金融技术的采用,人力资源能力潜力,服务创新和银行部门使用Entropy和TOPSIS的稳定增长的交叉点
Habib Ullah Khan1, Muhammad Abbas2, Shah Nazir3
1Department of Accounting & Information Systems at the College of Business and Economics, Qatar University, Qatar.
PloS one
|January 14, 2025
概括
本研究介绍了一种混合多标准决策 (MCDM) 模型,该模型结合了重法 (EWM) 和以类似于理想解决方案的订单偏好技术 (TOPSIS),以评估金融技术的采用,人力资源能力,服务创新和银行业的公司增长. 该模型有效地对替代品进行排名,指导在不断变化的金融部门的战略决策.
科学领域:
- 银行业发展 银行业发展
- 金融技术 (金融科技) 采用 采用
- 人力资源 (HR) 能力提升能力提升
背景情况:
- 银行业的增长受到金融技术的采用,人力资源能力,服务创新和公司增长的重大影响.
- 因素之间的复杂,高维度的相关性在获得可靠的评估结果方面带来了挑战.
- 在这种充满活力的环境中,需要一个结构化的方法来系统地评估和排名替代品.
研究的目的:
- 开发和应用混合多标准决策 (MCDM) 模型来评估银行业发展的关键因素.
- 通过使用集成的MCDM方法,根据定义的标准系统地评估和排名多种替代方案.
- 提供一个结构化的框架,为有关金融技术,人力资源,创新和银行业增长的知情决策提供支持.
主要方法:
- 整合输入权重方法 (EWM) 进行客观权重和顺序偏好技术通过相似的理想解决方案 (TOPSIS) 进行排名.
- 应用混合EWM-TOPSIS模型来分析八种潜在替代品 (A1-A8) 的11个多方面的特征.
- 系统评估和对替代品的排名,基于它们在银行业发展相关的关键标准上的表现.
主要成果:
- 拟议的混合MCDM方法有效地评估了金融技术采用,人力资源能力,服务创新和公司增长的复杂相互作用.
- 据证明,EWM-TOPSIS模型对评估和排名八个考虑的替代方案产生了重大影响.
- 该研究根据综合多标准分析确定了表现最佳的替代方案.
结论:
- 混合的EWM-TOPSIS模型为银行业的决策提供了一个结构化和有效的方法.
- 调查结果强调了金融技术,人力资源能力,服务创新和银行业发展中的坚定增长的关键作用.
- 这项研究为战略规划和优化现代银行业绩提供了宝贵的见解.
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