## A Systematic Approach to Assessing FinTech Adoption, HR Competency Potential, Service Innovation, and Firm Growth in the Banking Sector### IntroductionThe banking industry is undergoing a digital transformation driven by technological advancements, innovative service models, and evolving workforce capabilities. FinTech (Financial Technology) adoption, Human Resource (HR) competency development, service innovation, and firm growth are critical factors shaping the future of banking. However, evaluating the impact of these factors and selecting the most suitable alternatives can be challenging due to their multifaceted nature and complex interrelationships. To address this challenge, we propose a hybrid Multi-Criteria Decision-Making (MCDM) approach that integrates the Entropy-Weighted Method (EWM) for criterion weight determination and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for alternative ranking.### Methodology**Entropy-Weighted Method (EWM)**The EWM is a statistical technique that measures the relative importance of criteria based on the amount of information they provide. It assigns weights to criteria by calculating their entropy, which represents the uncertainty or randomness associated with the data. A lower entropy indicates a higher weight, reflecting the criterion's significance in the decision-making process.**Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)**The TOPSIS approach evaluates alternatives based on their closeness to an ideal solution and distance from a worst solution. It calculates a performance score for each alternative, which is used to rank them. The alternative with the highest performance score is considered the most desirable option.## Application of the Hybrid MCDM ApproachThe proposed hybrid MCDM approach was applied to evaluate and rank eight potential alternatives (A1 to A8) in the banking sector. Eleven interrelated criteria were identified through a comprehensive literature review, encapsulating aspects of FinTech adoption, HR competency potential, service innovation, and firm growth.**Evaluation Process**1. **Data Collection:** Data on the eleven criteria for each alternative was collected from industry experts and available literature.2. **Entropy Weight Calculation:** The EWM was employed to compute the weights of the criteria based on the collected data.3. **Normalization:** The decision matrix was normalized to ensure comparability between different criteria.4. **Weighted Normalized Matrix:** The normalized values were multiplied by the corresponding criteria weights.5. **Ideal and Worst Solutions:** The ideal best and worst solutions were determined for each criterion.6. **Performance Score Calculation:** The TOPSIS method was used to calculate the performance scores of each alternative based on their distance from the ideal and worst solutions.7. **Ranking:** Finally, the alternatives were ranked according to their performance scores.## ResultsThe application of the hybrid MCDM approach yielded the following results:* **Ranking of Alternatives:** Alternative A6 emerged as the top-performing alternative, indicating its strong performance across the considered criteria. Other alternatives were ranked in descending order of their performance scores.* **Key Criteria:** FinTech innovation, cost optimization, cybersecurity, and customer satisfaction emerged as the most influential criteria in determining the performance of alternatives in the banking sector.## DiscussionThe identified criteria and ranked alternatives provide valuable insights into the key factors that contribute to successful FinTech adoption, HR competency development, service innovation, and firm growth in the banking industry. The top-performing alternative, A6, represents a comprehensive approach that leverages technological advancements, innovation, and strategic HR practices to drive business success.The findings of this study highlight the importance of a holistic approach to evaluating and selecting alternatives in the banking sector. By considering multiple criteria and utilizing robust MCDM techniques, banks can make informed decisions that align with their specific goals and objectives.## ConclusionThe proposed hybrid MCDM approach provides a structured and data-driven framework for assessing the intersection of FinTech adoption, HR competency potential, service innovation, and firm growth in the banking sector. By leveraging the EWM for criterion weight determination and the TOPSIS for alternative ranking, the approach enables banks to prioritize and select the most suitable options that contribute to their growth and transformation strategies.