BUSINESS
Banking in a Pandemic: Navigating Resources and Capabilities
IndonesiaWed May 21 2025
The COVID-19 pandemic has shown how crucial it is for banks to make quick, informed decisions about their resources and capabilities. This is where a decision-making model comes in handy. It helps managers pick the right resources and capabilities in real-time, adapting as needed. However, the current dynamic capabilities framework has some issues. It struggles with context mismatch, inappropriate treatment, and strategy alignment. These problems are what researchers call "gaps. " They are the starting point for creating better decision-making models.
Researchers have developed a new decision-making model. This model is designed to help banks figure out their resources and capabilities in complex situations. The model is unique because it tackles the challenges of complex contexts head-on. The research process was thorough. It followed a method adapted from the International Society of Pharmacoeconomics and Outcomes Research-Society of Medical Decision Making. The study involved ten stages, using qualitative methods, case studies, and an abductive approach. The focus was on Indonesian State-Owned Banks.
The proposed model includes seven key managerial decisions. These are probe, sense, structuring, bundling, building, leverage, and reconfiguring. The model uses fuzzy preference judgments as inputs, deep learning analytics for processing, and success rate predictions as outputs. In theory, this research improves dynamic capabilities using the cynefin framework. In practice, it gives the board of directors a tool to make better decisions about resources and capabilities during complex environmental changes.
The cynefin framework is a sense-making tool. It helps organizations navigate complex situations. By integrating this framework, the model can handle the uncertainty and complexity that banks face today. Deep learning analytics adds another layer of sophistication. It allows the model to predict outcomes based on vast amounts of data. This predictive power is crucial in a fast-changing environment like banking.
The model's success rate predictions are a game-changer. They provide a clear, data-driven way to evaluate decisions. This is especially important in banking, where the stakes are high. The board of directors can use these predictions to make informed decisions. They can allocate resources more effectively and adapt to changes more quickly. This is not just about surviving the pandemic. It is about thriving in a complex, ever-changing world.
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questions
Are the gaps identified in the dynamic capabilities framework deliberately left unaddressed to maintain control?
How does the model address the potential biases that may arise from fuzzy preference judgments?
What evidence supports the effectiveness of the ISPOR-SMDM methodology in financial decision-making contexts?
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