Title 1

Strategic Resilience: A Case-Based Inquiry into Adaptive Practices of Global Supply Chains During Disruptive Events

Methodology 

Case study, Qualitative research using semi-structured interviews and document analysis

Description 

This study investigates how multinational companies operating in volatile geopolitical and economic environments adapt their global supply chain strategies in response to disruptions such as pandemics, trade wars, and climate risks. Through in-depth interviews with supply chain leaders and comparative case analysis across different industries, the research aims to extract patterns of strategic resilience and adaptive practices that optimise long-term performance beyond cost-efficiency models.

Key References:

1. Ali, K., & Aslani, K. (2023). Supply Chain Resilience within the Automotive Industry-A single-case study on the importance of Supply Chain Resilience practices for an automotive MNC as a result of the COVID-19 pandemic and the Russia-Ukraine war.
2. Kettele, M., & Lechner, G. (2024). Assessing resilience measures in the Austrian dairy supply chain: a case-based simulation integrating strategic, tactical, and operational decisions. International Journal of Production Research, 1-34.
3. Dehghani Sadrabadi, M. H., Makui, A., Ghousi, R., & Jabbarzadeh, A. (2024). An integrated optimization model for planning supply chains' resilience and business continuity under interrelated disruptions: a case study. Kybernetes, 53(12), 5801-5842.

Title 2

Unpacking the Drivers of Digital Integration and Sustainability in Global Supply Chain Optimisation: A Structural Equation Modelling Approach

Methodology 

Quantitative, cross-sectional survey with Structural Equation Modelling (SEM)

Description 

This study develops and validates a model to examine how digital transformation (e.g., blockchain, IoT, AI), sustainability practices, and organisational agility influence global supply chain optimisation. Data will be collected through structured questionnaires from mid- to senior-level supply chain professionals across multiple countries. SEM will be used to test the interrelationships and mediating effects, providing insights for designing technology-driven, sustainable global operations.

Key References:

1. Khan, A., & Jalal, A. (2023). Supply Chain Optimization through Technology Integration: Riding the Digital Wave to Efficiency. Abbottabad University Journal of Business and Management Sciences, 1(01), 53-63.
2. Khan, I., Rehman, M. Z., & Khan, I. (2025). Optimising natural resource management and global supply chains through digital innovation. Technological Forecasting and Social Change, 217, 124187.
3. Scholz, J., De Meyer, A., Marques, A. S., Pinho, T. M., Boaventura-Cunha, J., Van Orshoven, J., ... & Nummila, K. (2018). Digital technologies for forest supply chain optimisation: existing solutions and future trends. Environmental Management, 62, 1108-1133.

Title 3

From Strategy to Execution: A Mixed-Methods Exploration of Integrated Optimisation Practices in Globally Dispersed Supply Chains

Methodology 

Sequential explanatory mixed methods - Quantitative phase followed by qualitative interviews

Description 

This research explores how strategic alignment, stakeholder collaboration, and operational flexibility contribute to optimising global supply chains. The study begins with a quantitative survey analysed using SEM to test relationships among constructs like strategic alignment, supplier trust, lead time reduction, and cost efficiency. The follow-up qualitative phase involves in-depth interviews to interpret and enrich the quantitative findings, especially concerning contextual variations across regions and sectors.

Key References:

1. Mirabelli, G., & Solina, V. (2022). Optimization strategies for the integrated management of perishable supply chains: A literature review. Journal of Industrial Engineering and Management (JIEM), 15(1), 58-91.
2. Rasheed, K., Saad, S., Ammad, S., & Bilal, M. (2023). Integrating sustainability management and lean practices for enhanced supply chain performance: exploring the role of process Optimisation in SMEs. Engineering Proceedings, 56(1), 154.
3. Bhutta, M. K. S., Alhawari, O. I., & Mohamed, Z. M. (2024). An integrated production--distribution optimisation model for multinational manufacturing corporations. International Journal of Management Science and Engineering Management, 19(1), 29-45.

Title 4

Modelling Logistics Resilience: Modelling the Influence of Digital Integration, Supplier Agility, and Risk Culture on Disruption Recovery in Manufacturing Supply Chains

Methodology 

Quantitative - Structural Equation Modelling (SEM)

Description 

This study investigates how digital logistics systems, supplier responsiveness, and internal organisational risk culture contribute to the resilience of logistics operations during supply chain disruptions. Using SEM, the research will test hypothesised relationships using survey data from logistics managers in the manufacturing sector. The study targets professionals dealing with real-time disruption scenarios (e.g., port congestion, raw material shortages) and aims to provide a validated resilience model.

Key References:

1. Holloway, S. (2025). Navigating Supply Chain Disruptions with Technology-Driven Agility and Coordination. Available at SSRN 5136960.
2. Singh, R. K., & Modgil, S. (2025). Adapting to disruption: the impact of agility, absorptive capacity and ambidexterity on supply chain resilience. International Journal of Productivity and Performance Management, 74(2), 637-658.
3. Loh, K. B., & Tan, C. L. (2024). Enhancing Supply Chain Resilience through Supply Chain Integration, Learning, Agility, and Digital Transformation. Global Business and Management Research, 16(3), 118-144.

Title 5

Beyond the Blueprint: A Case-Based Inquiry into Contextual Decision-Making in Last-Mile Logistics Across Emerging Markets

Methodology 

Qualitative - Multiple Case Study using semi-structured interviews

Description 

This research explores how logistics professionals adapt last-mile delivery strategies across different urban and rural contexts in emerging economies. By conducting in-depth interviews and focus group discussions with industry experts across logistics firms, the study uncovers patterns of improvisation, cultural adjustments, and technology adoption that influence logistics outcomes. The study emphasises context-driven decision-making, making it relevant for logistics leaders engaged in market expansion.

Key References:

1. Gutierrez-Franco, E., Mejia-Argueta, C., & Rabelo, L. (2021). Data-driven methodology to support long-lasting logistics and decision making for urban last-mile operations. Sustainability, 13(11), 6230.
2. Silva, V., Amaral, A., & Fontes, T. (2023). Towards sustainable last-mile logistics: A decision-making model for complex urban contexts. Sustainable Cities and Society, 96, 104665.
3. Risberg, A., & Jafari, H. (2022). Last mile practices in e-commerce: framework development and empirical analysis of Swedish firms. International Journal of Retail & Distribution Management, 50(8/9), 942-961.

Title 6

Strategic Resilience in Cross-Border Supply Chains: A Multiple Case Study of Adaptive Practices in Emerging Market SMEs

Methodology 

Qualitative - Multiple case study using semi-structured interviews and document analysis.

Description 

This study explores how small and medium-sized enterprises (SMEs) in emerging markets build strategic resilience in cross-border trade operations. By investigating firms in sectors like manufacturing, pharmaceuticals, and agri-tech that have successfully navigated trade disruptions (e.g., tariffs, logistics bottlenecks, regulatory shifts), the research identifies adaptive strategies and governance practices that enhance operational continuity. The findings will help formulate a grounded model of resilient cross-border supply chain management.

Key References:

1. Ali, I., Sadiddin, A., & Cattaneo, A. (2023). Risk and resilience in agri-food supply chain SMEs in the pandemic era: a cross-country study. International Journal of Logistics Research and Applications, 26(11), 1602-1620.
2. Banomyong, R., Bhusiri, N., Julagasigorn, P., & Varadejsatitwong, P. (2024). Assessing Supply Chain Resilience to Mitigate Disruption: The Focus on Cross-Border Suppliers. Logistics, 9(1), 1.
3. Annamalah, S., Aravindan, K. L., & Ahmed, S. (2024). Resilience in the Face of Uncertainty: Navigating Supply Chain Challenges Through Proactive Risk Surveillance and Mitigation Strategies among SMEs in ASEAN countries. F1000Research, 13, 1037.

Title 7

Modelling the Impact of Cross-Border Trade Facilitation on Operational Agility: Evidence from ASEAN-based Exporters

Methodology 

Quantitative - Structural Equation Modelling (SEM) using survey data.

Description 

This research investigates how specific trade facilitation components such as customs efficiency, digital trade documentation, and regional trade agreements, impact operational agility among exporters in the ASEAN region. Using SEM, the study tests relationships between trade facilitation measures, internal supply chain integration, and the ability to respond rapidly to market changes. The findings offer empirical insights for both policymakers and supply chain leaders aiming to enhance regional trade performance.

Key References:

1. Chen, L., & Intal, P. S. (2017). ASEAN foreign trade, investment, and integration in comparative perspective. ASEAN and member states: Transformation and integration, 13-33.
2. Ika Riswanti, P., Hardi, W., Valeria, P., Dedi Abdul, H., Muammad, F. A., Muhammad, S., ... & Dewi, S. Assessing Inclusivity vs. Exclusivity: Impacts on ASEAN Trade Facilitation and Human Security.
3. Buban, S., Narjoko, D. A., Shrestha, R., Ha, D. T. T., & Asia, E. (2023). Integrative Chapter. Follow-up ASEAN Seamless Trade Facilitation Indicators, 3.

Title 8

Cross-Border Trade Strategy Innovation in Post-Pandemic Global Supply Chains: An Explanatory Sequential Study

Methodology 

Mixed Methods - Explanatory Sequential Design (Quantitative survey followed by qualitative interviews).

Description 

This research examines how firms are innovating their cross-border trade strategies in response to post-pandemic disruptions and geopolitical tensions. A large-scale quantitative survey will first identify dominant strategic orientations (e.g., reshoring, multi-shoring, digital integration) and their performance outcomes. Based on survey results, in-depth interviews will be conducted with key decision-makers to unpack the rationale and contextual drivers behind these choices. The mixed-methods approach ensures both statistical generalisability and deep contextual understanding of evolving global trade dynamics.

Key References:

1. Contractor, F. J. (2021). The world economy will need even more globalisation in the post-pandemic 2021 decade. Journal of International Business Studies, 53(1), 156.
2. Ghansah, F. A., & Lu, W. (2025). Managerial framework for quality assurance of cross-border construction logistics and supply chain during pandemic and post-pandemic: lessons from COVID-19 in the world's factory. Engineering, Construction and Architectural Management, 32(5), 3007-3056.
3. Zhao, L. (2023). Global value chains in the post-pandemic era. In Modern China and International Rules: Reconstruction and Innovation (pp. 13-40). Singapore: Springer Nature Singapore.

Title 9

The Behavioural Drivers of Compliance in Cross-Border Trade Operations: A Supply Chain Governance Perspective

Methodology 

Mixed Methods – Explanatory Sequential Design

Description 

This research investigates the behavioral, organizational, and regulatory factors influencing compliance practices in cross-border trade, from the lens of supply chain governance. In the first phase, a quantitative survey will be administered to professionals involved in logistics, customs management, and trade compliance to assess key behavioral drivers such as risk perception, institutional trust, procedural clarity, and ethical climate using SEM. In the second phase, qualitative interviews will be conducted with selected participants to explore context-specific insights, governance challenges, and the interplay between compliance behavior and organizational culture. This mixed-methods approach provides both statistical validation and contextual understanding, aiding firms in designing better compliance strategies within global supply networks.

Key References:

1. Maruf, I. R., Budiman, M., & Rastuti, T. (2024, February). Legal Compliance for Cross-Border Supply Chain: Challenge and Measurements. In International Seminar on Border Region (INTSOB 2023) (pp. 141-152). Atlantis Press.
2. Hameri, A. P., & Hintsa, J. (2009). Assessing the drivers of change for cross‐border supply chains. International Journal of Physical Distribution & Logistics Management, 39(9), 741-761.
3. Gao, C. (2024). Research on Optimization Strategies for Closed-Loop Supply Chain Management Based on Deep Learning Technology. International Journal of Information Systems and Supply Chain Management (IJISSCM), 17(1), 1-22.

Title 10

Hidden Drivers of Operational Efficiency: A Case-Based Exploration of Process Innovation in Multinational Logistics Firms

Methodology 

Multiple Case Study using Semi-Structured Interviews and Thematic Analysis

Description 

This research explores the less-documented, tacit factors contributing to operational efficiency in logistics divisions of multinational corporations. Using a multiple case study approach, it draws on semi-structured interviews with operations managers to understand how cultural, leadership, and localised process innovations influence efficiency across international operations. Insights from this study can guide context-driven operational strategies in global settings.

Key References:

1. Bayraktar, E., Eryarsoy, E., Kosanoglu, F., Acar, M. F., & Zaim, S. (2024). Unveiling the drivers of global logistics efficiency: insights from cross-country analysis. Sustainability, 16(7), 2683.
2. Bhattacharya, A., & David, D. A. (2018). An empirical assessment of the operational performance through internal benchmarking: a case of a global logistics firm. Production Planning & Control, 29(7), 614-631.