DOI QR코드

DOI QR Code

A Study on Agile Transformation in the New Digital Age

  • 투고 : 2019.12.31
  • 심사 : 2020.02.02
  • 발행 : 2020.03.31

초록

In the face of recent digital and digital transformation, companies and industries are trying to be agile to adapt and respond to change. Agile paradigm is spreading beyond the boundaries of existing applications such as IT-related projects and software development. In this regard, this study, we analyzed the diffusion of agile paradigm by text mining abstracts of research papers from 2001 to 2019. In addition, we discussed agile transformation in the Fourth Industrial Revolution. Through this study, we confirmed that we are studying agile transformation in various fields such as business environment, corporate organizational culture, manufacturing industry, and supply chain. The results of this study will contribute to understanding the meaning and role of agile as a basic paradigm for digital transformation in the Fourth Industrial Revolution.

키워드

참고문헌

  1. Porter, M.E. and J.E. Heppelmann, How smart, connected products are transforming companies. Harvard business review, 2015. 93(10): p. 96-114.
  2. Lee, J., A Study on Research Trend Analysis and Topic Class Prediction of Digital Transformation using Text Mining. International journal of advanced smart convergence, 2019. 8(2): p. 183-190. doi:http://doi.org/10.7236/IJASC.2019.8.2.183
  3. Beck, K., et al., Manifesto for agile software development. 2001.
  4. Larman, C. and B. Vodde, Scaling lean & agile development. Organization, 2009. 230(11).
  5. Highsmith, J., Agile project management: creating innovative products. 2009: Pearson education.
  6. Olteanu, C.G., IT Agile Transformation. Academy of Economic Studies. Economy Informatics, 2018. 18(1): p. 23-31.
  7. Fuchs, C. and T. Hess, Becoming agile in the digital transformation: the process of a large-scale agile transformation. 2018.
  8. Sommer, A.F., Agile Transformation at LEGO Group: Implementing Agile methods in multiple departments changed not only processes but also employees' behavior and mindset. Research-Technology Management, 2019. 62(5): p. 20-29. doi:https://doi.org/10.1080/08956308.2019.1638486
  9. Paasivaara, M., et al., Large-scale agile transformation at Ericsson: a case study. Empirical Software Engineering, 2018. 23(5): p. 2550-2596. https://doi.org/10.1007/s10664-017-9555-8
  10. Mahadevan, D., P. Jacobs, and B. Schlatmann, ING's agile transformation. McKinsey Quarterly, 2017. 1: p. 1-10.
  11. Kim, S., et al. Our journey to becoming agile: experiences with agile transformation in Samsung electronics. in 2016 23rd Asia-Pacific Software Engineering Conference (APSEC). 2016. IEEE. doi:https://doi.org/10.1109/APSEC.2016.064
  12. Power, K. Sensemaking and Complexity in Large-Scale Lean-Agile Transformation: A Case Study from Cisco. in 2016 49th Hawaii International Conference on System Sciences (HICSS). 2016. IEEE. doi:https://doi.org/10.1109/HICSS.2016.669
  13. Blei, D.M., Probabilistic topic models. Communications of the ACM, 2012. 55(4): p. 77-84. doi:http://doi.org/10.1145/2133806.2133826
  14. Lee, J.Y., Deep Learning Research Trend Analysis using Text Mining. International Journal of Advanced Culture Technology (IJACT). 7(4): p. 295-301. doi:https://doi.org/10.17703/IJACT2019.7.4.295
  15. Blei, D.M., A.Y. Ng, and M.I. Jordan, Latent dirichlet allocation. Journal of machine Learning research, 2003. 3(Jan): p. 993-1022.
  16. Newman, D., S. Karimi, and L. Cavedon. External evaluation of topic models. in in Australasian Doc. Comp. Symp., 2009. 2009. Citeseer.
  17. Bastani, K., H. Namavari, and J. Shaffer, Latent Dirichlet allocation (LDA) for topic modeling of the CFPB consumer complaints. Expert Systems with Applications, 2019. 127: p. 256-271. doi:http://doi.org/10.1016/j.eswa.2019.03.001