A Study of the impact of the adoption of Robotic Process Automation (RPA) on work productivity in the retail banking industry

Thesis(Master) -- KDI School: Master of Public Policy, 2020Automation is not a new phenomenon. The automation of activities have proven to be pivotal in productivity growth not only at the individual level, but at the business level and achieved the economies of scale. One of the emerging technologies that has had a significant impact in the financial services industry is the adoption of Robotic Process Automation (RPA). IBS Intelligence (2019)’s report acknowledged that the RPA technology deploys “software robots to automate repetitive, rule-based, and high-volume tasks, has helped financial institutions in the phase of digital transformation”. This research attempts to study the impact of RPA adoption in the South Korean retail banking industry in relation to work productivity through a quantitative analysis. Specifically, the study takes the attributes from the IT innovation theories to observe the front office bank employees’ behavior with the adoption of a new technology like RPA is introduced. Data sources included analysis of financial reports of the major banks in South Korea and business journals. Then, data were collected from 62 front-office bank employees working at the two of the top five retail banks in South Korea with experiences of reassigning tasks to RPA bots.ABSTRACT ACKNOWLEDGEMENTS Chapter 1: Introduction Chapter 2: Literature Review Chapter 3: Research Model Chapter 4: Discussion Chapter 5: Conclusion References AppendixmasterpublishedEura K

Essays on Optimal Control of Dynamic Systems with Learning

<p>This dissertation studies the optimal control of two different dynamic systems with learning: (i) diagnostic service systems, and (ii) green incentive policy design. In both cases, analytical models have been developed to improve our understanding of the system, and managerial insights are gained on its optimal management.</p><p>We first consider a diagnostic service system in a queueing framework, where the service is in the form of sequential hypothesis testing. The agent should dynamically weigh the benefit of performing an additional test on the current task to improve the accuracy of her judgment against the incurred delay cost for the accumulated workload. We analyze the accuracy/congestion tradeoff in this setting and fully characterize the structure of the optimal policy. Further, we allow for admission control (dismissing tasks from the queue without processing) in the system, and derive its implications on the structure of the optimal policy and system's performance.</p><p>We then study Feed-in-Tariff (FIT) policies, which are incentive mechanisms by governments to promote renewable energy technologies. We focus on two key network externalities that govern the evolution of a new technology in the market over time: (i) technological learning, and (ii) social learning. By developing an intertemporal model that captures these dynamics, we investigate how lawmakers should leverage on such effects to make FIT policies more efficient. We contrast our findings against the current practice of FIT-implementing jurisdictions, and also determine how the FIT regimes should depend on specific technology and market characteristics.</p>Dissertatio