The role of industry-academia collaboration in enhancing educational opportunities and outcomes under the digital driven Industry 4.0

Caroline Olufunke Esangbedo, Jingxiao Zhang, Moses Olabhele Esangbedo, Seydou Dramane Kone, Lin Xu

Article ID: 2569
Vol 8, Issue 1, 2024

VIEWS - 12588 (Abstract)

Abstract


We studied the role of industry-academic collaboration (IAC) in the enhancement of educational opportunities and outcomes under the digital driven Industry 4.0 using research and development, the patenting of products/knowledge, curriculum development, and artificial intelligence as proxies for IAC. Relevant conceptual, theoretical, and empirical literature were reviewed to provide a background for this research. The investigator used mainly principal (primary) data from a sample of 230 respondents. The primary statistics were acquired through a questionnaire. The statistics were evaluated using the structural equation model (SEM) and Stata version 13.0 as the statistical software. The findings indicate that the direct total effect of Artificial intelligence (Aint) on educational opportunities (EduOp) is substantial (Coef. 0.2519916) and statistically significant (p < 0.05), implying that changes in Aint have a pronounced influence on EduOp. Additionally, considering the indirect effects through intermediate variables, Research and Development (Res_dev) and Product Patenting (Patenting) play crucial roles, exhibiting significant indirect effects on EduOp. Res_dev exhibits a negative indirect effect (Coef = −0.009969, p = 0.000) suggesting that increased research and development may dampen the impact of Aint on EduOp against a priori expectation while Patenting has a positive indirect effect (Coef = 0.146621, p = 0.000), indicating that innovation, as reflected by patenting, amplifies the effect of Aint on EduOp. Notably, Curriculum development (Curr_dev) demonstrates a remarkable positive indirect effect (Coef = 0.8079605, p = 0.000) underscoring the strong role of current development activities in enhancing the influence of Aint on EduOp. The study contributes to knowledge on the effective deployment of artificial intelligence, which has been shown to enhance educational opportunities and outcomes under the digital driven Industry 4.0 in the study area.


Keywords


Industry 4.0; industry-academia collaboration; artificial intelligence; patent development; curriculum; research and development; SEM

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References

  1. Abrahams L, Burke M, Mouton J (2010). Research productivity-visibility-accessibility and scholarly communication in Southern African universities. The African Journal of Information and Communication (AJIC) 10. doi: 10.23962/10539/19768
  2. Agrawal AK (2001). University-to-industry knowledge transfer: Literature review and unanswered questions. International Journal of Management Reviews 3(4): 285–302. doi: 10.1111/1468-2370.00069
  3. Aiello F, Cardamone P, Pupo V (2019). New evidence on the firm-university linkages in Europe. The role of meritocratic management practices. International Review of Applied Economics 33(6): 813–828. doi: 10.1080/02692171.2019.1608917
  4. Aishath MA, Chaithra BK, Nishmitha B, et al. (2019). Survey on artificial intelligence. International Journal of Computer Sciences and Engineering 7(5): 1778–1790. doi: 10.26438/ijcse/v7i5. 17781790
  5. Albahari A, Pérez-Canto S, Barge-Gil A, et al. (2017). Technology Parks versus Science Parks: Does the university make the difference? Technological Forecasting and Social Change 116: 13–28. doi: 10.1016/j.techfore.2016.11.012
  6. Alexander A, Martin DP, Manolchev C, et al. (2020). University–industry collaboration: Using meta-rules to overcome barriers to knowledge transfer. The Journal of Technology Transfer 45(2): 371–392. doi: 10.1007/s10961-018-9685-1
  7. Ankrah SN, Al-Tabbaa O (2015). Universities-industry collaboration: A systematic review. Scandinavian Journal of Management 31: 387–408. doi: 10.2139/ssrn.2596018
  8. Apiyo R, Kiarie D (2018). Role of ICT tools in supply chain performance. International Journal of Supply Chain Management 3(1): 17–26 (2018).
  9. Argyropoulou M, Soderquist KE, Ioannou G (2019). Getting out of the European Paradox trap: Making European research agile and challenge driven. European Management Journal 37(1): 1–5. doi: 10.1016/j.emj.2018.10.005
  10. Balconi M, Laboranti A (2006). University–industry interactions in applied research: The case of microelectronics. Research Policy 35(10): 1616–1630. doi: 10.1016/j.respol.2006.09.018
  11. Belitski M, Aginskaja A, Marozau R (2019). Commercializing university research in transition economies: Technology transfer offices or direct industrial funding? Research Policy 48(3): 601–615. doi: 10.1016/j.respol.2018.10.011
  12. Biba E (2016). Three Ways Artificial Intelligence is Helping to Save the World. Ensia.
  13. Boersma FK, Reinecke CJ, Gibbons M (2008). Organizing the University–Industry Relationship: A case study of research policy and curriculum restructuring at the North‐West University in South Africa. Tertiary Education and Management 14(3): 209–226. doi: 10.1080/13583880802228216
  14. Breese R (2012). Benefits realisation management: Panacea or false dawn? International Journal of Project Management 30(3): 341–351. doi: 10.1016/j.ijproman.2011.08.007
  15. Cai Y, Etzkowitz H (2020). Theorizing the Triple Helix model: Past, present, and future. Triple Helix Journal 6(1): 1–38. doi: 10.1163/21971927-bja10003
  16. Calder ES (2007). Best Practices for University-Industry Collaboration [PhD thesis]. Massachusetts Institute of Technology.
  17. Chen L, Chen P, Lin Z (2020). Artificial intelligence in education: A review. IEEE Access 8: 75264–75278. doi: 10.1109/access.2020.2988510
  18. Chen S (2018). China’s schools are quietly using AI to mark students’ essays… but do the robots make the grade? Society, South China Morning Post, 27 May 2018.
  19. Cheng W, Drahos P (2017). How China built the world’s biggest patent office: The pressure driving mechanism. IIC-International Review of Intellectual Property and Competition Law 49(1): 5–40. doi: 10.1007/s40319-017-0655-1
  20. Cohen WM, Levinthal DA (1989). Innovation and learning: The two faces of R & D. The Economic Journal 99(397): 569–596. doi: 10.2307/2233763
  21. Colman AM (2015). A Dictionary of Psychology. Oxford University Press.
  22. Comodi G, Cioccolanti L, Mahkamov K, et al. (2019). Analysis of labour market needs for engineers with enhanced knowledge in renewable energy in some European and Latin-American Countries. Energy Procedia 158: 1135–1140. doi: 10.1016/j.egypro.2019.01.279
  23. Cunningham JA, Link AN (2014). Fostering university-industry R&D collaborations in European Union countries. International Entrepreneurship and Management Journal 11(4): 849–860. doi: 10.1007/s11365-014-0317-4
  24. D’Este P, Patel P (2007). University–industry linkages in the UK: What are the factors underlying the variety of interactions with industry? Research Policy 36(9): 1295–1313. doi: 10.1016/j.respol.2007.05.002
  25. Deborah R (2011). Partnering industry and education for curricular enhancement: A response for greater educational achievement. Online Journal of Workforce Education and Development, 2, 1-15.
  26. Dill DD (1995). University-industry entrepreneurship: The organization and management of American university technology transfer units. Higher Education 29(4): 369–384. doi: 10.1007/bf01383958
  27. Dooley L, Kirk D (2007). Universityindustry collaboration. European Journal of Innovation Management 10(3): 316–332. doi: 10.1108/14601060710776734
  28. Dopson LR, Tas RF (2004). A practical approach to curriculum development: A case study. Journal of Hospitality & Tourism Education 16(1): 39–46. doi: 10.1080/10963758.2004.10696783
  29. Dosi G, Llerena P, Labini MS (2006). The relationships between science, technologies and their industrial exploitation: An illustration through the myths and realities of the so-called ‘European Paradox.’ Research Policy 35(10): 1450–1464. doi: 10.1016/j.respol.2006.09.012
  30. Edmondson G, Valigra L, Kenward M, et al. (2012). Making Industry-University Partnerships Work. Lessons from Successful Collaborations. Business Innovati on Board.
  31. Estevez J, Garate G, Grana M (2019). Gentle introduction to artificial intelligence for high-school students using scratch. IEEE Access 7: 179027–179036. doi: 10.1109/access.2019.2956136
  32. Esangbedo, C. O., Zhang, J., Esangbedo, M. O., Kone, S. D., & Xu, L.. Industry-academia collaboration survey. 6th March, 2023. From https://bit.ly/3poGbVm
  33. Fernández López S, Pérez Astray B, Rodeiro Pazos D, et al. (2014). Are firms interested in collaborating with universities? An open-innovation perspective in countries of the South West European Space. Service Business 9(4): 637–662. doi: 10.1007/s11628-014-0243-0
  34. Fischer BB, Schaeffer PR, Vonortas NS (2019). Evolution of university-industry collaboration in Brazil from a technology upgrading perspective. Technological Forecasting and Social Change 145: 330–340. doi: 10.1016/j.techfore.2018.05.001
  35. George D, Mallery P (2021). IBM SPSS Statistics 27 Step by Step. Routledge. doi: 10.4324/9781003205333
  36. Griliches Z (1990). Patent statistics as economic indicators: A survey. Journal of Economic Literature 28: 1661–1707. doi: 10.3386/w3301
  37. Guimón J (2013). Promoting University-Industry Collaboration in Developing Countries. Word Bank.
  38. Hair JF (2011). Multivariate data analysis: An overview. International Encyclopedia of Statistical Science 904–907. doi: 10.1007/978-3-642-04898-2_395
  39. He Y, Bowser A (2017). How China is Preparing for an AI-powered Future. Wilson Briefs.
  40. Homma H, Ikeda N, Attalage RA (2008). Strengthening university-industry linkage in developing countries through international cooperation: Case of Sri Lanka through cooperation of Toyohashi University of Technology, Japan. In: Iskander M (editor). Innovative Techniques in Instruction Technology, E-learning, E-assessment, and Education. Springer. pp. 432–436. doi: 10.1007/978-1-4020-8739-4_76
  41. Hong J, Hong S, Wang L, et al. (2015). Government grants, private R&D funding and innovation efficiency in transition economy. Technology Analysis & Strategic Management 27(9): 1068–1096. doi: 10.1080/09537325.2015.1060310
  42. Hong W (2008). Decline of the center: The decentralizing process of knowledge transfer of Chinese universities from 1985 to 2004. Research Policy 37(4): 580–595. doi: 10.1016/j.respol.2007.12.008
  43. Hou B, Hong J, Chen Q, et al. (2019). Do academia-industry R&D collaborations necessarily facilitate industrial innovation in China? European Journal of Innovation Management 22(5): 717–746. doi: 10.1108/ejim-09-2018-0195
  44. Hou B, Hong J, Wang H, et al. (2018). Academia-industry collaboration, government funding and innovation efficiency in Chinese industrial enterprises. Technology Analysis & Strategic Management 31(6): 692–706. doi: 10.1080/09537325.2018.1543868
  45. Hu L, Bentler PM (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal 6(1): 1–55. doi: 10.1080/10705519909540118
  46. Hughes A, Kitson M (2012). Pathways to impact and the strategic role of universities: New evidence on the breadth and depth of university knowledge exchange in the UK and the factors constraining its development. Cambridge Journal of Economics 36(3): 723–750. doi: 10.1093/cje/bes017
  47. Jing M (2018). China wants to bring artificial intelligence to its classrooms to boost its education system. Science & Research, 14 October 2017.
  48. Khachoo Q, Sharma R, Dhanora M (2018). Does proximity to the frontier facilitate FDI-spawned spillovers on innovation and productivity? Journal of Economics and Business 97: 39–49. doi: 10.1016/j.jeconbus.2018.03.002
  49. Kim YC, Rhee M, Kotha R (2019). Many hands: The effect of the prior inventor-intermediaries relationship on academic licensing. Research Policy 48(3): 813–829. doi: 10.1016/j.respol.2018.11.007
  50. Kollo T, von Rosen D, Hazewinkel M (2005). Advanced Multivariate Statistics with Matrices. Springer Netherlands. doi: 10.1007/1-4020-3419-9
  51. Koomsap P, Luong HT, Lima RM, et al. (2019). Roles of MSIE graduates to support Thailand sustainable smart industry. In: Transdisciplinary Engineering for Complex Socio-technical Systems. IOS Press. doi: 10.3233/atde190110
  52. Kusmin KL, Tammets K, Ley T (2018). University-industry interoperability framework for developing the future competences of Industry 4.0. Interaction Design and Architecture(s) (38): 28–45. doi: 10.55612/s-5002-038-002
  53. Laursen K, Salter A (2004). Searching high and low: What types of firms use universities as a source of innovation? Research Policy 33(8): 1201–1215. doi: 10.1016/j.respol.2004.07.004
  54. Leydesdorff L (2004). The university–industry knowledge relationship: Analyzing patents and the science base of technologies. Journal of the American Society for Information Science and Technology 55(11): 991–1001. doi: 10.1002/asi.20045
  55. Li T, Zhao Z, Wang Y, et al. (2020). The status of university-industry collaboration in China, EU and USA—A comparative research on co-authored publications (2020). In: Proceedings of 2020 ASEE Virtual Annual Conference Content Access; 22–26 June 2020.
  56. Liefner I, Kroll H, Peighambari A (2016). Research-driven or party-promoted? Factors affecting patent applications of private small and medium-sized enterprises in China’s Pearl River Delta. Science and Public Policy 43(6): 849–858. doi: 10.1093/scipol/scw002
  57. Liu X, Schwaag Serger S, Tagscherer U, et al. (2017). Beyond catch-up—Can a new innovation policy help China overcome the middle income trap? Science and Public Policy 44(5): 656–669. doi: 10.1093/scipol/scw092
  58. Luan C, Zhou C, Liu A (2010). Patent strategy in Chinese universities: A comparative perspective.Scientometrics 84(1): 53–63. doi: 10.1007/s11192-010-0194-8
  59. Lucietto A, Peters D, Taleyarkhan M, et al. (2021). Academic and industry collaboration: A literature review. In: School of Engineering Education Faculty Publications. Purdue University.
  60. Manyika J, Lund S, Chui M, et al. (2017). Jobs Lost, Jobs Gained: What the Future of Work Will Mean for Jobs, Skills, and Wages. McKinsey Global Institute.
  61. Mazzocchi S (2004). Open innovation: The new imperative for creating and profiting from technology. Innovation 6(3): 474. doi: 10.5172/impp.2004.6.3.474
  62. Mears L, Omar M, Kurfess TR (2009). Automotive engineering curriculum development: Case study for Clemson University. Journal of Intelligent Manufacturing 22(5): 693–708. doi: 10.1007/s10845-009-0329-z
  63. Mgaiwa SJ (2021). Fostering graduate employability: Rethinking Tanzania’s university practices. SAGE Open 11(2): 215824402110067. doi: 10.1177/21582440211006709
  64. Miller K, McAdam R, McAdam M (2016). A systematic literature review of university technology transfer from a quadruple helix perspective: Toward a research agenda. R&D Management 48(1): 7–24. doi: 10.1111/radm.12228
  65. Ministry of Education (2019). Annual Report on the National Higher Education. Ministry of Education.
  66. Ministry of Science and Technology China (2020). Report of the Ministry of Science and Technology of the People’s Republic of China on the Development of Industry-Academia Collaboration in 2020 (Chinese). Ministry of Science and Technology.
  67. Motohashi K (2006). China’s national innovation system reform and growing science industry linkage. Asian Journal of Technology Innovation 14(2): 49–65. doi: 10.1080/19761597.2006.9668618
  68. Motohashi K, Muramatsu S (2012). Examining the university industry collaboration policy in Japan: Patent analysis. Technology in Society 34(2): 149–162. doi: 10.1016/j.techsoc.2012.02.006
  69. Mukherji N, Silberman J (2021). Knowledge flows between universities and industry: The impact of distance, technological compatibility, and the ability to diffuse knowledge. The Journal of Technology Transfer 46(1): 223–257. doi: 10.1007/s10961-019-09770-9
  70. Munyoki J, Kibera F, Ogutu M (2011). Extent to which university- industry linkage exists in Kenya: A study of medium and large manufacturing firms in selected industries in Kenya. Business Administration and Management 1(4): 163–169.
  71. Murphy R (2020). Artificial intelligence applications to support K-12 teachers and teaching: A review of promising applications, challenges, and risks. RAND Corporation. doi: 10.7249/pe315
  72. Nagaoka S, Motohashi K, Goto A (2010). Patent statistics as an innovation indicator. Handbook of the Economics of Innovation 2: 1083–1127. doi: 10.1016/s0169-7218(10)02009-5
  73. National Bureau of statistics of China (2010). China Statistical Year Book. Tech. Rep.
  74. Naughton B, Tsai KS (2015). State Capitalism, Institutional Adaptation, and the Chinese Miracle. Cambridge University Press. doi: 10.1017/cbo9781139962858
  75. O’Dwyer M, Filieri R, O’Malley L (2023). Establishing successful university–industry collaborations: Barriers and enablers deconstructed. The Journal of Technology Transfer 48(3): 900–931. doi: 10.1007/s10961-022-09932-2
  76. Payne M (2007). Benefits Management: Releasing Project Value into the Business. Project Manager Today.
  77. Perkmann M, Neely A, Walsh K (2011). How should firms evaluate success in university–industry alliances? A performance measurement system. R&D Management 41(2): 202–216. doi: 10.1111/j.1467-9310.2011.00637.x
  78. Perkmann M, Tartari V, McKelvey M, et al. (2012). Academic engagement and commercialization: A review of the literature on university-industry relations. Research Policy 42(2): 423–442. doi: 10.2139/ssrn.2088253
  79. Perkmann M, Walsh K (2007). University–industry relationships and open innovation: Towards a research agenda. International Journal of Management Reviews 9(4): 259–280. doi: 10.1111/j.1468-2370.2007.00225.x
  80. Polt W, Rammer C, Gassler H, et al. (2001). Benchmarking industry-science relations: The role of framework conditions. Science and Public Policy 28(4): 247–258. doi: 10.3152/147154301781781453
  81. Ponomariov B (2013). Government-sponsored university-industry collaboration and the production of nanotechnology patents in US universities. The Journal of Technology Transfer 38(6): 749–767. doi: 10.1007/s10961-013-9301-3
  82. Preacher KJ, Hayes AF (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods, Instruments, & Computers 36(4): 717–731. doi: 10.3758/bf03206553
  83. Prud’homme D (2017). Utility model patent regimes and innovation in China and Beyond*. In: Patents and Innovation in China and Hong Kong. Cambridge University Press. pp. 29–78. doi: 10.1017/9781108163583.004.
  84. Rahm D, Kirkland J, Bozeman B (2000). University-Industry R&D Collaboration in the United States, the United Kingdom, and Japan. Springer Netherlands. doi: 10.1007/978-94-015-9574-2
  85. Rosell C, Agrawal A (2009). Have university knowledge flows narrowed? Research Policy 38(1): 1–13. doi: 10.1016/j.respol.2008.07.014
  86. Ryan S (2021). The U.S., China, and Artificial Intelligence Competition Factors. China Aerospace Studies Institute.
  87. Scandura A (2016). University–industry collaboration and firms’ R&D effort. Research Policy 45(9): 1907–1922. doi: 10.1016/j.respol.2016.06.009
  88. Shewakena Tessema B (2017). University-industry collaboration in curriculum development: Analysis of banking and finance graduates’ attributes from educators and industries perspective. Education Journal 6(2): 87. doi: 10.11648/j.edu.20170602.13
  89. Smith K (2006). Measuring Innovation. Oxford Handbooks. doi: 10.1093/oxfordhb/9780199286805.003.0006
  90. Thorndike RM (1995). Book review: Psychometric theory (3rd ed.) by Jum Nunnally and Ira Bernstein New York: McGraw-Hill, 1994, xxiv + 752 pp. Applied Psychological Measurement 19(3): 303–305. doi: 10.1177/014662169501900308
  91. Tucker LR, Lewis C (1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika 38(1): 1–10. doi: 10.1007/bf02291170
  92. United Nations Educational, Scientific and Cultural Organization (UNESCO) (2019). ICT in Education: A Global Outlook. United Nations Educational, Scientific and Cultural Organization (UNESCO).
  93. Vedaraman S (1971). Patents: Recent Developments and Future Prospects on the National Level in India. WIPO Lectures, Montreaux
  94. Wang C, Wang L (2016). Unfolding policies for innovation intermediaries in China: A discourse network analysis. Science and Public Policy 44(3): 354–368. doi: 10.1093/scipol/scw068
  95. Weagle D, Ortendahl D, Ahern AP (2019). Universities and industries: A proactive countries. Innov. Solutions for Energy Transitions 158: 1135–1140. Doi: 10.1016/j.egypro.2019.01.279.l
  96. West SG, Taylor AB, Wu W (2012). Model fit and model selection in structural equation modeling. In: Hoyle RH (editor). Handbook of Structural Equation Modeling. The Guilford Press. pp. 209–231.
  97. Xie J, Bentler PM (2003). Covariance structure models for gene expression microarray data. Structural Equation Modeling: A Multidisciplinary Journal 10(4): 566–582. doi: 10.1207/s15328007sem1004_5
  98. Zhang L, Wang H, Li Q, et al. (2018). Big data and medical research in China. BMJ 360: j5910. doi: 10.1136/bmj.j5910
  99. Zheng Y, Wang Y (2021). Research on the contribution rate of scientific and technological progress to Chongqing’s economic growth based on the Solow Growth model. Proceedings of E3S Web of Conferences 235: 01012. doi: 10.1051/e3sconf/202123501012
  100. Ziegler WL (1983). Computer science education and industry: Preventing educational misalignment. In: The Proceedings of the Twentieth Annual Computer Personnel on Research Conference; 17–18 November 1983; New York, NY, United States. doi: 10.1145/800030.800626


DOI: https://doi.org/10.24294/jipd.v8i1.2569

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