Analysis Of Factors Influencing Knowledge Flow Using Multi-Criteria Decision-Making Techniques (The Case Of Study: Knowledge-Based Companies In Yazd Science And Technology Park)

Mirghafoori, Seyyed Habibollah; Rayatpoor, Mohamad Mohsen; Saffari Darberazi, Ali

doi:10.47176/smok.2025.1940

Journals List

Automatic welding with the help of a portable robot

Artificial intelligence application chain

Infrared sensors and countermeasures

news

Number of Journals	38
Number of Issues	1,418
Number of Articles	10,203
Article View	12,182,977
PDF Download	7,127,264

	Analysis Of Factors Influencing Knowledge Flow Using Multi-Criteria Decision-Making Techniques (The Case Of Study: Knowledge-Based Companies In Yazd Science And Technology Park)
مدیریت راهبردی دانش سازمانی
Volume 8, Issue 3 - Serial Number 30, September 2025, Pages 147-175 PDF (1.35 M)
Document Type: Case study
DOI: 10.47176/smok.2025.1940
Authors
Seyyed Habibollah Mirghafoori^* ¹; Mohamad mohsen Rayatpoor²; Ali Saffari Darberazi³
¹Associate Professor of Industrial Management, Faculty of Economics, Management, and Accounting, Yazd University, Yazd, Iran
²M.Sc. in Industrial Management, Faculty of Management and Accounting, University of Science and Arts, Yazd, Iran
³Assistant Professor, Department of Industrial Engineering, Faculty of Engineering, Bam Higher Education Complex, Bam, Iran
Abstract
Purpose: In the knowledge-based era, knowledge as an intangible asset plays a vital role in the success of organizations. Knowledge flow, which relies on information exchange and collaboration among individuals, facilitates the updating of knowledge and the advancement of new research. Knowledge-based companies, as key players in the knowledge-driven economy, require effective management of knowledge flow to maintain competitiveness and foster innovation. This study aims to identify and prioritize the factors influencing knowledge flow in knowledge-based companies located in the Yazd Science and Technology Park. Methodology: This research employs multi-criteria decision-making (MCDM) techniques, including SWARA, ARAS, and COCOSO. Initially, factors affecting knowledge flow were extracted through a comprehensive review of literature and prior research. Subsequently, questionnaires were designed and distributed to experts, including managers, employees of knowledge-based companies, and academic professionals. The statistical population of this study consisted of managers and employees of knowledge-based companies in the Yazd Science and Technology Park, as well as academic experts. The collected data were analyzed and ranked using the aforementioned techniques. Finally, the results were integrated using the rank averaging method. Findings: The results revealed that the most significant factors influencing knowledge flow in knowledge-based companies are management support and commitment (C8), continuous training (C4), IT infrastructure (E1), teamwork (A2), work ethic (A1), and support for teamwork (B5). These factors, in order of priority, play a key role in facilitating knowledge flow. Management support and commitment emerged as the most critical factor, highlighting the significant impact of leadership in fostering an organizational culture conducive to knowledge exchange. Continuous training and IT infrastructure were also identified as vital factors, enabling access to and updating of knowledge. Teamwork and work ethic, as human factors, enhance interaction and collaboration among employees.` Research limitations/implications: While this study employs an innovative combination of multi-criteria methods to comprehensively analyze knowledge flow factors, it has certain limitations, including its focus on companies in Yazd Science and Technology Park (which necessitates additional studies to generalize findings to other regions) and partial reliance on expert opinions (which may be subject to cognitive biases). Nevertheless, the findings can serve as a foundation for designing policy models in technology parks, developing training programs to enhance human factors affecting knowledge flow, and improving technological infrastructure in knowledge-based companies. Furthermore, the proposed hybrid methodology can provide a framework for future research in other knowledge management domains. Practical implications: The findings of this study can significantly assist managers of knowledge-based companies in developing operational strategies to enhance knowledge flow. Specifically, highlighting the crucial role of "management support and commitment" underscores the need for senior executives to foster an organizational culture conducive to knowledge sharing. Additionally, identifying key factors such as "continuous training" and "IT infrastructure" provides clear directions for future investments. The study recommends that policymakers in science and technology parks design specialized support programs to strengthen teamwork and develop knowledge infrastructure. On a broader scale, the proposed model can serve as a framework for evaluating the effectiveness of national-level initiatives aimed at developing knowledge-based ecosystems. Originality/value: This study offers unique scientific originality and value from multiple perspectives. Methodologically, the innovative integration of three multi-criteria decision-making techniques (SWARA, ARAS, and COCOSO) for analyzing knowledge flow factors presents a pioneering approach in knowledge management literature, enhancing result accuracy and reliability while enabling comprehensive findings comparison. The research's focus on knowledge-based companies in Yazd Science and Technology Park as a distinctive sample of Iran's innovation ecosystems addresses existing gaps in regional studies. The practical findings, particularly identifying "management support and commitment" as a key factor, not only emphasize leadership's vital role in shaping knowledge-oriented culture but also provide an operational framework for policymaking in other science and technology parks nationwide. Furthermore, the study bridges theory and practice through empirical evidence of simultaneous impacts from human factors (e.g., work ethics) and technological factors (e.g., IT infrastructure) on knowledge flow, transcending traditional boundaries in knowledge management research. Notably, this represents the first study simultaneously applying SWARA, ARAS, and COCOSO methods to analyze knowledge flow in Iranian knowledge-based companies, significantly enhancing its scientific value and innovation.
Keywords
Science and Technology Park; ARAS Technique; SWARA Technique; COCOsO Technique; Knowledge Flow; Rank Averaging Method; Knowledge-Based

References
Akbari, A., Nooshnfard, F., & Hriri, N. (2019). Evaluation of Barriers to Knowledge Flow Factors in Academic Libraries Based on Knowledge Management Maturity Model. Journal of Studies in Library and Information Science, 11(ُSpecial Issue: Knowledge Management), 67-88. https://doi.org/10.22055/slis.2018.24169.1422 (In Persian) AlMulhim, A. F. (2023). The impact of administrative management and information technology on e-government success: The mediating role of knowledge management practices. Cogent Business & Management, 10(1), 2202030. https://doi.org/10.1080/23311975.2023.2202030 Arab-Rahmatipour, M. , Mirhosseini, Z. and Saeedi, A. (2023). Investigating the relationship between knowledge sharing and science production from the viewpoints of faculty members of Knowledge and Information Science at Tehran's public universities. Quarterly Journal of Research and Planning in Higher Education, 27(1), 183-210. (In Persian) Bamel, N., Pereira, V., Bamel, U., & Cappiello, G. (2021). Knowledge management within a strategic alliances context: past, present and future. Journal of Knowledge Management, 25(7), 1782-1810. https://doi.org/10.1108/JKM-06-2020-0443 Barra, C., Castro-Peña, M. Y., & Coello, J. G. (2024). Research and knowledge transfer performance in Colombian universities. Quality & Quantity, 58(1), 77-96. https://doi.org/10.1007/s11135-023-01627-6 Ben-Ari, G., & Vonortas, N. S. (2007). Risk financing for knowledge-based enterprises: mechanisms and policy options. Science and Public Policy, 34(7), 475-488. https://doi.org/10.3152/030234207X244829 Berraies, S., & Chouiref, A. (2023). Exploring the effect of team climate on knowledge management in teams through teamwork engagement: Evidence from knowledge-intensive firms. Journal of Knowledge Management, 27(3), 842-869. https://doi.org/10.1108/JKM-09-2021-0720 Bhosale, V. A., & Kant, R. (2016). An integrated ISM fuzzy MICMAC approach for modelling the supply chain knowledge flow enablers. International Journal of Production Research, 54(24), 7374-7399. https://doi.org/10.1080/00207543.2016.1189102 Boateng, G. O., Neilands, T. B., Frongillo, E. A., Melgar-Quiñonez, H. R., & Young, S. L. (2018). Best practices for developing and validating scales for health, social, and behavioral research: a primer. Frontiers in public health, 6, 149. https://doi.org/10.3389/fpubh.2018.00149 Brewer, P. D., & Brewer, K. L. (2010). Knowledge management, human resource management, and higher education: A theoretical model. Journal of Education for Business, 85(6), 330-335. https://doi.org/10.1080/08832321003604938 Budur, T., Demirer, H., & Rashid, C. A. (2024). The effects of knowledge sharing on innovative behaviours of academicians; mediating effect of innovative organization culture and quality of work life. Journal of Applied Research in Higher Education, 16(2), 405-426. https://doi.org/10.1108/JARHE-08-2022-0257 Chen, K., Wupur, A., Abudouguli, A., & Yang, G. L. (2023). Measuring the knowledge transfer efficiency of social science in Chinese universities from a think tank perspective. Socio-Economic Planning Sciences, 90, 101745. https://doi.org/10.1016/j.seps.2023.101745 Chen, P., & Yu, P. (2024). Research on the impact of knowledge flow in the new energy vehicle industry alliance on technology commercialization. Environment, Development and Sustainability, 26(9), 23909-23935. https://doi.org/10.1007/s10668-023-03628-9 Cleveland, S., Mitkova, L., & Gonçalves, L. C. (2015, April). Knowledge flow in the open innovation model the effects of ICT capacities and open innovation practices on knowledge streams. In SoutheastCon 2015 (pp. 1-2). IEEE. https://doi.org/10.1109/SECON.2015.7133033 Das, A. K., Tonoy, M. T. A., & Hossain, M. (2024, September). Blockchain-Based Knowledge Repository for Training Artificial Intelligence Models: Bridging AIML with Decentralized Data. In 2024 IEEE Region 10 Symposium (TENSYMP) (pp. 1-6). IEEE. https://doi.org/10.1109/ACCESS.2023.3349019 Davenport, F., & Prosak, E. (1998). Investigating the effect of job attitude on knowledge sharing and its relationship with organizational citizenship behavior in Ghavamin Bank. Quarterly Journal of Development and Transformation Management, 14(2), 1-10. Duan, Y., Yang, M., Huang, L., Chin, T., Fiano, F., de Nuccio, E., & Zhou, L. (2022). Unveiling the impacts of explicit vs. tacit knowledge hiding on innovation quality: The moderating role of knowledge flow within a firm. Journal of Business Research, 139, 1489-1500. https://doi.org/10.1016/j.jbusres.2021.10.068 Fallahtafti, H., Saffaridarberazi, A., & Zare, F. (2023). Causal analysis of knowledge synergy in knowledge-based companies with the integrated approach of interpretive structural modeling and structural equations (Study case: Yazd Science and Technology Park). Strategic Management of Organizational Knowledge, 6(3), 49-80. https://doi.org/10.47176/smok.2023.1623 (In Persian) Fartash, K. (2024). Investigation on the governance of innovation and technology in science and technology parks of Iran. Quarterly Journal of Governance Knowledge, 2(3), 110-124. https://doi.org/10.22034/jokog.2024.201819 (In Persian) Ghayour Baghbani, S. M. , Yegane Mofrad, M. and HamedHamrahiyan, F. (2023). Investigating the impact of social capital components on innovation capability through tacit knowledge sharing. Innovation EconomicEcosystem Studies, 3(2), 61-80. https://doi.org/10.22111/innoeco.2023.44606.1050 (In Persian) Haghighi Boroujeni, P., Safdari Ranjbar, M., Karimi, T., & Jafari, S. M. (2025). Rethinking the institutional mapping of Iran's national innovation system; A new typology based on knowledge functions. Strategic Management of Organizational Knowledge, 8(2), 86-104. https://doi.org/10.47176/smok.2025.1859 (In Persian) Hassani Ahangar, M. R., Tavallaei, R., & Shadmanfar, M. H. (2023). Presenting the model of a wise core - a capable network for the role-playing of universities in the knowledge management of modern Islamic civilization issues based on imam khamenei's thought. Strategic Management of Organizational Knowledge, 6(4), 21-48. https://doi.org/10.47176/smok.2023.1659 Hassanzadeh, M., & Teymori Tabieh, M. (2015). Survey of Knowledge Flow in Knowledge based Organization of Science and Technology Park of the University of Tehran and Presentation of a Conceptual Mode. Knowledge Retrieval and Semantic Systems, 2(2), 23-40. https://doi.org/10.22054/jks.2015.1088 Hislop, D. (2003). Linking human resource management and knowledge management via commitment: A review and research agenda. Employee relations, 25(2), 182-202. https://doi.org/10.1108/01425450310456479 Huang, J. J., & Chin-Yi, C. (2025). Knowledge Flow Dynamics in Organizations: A Stochastic Multi-Scale Analysis of Learning Barriers. Mathematics, 13(2), 294. https://doi.org/10.3390/math13020294 Hussain, I., Mujtaba, G., Shaheen, I., Akram, S., & Arshad, A. (2022). An empirical investigation of knowledge management, organizational innovation, organizational learning, and organizational culture: Examining a moderated mediation model of social media technologies. Journal of Public Affairs, 22(3), e2575. https://doi.org/10.1002/pa.2575 Jafari, M., Akhavan, P., & Rafiei, A. (2014). Technology transfer effectiveness in knowledge-based centers providing a model based on knowledge management. International Journal of Scientific Knowledge, 4(7). Jensen, M. C., & Meckling, W. H. (2009). Specific and general knowledge, and organizational structure. In Knowledge management and organisational design (pp. 17-38). Routledge. Jozi, Z., Nourmohammadi, H., & Noroozi Chakoli, A. (2022). Survey of Knowledge Flow from Universities as Sources of Science and Technology Production: A Comparison Between Iranian Universities and the Top 100 Universities in the World Using Citation Measurement Between Articles and Patents. Scientometrics Research Journal, 8((Issue 2, Autumn & Winter)), 181-202. https://doi.org/10.22070/rsci.2021.13857.1475 (In Persian) Kapoor, T., Falconer, M., Hutchen, J., Westwood, A. R., Young, N., & Nguyen, V. M. (2023). Implementing and evaluating knowledge exchange: Insights from practitioners at the Canadian Forest Service. Environmental Science & Policy, 148, 103549. https://doi.org/10.1016/j.envsci.2023.07.009 Khademizadeh, S., Mohammadi, Z., Kohirostami, M., & Mehralizadeh, Y. (2024). Presenting the model of knowledge management in universities with a meta-synthesis approach. Strategic Management of Organizational Knowledge, 7(2), 75-106. https://doi.org/10.47176/smok.2024.1751 (In Persian) Khosravi, A., & Mohammadi sarab, N. (2024). Determining the product diversification strategy of insurance companies using product space theory and network science. Iranian Journal of Insurance Research, 13(3), 207-226. https://doi.org/10.22056/ijir.2024.03.02 (In Persian) Kianto, A., Sáenz, J., & Aramburu, N. (2017). Knowledge-based human resource management practices, intellectual capital and innovation. Journal of Business Research, 81, 11-20. https://doi.org/10.1016/j.jbusres.2017.07.018 Koolivand, A., Salehi, M., Arabzadeh, M., & Ghodrati, H. (2023). The relationship between knowledge-based economy and fraudulent financial reporting. Journal of Facilities Management, 21(1), 16-29. https://doi.org/10.1108/JFM-07-2021-0076 Liu, N., & Xu, Z. (2021). An overview of ARAS method: Theory development, application extension, and future challenge. International Journal of Intelligent Systems, 36(7), 3524-3565. https://doi.org/10.1002/int.22425 Mirghafoori, S. H., Aghazade Bafgh, M., & Saffari Darberazi, A. (2024). Designing a cognitive model of retaining knowledge workers in technological and knowledge-based companies of Yazd Science and Technology Park. Strategic Management of Organizational Knowledge, 7(3), 29-54. https://doi.org/10.47176/smok.2024.1779 (In Persian) Mohammad Esmaeil, S., & Hamidi, F. (2021). Knowledge Flow Model in Banking Industry. Strategic Management of Organizational Knowledge, 4(1), 123-153. https://doi.org/10.47176/smok.2021.1251 (In Persian) Mohammadi, M., Samizadeh, M., Pouya, S., & Arabahmadi, R. (2023). Examining the mediating effect of knowledge management on the relationship between organizational culture and organizational performance. Journal of Soft Computing and Decision Analytics, 1(1), 63-79. https://doi.org/10.31181/jscda1120235 Morris, T., Maillet, S., & Koffi, V. (2022). Financial knowledge, financial confidence and learning capacity on financial behavior: a Canadian study. Cogent Social Sciences, 8(1), 1996919. https://doi.org/10.1080/23311886.2021.1996919 Morshedi, A. , Nezafati, N. , Amirkhani, T. and Zolfiaghdam, Y. (2021). Dynamic Simulation of Motivational Policies Impacts on Employees Knowledge Sharing: Case of Bandar Imam Petrochemical Company. Management Research in Iran, 22(3), 29-48. https://doi.org/20.1001.1.2322200.1397.22.3.2.5 (In Persian) Muftahu, M., & Jamil, H. (2021). Sustainable knowledge flow and innovation in higher education: the implementation of change management in universities. International Journal of Innovation and Sustainable Development, 15(2), 159-168. https://doi.org/10.1504/IJISD.2021.114328 Munawar, S., Yousaf, H. Q., Ahmed, M., & Rehman, S. (2022). Effects of green human resource management on green innovation through green human capital, environmental knowledge, and managerial environmental concern. Journal of Hospitality and Tourism Management, 52, 141-150. https://doi.org/10.1016/j.jhtm.2022.06.009 Nonaka, I. (1994). A dynamic theory of organizational knowledge creation. Organization science, 5(1), 14-37. https://doi.org/10.1287/orsc.5.1.14 Ojha, R., & Agarwal, A. (2024). People-centric variables in the fourth industrial revolution: an application of SWARA methodology. Journal of Advances in Management Research. https://doi.org/10.1108/JAMR-12-2023-0357 Padilla Bejarano, J. B., Zartha Sossa, J. W., Ocampo-Lopez, C., & Ramirez-Carmona, M. (2023). University technology transfer from a knowledge-flow approach—Systematic literature review. Sustainability, 15(8), 6550. https://doi.org/10.3390/su15086550 Petrillo, A., & De Felice, F. (Eds.). (2023). Analytic Hierarchy Process-Models, Methods, Concepts, and Applications: Models, Methods, Concepts, and Applications. BoD–Books on Demand. Putra, I., Wiagustini, N. L. P., Ramantha, I. W., & Sedana, I. B. P. (2021). Financial sustainability based on resource-based view theory and knowledge-based view theory. Academic of Accounting and Financial Studies Journal. Ramos Cordeiro, E., Lermen, F. H., Mello, C. M., Ferraris, A., & Valaskova, K. (2024). Knowledge management in small and medium enterprises: a systematic literature review, bibliometric analysis, and research agenda. Journal of Knowledge Management, 28(2), 590-612. https://doi.org/10.1108/JKM-10-2022-0800 Raziq, M. M., Jabeen, Q., Saleem, S., Shamout, M. D., & Bashir, S. (2024). Organizational culture, knowledge sharing and organizational performance: a multi-country study. Business Process Management Journal, 30(2), 586-611. https://doi.org/10.1108/BPMJ-07-2023-0549 sabouri, G. , Babaki rad, A. and Shalbaf Yazdi, S. V. (2024). Financing companies based on knowledge and transparency strategy of optimal allocation of banking resources. Monetary & Financial Economics, (), 33-55. https://doi.org/10.22067/mfe.2024.90933.1475 (In Persian) Sadeghi, M. E., & Sadabadi, A. A. (2015). Evaluating science parks capacity to create competitive advantages: Comparison of Pardis Technology Park and Sheikh Bahaei Science and Technology Park in Iran. International journal of innovation and technology management, 12(06), 1550031. https://doi.org/10.1142/S0219877015500315 Salehnezhad, A., & Shadmanfar, M. H. (2024). Designing an experience management model based on the statements of imam khamenei. Strategic Management of Organizational Knowledge, 7(1), 19-52. https://doi.org/10.47176/smok.2024.1670 Samani, H., Fareed, D., & Nafar, F. (2023). Strategies for developing knowledge-based employment in Yazd province. Journal of Business Administration Researches. https://doi.org/10.22034/jbar.2023.17684.4111 (In Persian) Serenko, A., Bontis, N., & Hardie, T. (2007). Organizational size and knowledge flow: a proposed theoretical link. Journal of intellectual capital, 8(4), 610-627. https://doi.org/10.1108/14691930710830783 Seyed Javadin, S. R., Nosratpanah, R., & Ashrafzadeh, A. (2025). The influence of knowledge absorptive capacity on the knowledge-based enterprises performance by explaining the mediating role of innovation strategy and open innovation activities. Strategic Management of Organizational Knowledge, 7(4), 11-33. https://doi.org/10.47176/smok.2024.1822 (In Persian) Sikombe, S., & Phiri, M. A. (2019). Exploring tacit knowledge transfer and innovation capabilities within the buyer–supplier collaboration: A literature review. Cogent Business & Management, 6(1), 1683130. https://doi.org/10.1080/23311975.2019.1683130 Spraggon, M., & Bodolica, V. (2021). On the heterogeneity and equifinality of knowledge transfer in small innovative organizations. Management Decision, 59(6), 1421-1441. https://doi.org/10.1108/MD-03-2019-0318 Theeranattapong, T., Pickernell, D., & Simms, C. (2021). Systematic literature review paper: The regional innovation system-university-science park nexus. The Journal of Technology Transfer, 46(6), 2017-2050. https://doi.org/10.1007/s10961-020-09837-y Torkayesh, A. E., Ecer, F., Pamucar, D., & Karamaşa, Ç. (2021). Comparative assessment of social sustainability performance: Integrated data-driven weighting system and CoCoSo model. Sustainable Cities and Society, 71, 102975. https://doi.org/10.1016/j.scs.2021.102975 Triantaphyllou, E. (2000). Multi-criteria decision making methods. In Multi-criteria decision making methods: A comparative study (pp. 5-21). Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-3157-6_2 Umar, M., Sial, M. H., Ali, S. A., Bari, M. W., & Ahmad, M. (2023). Trust and social network to boost tacit knowledge sharing with mediation of commitment: does culture moderate?. VINE Journal of Information and Knowledge Management Systems, 53(6), 1135-1158. https://doi.org/10.1108/VJIKMS-01-2021-0012 Wey, W. M., & Wu, K. Y. (2007). Using ANP priorities with goal programming in resource allocation in transportation. Mathematical and computer modelling, 46(7-8), 985-1000. https://doi.org/10.1016/j.mcm.2007.03.017 YariZanganeh, M. (2023). Investigating the Impact of Managers' Attitudes Toward Change and Human Resource Development on Knowledge Management in University Libraries. Sciences and Techniques of Information Management, 9(4), 319-340. https://doi.org/10.22091/stim.2023.9528.1969 (In Persian) Zahedi, M. R., & Javadi Kamni, S. (2018). Knowledge flow approach and its role in enterprise entrepreneurship. Science and Technology Policy Letters, 8(3), 63-75. https://doi.org/20.1001.1.24767220.1397.08.3.6.7 (In Persian) Zahedi, M. R., Naghdi Khanachah, S., & Papoli, S. (2024). Identifying and prioritizing the factors affecting the knowledge flow in high-tech industries. Journal of Science and Technology Policy Management, 15(5), 895-925. https://doi.org/10.1108/JSTPM-01-2021-0011 Zhang, J., Zia, U., & Shehzad, M. U. (2025). Tacit knowledge management process, product innovation and organizational performance: exploring the role of affective trust and task efficiency. Business Process Management Journal, 31(1), 267-297. https://doi.org/10.1108/BPMJ-11-2023-0873 Zhang, S., & Wang, X. (2022). Effects of Local Government Behavior on University–Enterprise Knowledge Flow: Evidence from China. Sustainability, 14(18), 11696. https://doi.org/10.3390/su141811696 Zhang, S., Wang, X., & Zhang, B. (2021). Innovation ability of universities and the efficiency of university–industry knowledge flow: the moderating effect of provincial innovative agglomeration. Chinese Management Studies, 16(2), 446-465. https://doi.org/10.1108/CMS-08-2020-0343 Zhang, S., Wang, X., & Zhang, B. (2022). The policy effects of innovative city pilot on the dual efficiency of industry–university–research knowledge flow. Technology Analysis & Strategic Management, 34(9), 1038-1049. https://doi.org/10.1080/09537325.2021.1942446 Zhang, W., Jiang, Y., Zhou, W., & Pan, W. (2023). Antecedents of knowledge-seeking intentions and efforts within new product development teams: empirical evidence from knowledge-based Chinese companies. Journal of Knowledge Management, 27(2), 356-382. https://doi.org/10.1108/JKM-08-2021-0617
Statistics Article View: 482 PDF Download: 262

News

Statistics

Analysis Of Factors Influencing Knowledge Flow Using Multi-Criteria Decision-Making Techniques (The Case Of Study: Knowledge-Based Companies In Yazd Science And Technology Park)