A hybrid approach for identifying and prioritizing critical success factors in technology transfer projects (case study: diesel locomotive manufacturing)

Abstract

Purpose With the rapid advancement of technology, companies use new technologies to produce their products and services to maintain a competitive advantage. As companies alone cannot research and develop their technologies, they should use knowledge sources outside the organization that may exist throughout the world; hence, organizations need technology transfer. Because the success rate of technology transfer projects is low, the need to accurately assess and investigate the critical success factors of technology transfer projects is felt. In this regard, this study aims to identify and prioritize the critical success factors in technology transfer projects. Design/methodology/approach In this research, 56 critical success factor (CSF) were extracted from the context of the articles and were adjusted using experts’ opinions in different phases, as well as the fuzzy-Delphi approach. Finally, 15 factors were categorized in the form of steps of the technology transfer model: STAGE-GATE. In the next step, the set of criteria needed to prioritize CFSs was extracted from the literature and finalized with the help of the experts. Then, how each of the CSF influences the identified criteria was scored according to the organization’s export opinions. Finally, the priority of each key success factor was calculated using the additive ratio assessment (ARAS) method. Findings The results obtained for prioritization of the critical success factors show that experience in technology transfer in the transferee company, the existence of experienced technology transfer managers, sufficient organizational infrastructure and documenting project problems, achievements and experiences are four critical success factors of the technology transfer projects. Considering the long-term and short-term specific goals of the technology transfer process and the choice of technology in line with the company’s commercial strategy are also the critical success factors with the next priorities. Originality/value The combination of ARAS and step-wise weight assessment ratio analysis methods for identifying and prioritizing managerial decisions in the high-tech industries is a value of this research. Also, a combination of novel multi-attribute decision-making methods by the older framework of new product development is another contribution of this research.

Evaluation framework for facilitating the technology transfers of universities: Focusing on the perspective of technology donors

“…The ability of the organization to penetrate new markets [203,210,212] Level of sharing the goal Financial and resource availability [63,155,211,212] Establishment strategies of TT [63,154,155,203,210,212,213] Level of sharing the strategy…”

Assessing educational methods for tomorrow's supply chain leaders with the integration of skill development priorities: a fuzzy decision-making approach

“…SWARA and TOPSIS as MCDM approaches have been widely applied in various real-world problems such as green supplier selection (Mansory et al. , 2021), managerial decisions prioritising (Mohammadi et al. , 2021), performance evaluation of privatised ports (Gök-Kısa et al.…”

“…TOPSIS can maintain the same number of EMs alternatives and conflicting skill sets requirements regardless of the size of the decision-making problem (Singh et al, 2018). SWARA and TOPSIS as MCDM approaches have been widely applied in various real-world problems such as green supplier selection (Mansory et al, 2021), managerial decisions prioritising (Mohammadi et al, 2021), performance evaluation of privatised ports (G€ ok-Kısa et al, 2021), adoption barriers for the circular economy (Cui et al, 2021), outsourcing provider selection (Perçin, 2019), ranking of solutions for mitigating sustainable SC risks (Ansari et al, 2020) and modelling a credit score system (Roy and Shaw, 2022). After introducing the fuzzy concept in decision-making problems (Bellman and Zadeh, 1970), fuzzy SWARA-fuzzy TOPSIS have used to address uncertainty due to human judgment preferences, the DMs' psychological behaviours and the absence of information.…”

A Model to Transfer Technology and Technical Knowledge in Auto Parts Manufacturers

“…Capability to share knowledge (O'kane et al, 2015), (Koutsouris, 2018), (Sutopo et al, 2022) 27 Company's capability to provide innovation in process (Kalaitzandonakes et al, 2018), (Hill, 2018), (da Silva et al, 2021) 10 Adapting technology to organizational knowledge and applying existing experiences (Hill, 2018), (Klempin & Karp, 2018), (Mohammadi et al, 2021) 28…”

“…Existence of new mechanisms to use and absorb technology (Zhang et al, 2019), (Weckowska, 2015), (Shmeleva et al, 2021), (Mohammadi et al, 2021) 11 Company's understanding of opportunities to use new technologies (Alem & Broussard, 2018), (Park et al, 2018) 29 Company's capability to deliver superior innovations (Zhang et al, 2019), (Park et al, 2018) 12 Capability to fix problems, predictable and preventive repairs and breakdowns (Audretsch et al, 2016), (Kalaitzandonakes et al, 2018) 30 Compatibility of technology with industry's operational requirements (Koutsouris, 2018), (Weckowska, 2015), (Hill, 2018) 13 Independent capability to create technology (Del Giudice et al, 2017), (Kalaitzandonakes et al, 2018) 31 Return on investment (Audretsch et al, 2016), (Kalaitzandonakes et al, 2018) 14 Investigating how to segment the market (Weckowska, 2015), (Hill, 2018) (Hill, 2018) 33 Extent of cooperation with R&D centers (Koutsouris, 2018), (Hill, 2018), (Liu et al, 2021) 16…”