enhanced decision support. Journal of Business Analytics, 8(2), 119-137.

Title 7

Decoding Executive Intuition: How Industry Leaders Interpret Predictive Analytics in Shaping Future Market Strategy

Methodology

Qualitative - Case-Based, In-depth Interviews

Description

This study explores how senior decision-makers across various industries perceive and integrate predictive analytics insights into their strategic planning. Through in-depth interviews with business leaders and data scientists, the research uncovers cognitive, cultural, and organisational factors influencing interpretation and trust in data-driven forecasts.

Key References:

1. Adesina, A. A., Iyelolu, T. V., & Paul, P. O. (2024). Leveraging predictive analytics for strategic decision-making: Enhancing business performance through data-driven insights. World Journal of Advanced Research and Reviews, 22(3), 1927-1934.
2. Korherr, P., Kanbach, D. K., Kraus, S., & Jones, P. (2023). The role of management in fostering analytics: The shift from intuition to analytics-based decision-making. Journal of Decision Systems, 32(3), 600-616.
3. Sadler-Smith, E., & Shefy, E. (2004). The intuitive executive: Understanding and applying 'gut feel' in decision-making. Academy of Management Perspectives, 18(4), 76-91.

Title 8

Modelling Market Responsiveness through Predictive Analytics Capabilities: A Structural Equation Modelling Approach

Methodology

Quantitative - Survey-Based, SEM

Description

This research empirically investigates the relationship between a firm's predictive analytics maturity and its ability to respond to market shifts. It uses SEM to test a model connecting data infrastructure, analytics competencies, organisational agility, and market responsiveness based on survey data collected from mid-to-senior level managers across multiple sectors.

Key References:

1. Olayinka, O. H. (2021). Big data integration and real-time analytics for enhancing operational efficiency and market responsiveness. Int J Sci Res Arch, 4(1), 280-96.
2. Hossain, M. A., Akter, S., Yanamandram, V., & Wamba, S. F. (2023). Data-driven market effectiveness: The role of a sustained customer analytics capability in business operations. Technological Forecasting and Social Change, 194, 122745.
3. Lisbet, Z. T., Al Faroqi, F., Evelyna, F., & Prihadi, D. J. (2024). The Nexus Between Data-Driven Decision-Making, Market Responsiveness, and Strategic Alliances in Boosting Business Growth of Start-Up Companies. International Journal of Business, Law, and Education, 5(2), 2470-2482.

Title 9

From Prediction to Action: A Mixed-Methods Inquiry into the Strategic Use of Predictive Market Analytics in Fast-Moving Industries

Methodology

Mixed Methods - Quantitative Survey + Qualitative Focus Groups

Description

Combining a survey of industry professionals (to quantify adoption factors and perceived outcomes) with focus group discussions (to explore contextual insights), this research aims to understand how predictive analytics not only forecasts market trends but also informs actionable strategic decisions in rapidly changing sectors like retail, telecom, and FMCG.

Key References:

1. Sazu, M. H., & Jahan, S. A. (2022). The impact of big data analytics on supply chain management practices in fast moving consumer goods industry: evidence from developing countries. International Journal of Business Reflections, 3(1).
2. Adewale, T. T., Eyo-Udo, N. L., Toromade, A. S., & Ngochindo, A. (2024). Optimising food and FMCG supply chains: A dual approach leveraging behavioural finance insights and big data analytics for strategic decision-making. Comprehensive Research and Reviews Journal, 2(1).
3. Oyedokun, O., Ewim, S. E., & Oyeyemi, O. P. (2024). Leveraging advanced financial analytics for predictive risk management and strategic decision-making in global markets. Global Journal of Research in Multidisciplinary Studies, 2(02), 016-026.

Title 10

Exploring Data-Driven Culture: A Case-Based Inquiry into Operational Optimisation Practices in Lean Manufacturing Firms

Methodology

Qualitative - Case-Based, In-Depth Interviews and Document Analysis

Description

This study explores how a data-driven culture is cultivated and embedded in operational decision-making across multiple lean manufacturing firms. Using semi-structured interviews and internal documentation, it investigates managerial perspectives and organisational behaviour toward analytics adoption. Insights will aid leaders in fostering sustainable optimisation habits across operations.

Key References:

1. Huang, J., Irfan, M., Fatima, S. S., & Shahid, R. M. (2023). The role of lean six sigma in driving sustainable manufacturing practices: an analysis of the relationship between lean six sigma principles, data-driven decision making, and environmental performance. Frontiers in Environmental Science, 11, 1184488.
2. Chatterjee, S., Chaudhuri, R., & Vrontis, D. (2024). Does data-driven culture impact innovation and performance of a firm? An empirical examination. Annals of Operations Research, 333(2), 601-626.
3. Tripathi, V., Chattopadhyaya, S., Mukhopadhyay, A. K., Saraswat, S., Sharma, S., Li, C., & Rajkumar, S. (2022). Development of a data‐driven decision‐making system using lean and smart manufacturing concept in industry 4.0: a case study. Mathematical Problems in Engineering, 2022(1), 3012215.

Title 11

Modelling the Impact of Real-Time Analytics Adoption on Operational Agility and Performance: Evidence from Mid-Sized Manufacturing Enterprises

Methodology

Quantitative - Survey-Based with SEM

Description

This research uses Structural Equation Modelling to examine the relationship between real-time analytics adoption and operational agility, and its subsequent effect on performance outcomes. Data will be collected through structured questionnaires from middle managers across manufacturing firms. The study quantifies the enablers and barriers to analytics-led optimisation.

Key References:

1. Feng, C., & Ali, D. A. (2024). Leveraging digital transformation and ERP for enhanced operational efficiency in manufacturing enterprises. Journal of Law and Sustainable Development, 12(3), e2455-e2455.
2. Tulli, S. K. C. (2024). Leveraging Oracle NetSuite to Enhance Supply Chain Optimisation in Manufacturing. International Journal of Acta Informatica, 3(1), 59-75.
3. Osho, G. O., Omisola, J. O., & Shiyanbola, J. O. An Integrated AI-Power BI Model for Real-Time Supply Chain Visibility and Forecasting: A Data-Intelligence Approach to Operational Excellence.

Title 12

Bridging Analytics Maturity and Operational Excellence: A Mixed-Methods Study on Strategy Execution in Logistics Firms

Methodology

Mixed Methods - Survey with SEM, Focus Groups

Description

This study first quantifies the influence of analytics maturity on key optimisation KPIs via SEM. It then integrates insights from focus group discussions with operations managers to interpret the strategic and contextual drivers behind the quantitative findings. The goal is to present a holistic framework for executing analytics-enabled optimisation strategies.

Key References:

1. Henriquez-Machado, R., Muñoz-Villamizar, A., & Santos, J. (2024). Roadmap to enhance operational excellence in emerging countries. Heliyon, 10(10).
2. Ozbiltekin-Pala, M., Kazancoglu, Y., Kumar, A., Garza-Reyes, J. A., & Luthra, S. (2024). Analysing critical factors of strategic alignment between operational excellence and Industry 4.0 technologies in smart manufacturing. The TQM Journal, 36(1), 161-177.
3. Attah, R. U., Garba, B. M. P., Gil-Ozoudeh, I., & Iwuanyanwu, O. (2024). Strategic frameworks for digital transformation across logistics and energy sectors: Bridging technology with business strategy. Open Access Res J Sci Technol, 12(02), 070-80.