IMPLEMENTATION OF SMART TECHNOLOGY, ARTIFICIAL INTELLIGENCE, ROBOTICS, AND ALGORITHMS (STARA): A THREAT OR OPPORTUNITY FOR WORKERS 'FUTURE - Lestari, Lela; Djastuti, Indi (Review of Management and Entrepreneurship, 2020)
This qualitative phenomenological study aims to examine and compare the perceptions of workers in the banking sector regarding the implementation of STARA in their work. This study was conducted through in-depth interviews with informants about perceptions regarding optimizing STARA implementation in the banking sector, in the form of banking digitalization in the current Industrial Revolution 4.0 era. The informants in this study were bank employees such as the teller, customer service, and back-office personnel. Sampling was done using non-random sampling method with purposive sampling technique which amounted to 12 informants, consisting of four tellers, four customer service officers, and four back-office personnel. The results of the study showed that the majority of bank employees in frontliner positions, namely tellers, felt that their position could be replaced by the implementation of STARA. However, some informants in the frontliner position, namely customer service, stated that they did not feel threatened, because they believed there was a certain comfort when customers were served by humans rather than by machines. Meanwhile, bank employees in the back-office position have not felt any threat due to the implementation of STARA because they feel that the analysis process cannot be fully carried out properly by a system and still requires humans. In addition, employees in the back-office position stated that the implementation of STARA makes it easy for them to complete their work and can be used as an opportunity to achieve work performance. The results of this study also show that overall employees have not felt a decrease in commitment to the organization, obstacles in career advancement, desire to move, cynicism, or depression when working with the implementation of STARA, which is predicted to take their jobs.