Asia-Pacific Journal of Business Venturing and Entrepreneurship (벤처창업연구)

The Korean Society of Business Venturing (한국벤처창업학회)
권호 표지

A Method for Selecting AI Innovation Projects in the Enterprise: Case Study of HR part

기업의 혁신 프로젝트 선정을 위한 모폴로지-AHP-TOPSIS 모형: HR 분야 사례 연구

  • Chung Doohee (Handong Globaluniversity, AI Convergence Entrepreneurship) ;
  • Lee Jaeyun (Handong Global University, AI Convergence Entrepreneurship) ;
  • Kim Taehee (Handong Global University, AI Convergence Entrepreneurship)
  • 정두희 (한동대학교 AI Convergence & Entrepreneurship 전공 ) ;
  • 이재윤 (한동대학교 AI Convergence & Entrepreneurship 전공 ) ;
  • 김태희 (한동대학교 AI Convergence & Entrepreneurship 전공 )
  • Received : 2023.07.05
  • Accepted : 2023.10.12
  • Published : 2023.10.31
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we proposed a methodology to effectively determine the selection and prioritization of new business and innovation projects using AI technology. AI technology is a technology that can upgrade the business of companies in various industries and increase the added value of the entire industry. However, there are various constraints and difficulties in the decision-making process of selecting and implementing AI projects in the enterprise. In this paper, we propose a new methodology for prioritizing AI projects using Morphology, AHP, and TOPSIS. The proposed methodology helps prioritize AI projects by simultaneously considering the technical feasibility of AI technology and real-world user requirements. In this study, we applied the proposal methodology to a real enterprise that wanted to prioritize multiple AI projects in the HR field and evaluated the results. The results confirm the practical applicability of the methodology and suggest ways to use it to help companies make decisions about AI projects. The significance of the methodology proposed in this study is that it is a framework for prioritizing multiple AI projects considered by a company in the most reasonable way by considering both business and technical factors at the same time.

본 논문에서는 효과적으로 AI 프로젝트 및 신사업을 선정할 수 있는 방법론을 제안했다. AI 기술은 다양한 산업 분야에서 기업의 비즈니스를 고도화하고 산업 전체의 부가가치를 증대시킬 수 있는 기술이다. 기업가정신 연구 분야에서도 AI 기술은 중요한 소재가 되고 있다. 기업들은 AI 기술을 이용해 새로운 비즈니스를 창업하거나 기존 기업 내에서 신사업을 추진하고 혁신을 추진한다. 그러나 기업에서 AI 프로젝트를 선정하고 추진하는 의사결정 과정에서는 다양한 제약사항과 어려움이 존재한다. 본 논문에서는 모폴로지(Morphology)와 AHP 및 TOPSIS 결합 모형을 통한 AI 프로젝트 선정의 새로운 방법론을 제안한다. 제안 방법론은 AI 기술의 기술적 타당성과 현업의 사용자 요구조건을 동시에 고려하여 AI 프로젝트를 선정할 수 있도록 도와준다. 이 연구에서는 HR 분야의 다수 AI 프로젝트를 결정하고자 하는 실제 기업에 제안 방법론을 적용하고 그 결과를 평가했다. 이를 통해 방법론의 현실 적용 가능성을 확인하였으며, 기업의 AI 프로젝트 관련 의사결정에 유용하게 활용하기 위한 방법을 제시했다. 이 연구에서 제안하는 방법론은 사내 기업가정신(Intrapreneurship) 효과를 증진시키는 차원에서, 기업이 고려하는 여러 AI 프로젝트에 대하여 합리적인 방법으로 선정에 대한 의사결정의 프레임워크를 제시한다는 점에서 의미가 크다.

Keywords

References

  1. Abduljabbar, R., Dia, H., Liyanage, S., & Bagloee, S. A.(2019). Applications of artificial intelligence in transport: An overview. Sustainability, 11(1), 189.
  2. Abernathy, W. J., & Clark, K. B.(1985). Innovation: Mapping the winds of creative destruction. Research policy, 14(1), 3-22. https://doi.org/10.1016/0048-7333(85)90021-6
  3. Albayrak, E., & Erensal, Y. C.(2004). Using analytic hierarchy process (AHP) to improve human performance: An application of multiple criteria decision making problem. Journal of intelligent manufacturing, 15, 491-503. https://doi.org/10.1023/B:JIMS.0000034112.00652.4c
  4. Alnamrouti, A., Rjoub, H., & Ozgit, H.(2022). Do strategic human resources artificial intelligence help to make organisations more sustainable? evidence from non-governmental organisations. Sustainability, 14(12), 7327.
  5. Barrios, M. A. O., De Felice, F., Negrete, K. P., Romero, B. A., Arenas, A. Y., & Petrillo, A.(2016). An AHP-topsis integrated model for selecting the most appropriate tomography equipment. International Journal of Information Technology & Decision Making, 15(04), 861-885. https://doi.org/10.1142/S021962201640006X
  6. Brynjolfsson, E., & McAfee, A.(2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton & Company.
  7. Bughin, J., Seong, J., Manyika, J., Chui, M., & Joshi, R.(2018). Notes from the AI frontier: Modeling the impact of AI on the world economy. McKinsey Global Institute, 4.
  8. Chai, J., Liu, J. N., & Ngai, E. W.(2013). Application of decision-making techniques in supplier selection: A systematic review of literature. Expert systems with applications, 40(10), 3872-3885. https://doi.org/10.1016/j.eswa.2012.12.040
  9. Chidamber, S. R., & Kon, H. B.(1994). A research retrospective of innovation inception and success: the technology-push, demand-pull question. International Journal of Technology Management, 9(1), 94-112.
  10. Choudhury, P., Allen, R. T., & Endres, M. G.(2021). Machine learning for pattern discovery in management research. Strategic Management Journal, 42(1), 30-57. https://doi.org/10.1002/smj.3215
  11. Dagdeviren, M., Yavuz, S., & Kilinc, N.(2009). Weapon selection using the AHP and TOPSIS methods under fuzzy environment. Expert systems with applications, 36(4), 8143-8151. https://doi.org/10.1016/j.eswa.2008.10.016
  12. Davenport, T. H., & Kirby, J.(2015). Beyond automation. Harvard Business Review, 93(6), 58-65.
  13. Deloitte(2023). Summer 2023 Fortune/Deloitte CEO Survey. Retrieved (2023.10.18) from [https://www2.deloitte.com/us/en/pages/chief-executive-officer/articles/ceo-survey.html].
  14. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N.(2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929.
  15. Fountaine, T., McCarthy, B., & Saleh, T.(2019). Building the AI-powered organization. Harvard Business Review, 97(4), 50-59.
  16. Geum, Y., Jeon, H., & Lee, H.(2016). Developing new smart services using integrated morphological analysis: integration of the market-pull and technology-push approach. Service Business, 10, 531-555. https://doi.org/10.1007/s11628-015-0281-2
  17. Gunasekaran, A., & Ngai, E. W.(2004). Information systems in supply chain integration and management. European journal of operational research, 159(2), 269-295. https://doi.org/10.1016/j.ejor.2003.08.016
  18. Hajduk, S.(2021). Multi-criteria analysis in the decision-making approach for the linear ordering of urban transport based on TOPSIS technique. Energies, 15(1), 274.
  19. Hajduk, S., & Jelonek, D.(2021). A decision-making approach based on TOPSIS method for ranking smart cities in the context of urban energy. Energies, 14(9), 2691.
  20. Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A. R., Jaitly, N., ... & Kingsbury, B.(2012). Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal processing magazine, 29(6), 82-97.
  21. Hwang, C. L., Lai, Y. J., & Liu, T. Y.(1993). A new approach for multiple objective decision making. Computers & operations research, 20(8), 889-899. https://doi.org/10.1016/0305-0548(93)90109-V
  22. Hwang, C. L., Yoon, K., Hwang, C. L., & Yoon, K.(1981). Methods for multiple attribute decision making. Multiple attribute decision making: methods and applications a state-of-the-art survey, 58-191.
  23. Jain, V., Sangaiah, A. K., Sakhuja, S., Thoduka, N., & Aggarwal, R.(2018). Supplier selection using fuzzy AHP and TOPSIS: a case study in the Indian automotive industry. Neural computing and applications, 29, 555-564. https://doi.org/10.1007/s00521-016-2533-z
  24. Juell-Skielse, G., Balasuriya, P., Guner, E. O., & Han, S.(2022). Cognitive robotic process automation: Concept and impact on dynamic IT capabilities in public organizations. Cham: Springer International Publishing, (pp. 65-88).
  25. Jung, D. H(2021). Proposing an AI-based new product development methodology: An ambidexterity approach. Technology Innovation Research, 29(4), 161-196. https://doi.org/10.14386/SIME.2021.29.4.161
  26. Kudyba, S., & Diwan, R.(2002). Increasing returns to information technology. Information Systems Research, 13(1), 104-111. https://doi.org/10.1287/isre.13.1.104.98
  27. Kwon, H., Park, Y., & Geum, Y.(2018). Toward data-driven idea generation: Application of Wikipedia to morphological analysis. Technological Forecasting and Social Change, 132, 56-80. https://doi.org/10.1016/j.techfore.2018.01.009
  28. Lee, S. A., & Jeang, T. H.(2023). Analyzing and predicting venture investment in generative AI startups: Focusing on the United States and South Korea. Venture Capital Research, 18(4), 21-35.
  29. Likert, R.(1932). A technique for the measurement of attitudes. Archives of psychology, 22 140, 55.
  30. Liu, H., Ke, W., Wei, K. K., & Hua, Z.(2013). The impact of IT capabilities on firm performance: The mediating roles of absorptive capacity and supply chain agility. Decision support systems, 54(3), 1452-1462. https://doi.org/10.1016/j.dss.2012.12.016
  31. Mandal, P., & Gunasekaran, A.(2003). Issues in implementing ERP: A case study. European Journal of Operational Research, 146(2), 274-283. https://doi.org/10.1016/S0377-2217(02)00549-0
  32. Mardani, A., Jusoh, A., Nor, K., Khalifah, Z., Zakwan, N., & Valipour, A.(2015). Multiple criteria decision-making techniques and their applications-a review of the literature from 2000 to 2014. Economic research-Ekonomska istrazivanja, 28(1), 516-571. https://doi.org/10.1080/1331677X.2015.1075139
  33. Mitchell, M.(2021). Why AI is harder than we think. arXiv preprint arXiv:2104.12871.
  34. Munakata, T.(1994). Commercial and industrial AI. Communications of the ACM, 37(3), 23-26. https://doi.org/10.1145/175247.175248
  35. Olson, D. L.(2004). Comparison of weights in TOPSIS models, Mathematical and Computer Modelling. 40(7-8), 721-727. https://doi.org/10.1016/j.mcm.2004.10.003
  36. Parkan, C., & Wu, M. L.(1997). On the equivalence of operational performance measurement and multiple attribute decision making. International Journal of Production Research, 35(11), 2963-2988. https://doi.org/10.1080/002075497194246
  37. Purcell, A. T., & Gero, J. S.(1996). Design and other types of fixation. Design studies, 17(4), 363-383. https://doi.org/10.1016/S0142-694X(96)00023-3
  38. Raju, S. S., Murali, G. B., & Patnaik, P. K.(2020). Ranking of Al-CSA composite by MCDM approach using AHP-TOPSIS and MOORA methods. Journal of Reinforced Plastics and Composites, 39(19-20), 721-732. https://doi.org/10.1177/0731684420924833
  39. Rammer, C., Fernandez, G. P., & Czarnitzki, D.(2022). Artificial intelligence and industrial innovation: Evidence from German firm-level data. Research Policy, 51(7), 104555.
  40. Roy, R., & Sarkar, M. B.(2016). Knowledge, firm boundaries, and innovation: Mitigating the incumbent's curse during radical technological change. Strategic Management Journal, 37(5), 835-854.
  41. Saaty, T. L.(1980). The Analytic Hierarchy Process Mcgraw Hill, New York. Agricultural Economics Review, 70. 34.
  42. Saaty, T. L.(1990). How to make a decision: the analytic hierarchy process. European journal of operational research, 48(1), 9-26. https://doi.org/10.1016/0377-2217(90)90057-I
  43. Syamsudin, S., & Rahim, R.(2017). Study Approach Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS). Int. J. Recent Trends Eng. Res, 3(3), 268-285.
  44. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., & Polosukhin, I.(2017). Attention is all you need. Advances in neural information processing systems, 30.
  45. Verganti, R., Vendraminelli, L., & Iansiti, M.(2020). Innovation and design in the age of artificial intelligence. Journal of Product Innovation Management, 37(3), 212-227. https://doi.org/10.1111/jpim.12523
  46. Wamba-Taguimdje, S. L., Fosso Wamba, S., Kala Kamdjoug, J. R., & Tchatchouang Wanko, C. E.(2020). Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. Business Process Management Journal, 26(7), 1893-1924. https://doi.org/10.1108/BPMJ-10-2019-0411
  47. Wei, C. C., Chien, C. F., & Wang, M. J. J.(2005). An AHP-based approach to ERP system selection. International journal of production economics, 96(1), 47-62. https://doi.org/10.1016/j.ijpe.2004.03.004
  48. Wissema, J. G.(1976). Morphological analysis: its application to a company TF investigation, Futures, 8(2), 146-153. https://doi.org/10.1016/0016-3287(76)90064-1
  49. Yoon, B., & Park, Y.(2005). A systematic approach for identifying technology opportunities: Keyword-based morphology analysis. Technological Forecasting and Social Change, 72(2), 145-160. https://doi.org/10.1016/j.techfore.2004.08.011
  50. Younus, A. M.(2022). The Effect of Artificial Intelligence on Job Performance in China's Small and Medium-Sized Enterprises (SMEs).
  51. Zahedi, F.(1986). The analytic hierarchy process-a survey of the method and its applications. interfaces, 16(4), 96-108. https://doi.org/10.1287/inte.16.4.96