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http://dx.doi.org/10.14400/JDC.2021.19.7.129

Innovation Resistance, Satisfaction and Performance: Case of Robotic Process Automation  

Yoon, Sungchul (Graduate School of Information, Yonsei University)
Roh, Jonggeuk (Management of Technology, Yonsei University)
Lee, Jungwoo (Graduate School of Information, Yonsei University)
Publication Information
Journal of Digital Convergence / v.19, no.7, 2021 , pp. 129-138 More about this Journal
Abstract
Many organizations are applying robotic process automation (RPA) to automate repetitive and rule based tasks to enhance the accuracy and efficiency of works. Some members are willing to join the projects hoping to eliminate annoying and meaningless tasks, but others are resisting this innovation fearing that they may lose their jobs. In this study, both positive and negative antecedents are posited to influence the performance in adopting RPA. The effects of relative advantage, compatibility, change management effect, innovation resistance and satisfaction, conclusively to performance improvement were examined via a survey of 109 employees involved in the 11 RPA projects in a manufacturing company, and the structural equation model analysis. The research considering the consumer characteristics of the innovation resistance model can be followed for the development of individualized change management strategy.
Keywords
Robotics Processing Automation; Relative Advantage; Compatibility; Change Management Effect; Innovation Resistance; Satisfaction; Performance Improvement;
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1 J. M. Choi. (2020). A study on the relationship between information technology investment and corporate performance of manufacturing companies: the difference according to the level of strategic use and environmental uncertainty. The Journal of Information Systems, 29(2), 1-26.
2 J. H. Jung. (2019). Note the solution of the 52-hour era, RPA-focused on the main considerations when introducing. POSRI Issue Report, 2019(2), 1-13.
3 N. Y. Kim. (2020). Robotic RPA revolutionizes office work. The Korea Economic Daily. https://www.hankyung.com/it/article/2020012947731
4 T. R. Han, K. H. Lee. (2019). Research on Financial Regulations Related RPA (Robotic Process Automation). The Journal of Bigdata, 4(2), 47-59.   DOI
5 Kaya, C. T., Turkyilmaz, M., & Birol, B. (2019). Impact of RPA technologies on accounting systems. Muhasebe ve Finansman Dergisi-Nisan, (82), 235-250. DOI:10.25095/mufad.536083   DOI
6 Kaplan, R. S., & Norton, D. P. (2005). The balanced scorecard: measures that drive performance. Harvard business review, 83(7), 71-79.
7 Ram, S. (1987). A model of innovation resistance. ACR North American Advances.
8 M. K. Kim & B. H. Park. (2019). Analysis on engineers' technology resistance and adoption factors for technology introduction. The Korea Society of Management Information Systems Conference (pp. 234-241), Seoul
9 IRPA&AI. (2017). What is Robotic Process Automation?. https://irpaai.com/what-is-robotic-process-automati on
10 Watson, J., & Wright, D. (2017). The robots are ready. Are you. Deloitte.
11 J. W. Leem, K. J. Jung, & T. D. Kang. (2019). A Study on RPA Adoption Cost Optimization for Aviation Service Industry. Journal of the Aviation Management Society of Korea, 17(6), 117-141. DOI:10.30529/amsok.2019. 17.6.006   DOI
12 Y. H. Lee, Hsieh, Y. C., & Hsu, C. N. (2011). Adding innovation diffusion theory to the technology acceptance model: Supporting employees' intentions to use e-learning systems. Educational Technology & Society, 14(4), 124-137
13 C. L. Hsu & J. C. C. Lin. (2020). Understanding continuance intention to use online to offline (O2O) apps. Electronic Markets, 30(4), 883-897. DOI:10.1007/s12525-019-00354-x   DOI
14 Delone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: a ten-year update. Journal of management information systems, 19(4), 9-30. DOI:10.1080/07421222.2003.11045748   DOI
15 Pitt, L. F., Watson, R. T., & Kavan, C. B. (1995). Service quality: a measure of information systems effectiveness. MIS quarterly, 173-187.
16 J. I. Kim. (2012). Reflective Indicator vs. Formative Indicator :Theoretical Discussion, Empirical Comparison, and Practical Usefulness, Korean Marketing Association, 27(4), 199-226.
17 Y. G. Hyun, J. Y. Lee. (2019). A study on the application of RPA(Robotics Process Automation) for productivity of business documents, Journal of Digital Convergence, 17(9), 199-212. DOI:10.14400/JDC.2019.17.9.199   DOI
18 S. W. Kang. (2018). Research on RPA solution introduction performance and success factors for Korean Exporting Firms. Korea Trade Research Association Conference (pp. 297-321), Seoul.
19 Schatsky, D., Muraskin, C., & Iyengar, K. (2016). Robotic process automation: A path to the cognitive enterprise. Deloitte University Press.
20 Rogers, E. M. & Shoemaker, F. F. (1971). Communication of Innovations; a cross-cultural approach.
21 M. H. Noh. (2004), An Analysis on Implementation Success and Performance of ERP System, Asia Pacific Journal of Samall Business 26(1), 3-26.
22 G. J. Kim, C. W. Shin, & K. P. Kim, (2007), The measurement of ERP System Performance Using BSC Evaluating Indicators, Journal of corporation and innovation, 121-143
23 Y. K. Hyun, & J. Y. Lee. (2018). Trends Analysis and Future Direction of Business Process Automation, RPA(Robotic Process Automation) in the Times of Convergence. Journal of Digital Convergence, 16(11), 313-327. DOI:10.14400/JDC.2018.16.11.313   DOI
24 S. I. Yang. (2009). A Study for the Effect for Work Innovation and Effective Change Management on Organizational Performance, Kyunghee University
25 Sheth, J. N. (1981). Pshcyhology of innovation resistance: The less developed concept (LDC) in diffusion research.
26 Seddon, P. B. (1997). A respecification and extension of the DeLone and McLean model of IS success. Information systems research, 8(3), 240-253. DOI:10.1287/isre.8.3.240   DOI
27 Y. K. Hyun, J. Y. Lee. (2018). Trends Analysis and Future Direction of Business Process Automation, RPA(Robotic Process Automation) in the Times of Convergence. Journal of Digital Convergence, 16(11), 313-327. DOI:10.14400/JDC.2018.16.11.313   DOI