AccScience Publishing / IJOCTA / Online First / DOI: 10.36922/IJOCTA026270140
Submit to IJOCTA
Cite this article
1
Download
24
Views
Related Info Links
Google Scholar
More by Authors Links
Syed Rizwan Ali
Muhammad Irfan Memon
Journal Browser
Volume | Year
Issue
Search
News and Announcements
View All
RESEARCH ARTICLE

An integrated partial least squares structural equation modeling, artificial neural network, and importance–performance map analysis framework for Industry 4.0 readiness in university startups

Syed Rizwan Ali1* ,  Muhammad Irfan Memon2* ,  Mohammad Mohatram2 ,  Muhammad Faraz2,3 ,  Muhammad Mumtaz Khan2,3 ,  Javeed Hussain2
Show Less
1 Department of Business Incubation Center & Software Engineering, Bahria School of Engineering and Applied Sciences, Bahria University, Karachi, Sindh , Pakistan
2 Department of Electrical and Electronics Engineering, Global College of Engineering and Technology, Muscat , Oman
3 Department of Business Studies, Bahria Business School, Bahria University, Karachi, Sindh , Pakistan
IJOCTA, 026270140 https://doi.org/10.36922/IJOCTA026270140
Received: 30 June 2026 | Revised: 25 September 2026 | Accepted: 15 October 2026 | Published online: 28 October 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
Download PDF
XML
Cite
Abstract

Since Industry 4.0 entered international policy and scholarly debate in 2011, the convergence of cyber-physical systems, the Internet of Things, cloud computing, data analytics, artificial intelligence, and intelligent automation has reshaped how university-linked startups are conceived, incubated, and scaled. This study evaluates an integrated partial least squares structural equation modeling (PLS-SEM), artificial neural network (ANN), PLSpredict/cross-validated predictive ability test (CVPAT), and importance–performance map analysis (IPMA) framework linking technological infrastructure (TI), innovation capacity (IC), entrepreneurship education (EE), digital capability (DC), and Industry 4.0 readiness (I4R). A quantitative cross-sectional survey using criterion-based purposive sampling produced 250 usable responses from student entrepreneurs, incubator resident venture representatives, faculty startup advisers, and innovation center employees. PLS-SEM with 5,000 bootstrap resamples showed that DC had the strongest positive association with I4R (β = 0.485, p < 0.001), followed by TI (β = 0.241, p < 0.001). EE showed a small, marginally significant positive association (β = 0.095, p = 0.047), whereas IC was not significant (β = 0.083, p = 0.093). The model explained 53.8% of the variance in I4R (R2 = 0.538). PLSpredict yielded positive Q2predict values for all I4R indicators, and construct-level CVPAT showed significantly lower PLS-SEM prediction loss than the linear-model benchmark (difference = −0.059, t = 2.602, p = 0.010). A fully specified, repeated cross-validated multilayer perceptron ANN ranked DC first (100% normalized importance), followed by TI (47.6%), EE (10.9%), and IC (10.0%); however, the matched linear benchmark achieved lower held-out root mean square error, so ANN is interpreted as a complementary ranking tool rather than evidence of superior predictive accuracy. IPMA identified DC (importance = 0.485; performance = 48.222) and TI (0.241; 46.037) as the leading intervention priorities. The framework therefore integrates explanatory assessment, out-of-sample prediction, predictive ranking, and intervention prioritization without implying causal proof from cross-sectional data.

Graphical abstract
Keywords
Industry 4.0 readiness
Entrepreneurship education
Digital capability
University startup ecosystem
Partial least squares structural equation modeling
Artificial neural network
Importance–performance map analysis
Intervention prioritization
Funding
None.
Conflict of interest
The authors declare no conflict of interest regarding the publication of this research.
References
  1. Kagermann H, Wahlster W, Helbig J. Recommendations for Implementing the Strategic Initiative INDUSTRIE 4.0: Securing the Future of German Manufacturing Industry: Final Report of the Industrie 4.0 Working Group. Frankfurt, Germany: Acatech, National Academy of Science and Engineering; 2013.
  2. Piardi L, Leitão P, Queiroz J, Pontes J. Role of digital technologies to enhance the human integration in industrial cyber-physical systems. Annu Rev Control. 2024;57:100934. https://doi.org/10.1016/j.arcontrol.2024.100934
  3. Greco F. Startup Ecosystems. Studies on Entrepreneurship, Structural Change and Industrial Dynamics. Cham, Switzerland: Springer; 2023. https://doi.org/10.1007/978-3-031-34414-5
  4. Muldoon-Smith K, Moreton L, Kotter R. Meaningful transfer: tech-knowlogical interdependencies in the digital built environment. Front Sustain Cities. 2021;3:709800. https://doi.org/10.3389/frsc.2021.709800
  5. Handaoui M. Digital transformation as a lever for entrepreneurial innovation in emerging business environments. J Bus Adm Econ Stud. 2025;11(2):177-196. Accessed June 15, 2026. https://asjp.cerist.dz/en/article/279069
  6. Robandi B, Setiawardani W. Training curriculum needs for entrepreneurship literacy of higher education in society 5.0. Curricula. 2025;4(1):567-580. https://doi.org/10.17509/curricula.v4i1.83520
  7. Stentoft J, Adsbøll Wickstrøm K, Philipsen K, Haug A. Drivers and barriers for Industry 4.0 readiness and practice: a SME perspective with empirical evidence. In: Proceedings of the Annual Hawaii International Conference on System Sciences. Honolulu, HI: Hawaii International Conference on System Sciences; 2019. https://doi.org/10.24251/hicss.2019.619
  8. Müller JM, Buliga O, Voigt KI. Fortune favors the prepared: how SMEs approach business model innovations in Industry 4.0. Technol Forecast Soc Change. 2018;132:2-17. https://doi.org/10.1016/j.techfore.2017.12.019
  9. Wahl D, Münch J. Turning students into Industry 4.0 entrepreneurs: design and evaluation of a tailored study program. Entrep Educ. 2022;5(3):225-259. https://doi.org/10.1007/s41959-022-00077-9
  10. Aghazadeh H, Zandi F, Amoozad Mahdiraji H, Sadraei R. Digital transformation and SME internationalisation: unravelling the moderated-mediation role of digital capabilities, digital resilience and digital maturity. J Enterp Inf Manag. 2024;37(5):1499-1526. https://doi.org/10.1108/JEIM-02-2023-0092
  11. Hoque MT, Tzokas N, Arslan A, Gupta DR, Ahammad MF. Knowledge management, operational resilience and export performance: moderating roles of strategic flexibility and maturity of digital transformation in emerging markets. Int Mark Rev. 2026;43(3):534-559. https://doi.org/10.1108/IMR-10-2024-0389
  12. Firmansyah D, Wahdiniwaty R. Prediction of innovation capability: the role of mediation in the relationship between digital transformation and competitiveness with the PLS-SEM model. Int J Manag Bus Intell. 2023;1(2):125-142. https://doi.org/10.59890/ijmbi.v1i2.238
  13. Salem IE, Elbaz AM, Magdy A, Hassan H, Ba Awain AM. Behavioural and innovative traits driving digital transformation and competitive advantage: the role of entrepreneurship orientation. Asia Pac J Bus Adm. 2026;18(2):544-566. https://doi.org/10.1108/APJBA-03-2025-0231
  14. Khan RU, Almazrouei FSSS, Sharairi MH, Khan MY, Harmal MAA. Digital leadership capabilities and dynamic workplaces enhance IT firm performance through digital innovation using a PLS SEM and ANN approach. Discov Sustain. 2026;7:605. https://doi.org/10.1007/s43621-026-02951-1
  15. Stentoft J, Adsbøll Wickstrøm K, Philipsen K, Haug A. Drivers and barriers for Industry 4.0 readiness and practice: empirical evidence from small and medium-sized manufacturers. Prod Plan Control. 2021;32(10):811-828. https://doi.org/10.1080/09537287.2020.1768318
  16. Hair J, Alamer A. Partial least squares structural equation modeling (PLS-SEM) in second language and education research: guidelines using an applied example. Res Methods Appl Linguist. 2022;1(3):100027. https://doi.org/10.1016/j.rmal.2022.100027
  17. Shmueli G, Ray S, Velasquez Estrada JM, Chatla SB. The elephant in the room: predictive performance of PLS models. J Bus Res. 2016;69(10):4552-4564. https://doi.org/10.1016/j.jbusres.2016.03.049
  18. Sarstedt M, Richter NF, Hauff S, Ringle CM. Combined importance-performance map analysis (cIPMA) in partial least squares structural equation modeling (PLS-SEM): a SmartPLS 4 tutorial. J Mark Anal. 2024;12(4):746-760. https://doi.org/10.1057/s41270-024-00325-y
  19. Büyüközkan G, Güler M. A combined hesitant fuzzy MCDM approach for supply chain analytics tool evaluation. Appl Soft Comput. 2021;112:107812. https://doi.org/10.1016/j.asoc.2021.107812
  20. Zambon I, Cecchini M, Egidi G, Saporito MG, Colantoni A. Revolution 4.0: industry vs. agriculture in a future development for SMEs. Processes. 2019;7(1):36. https://doi.org/10.3390/pr7010036
  21. Ülker N, Otrar M. University 4.0 essentials: influence of industry 4.0 on higher education. Ind High Educ. 2025;39(5):528-538. https://doi.org/10.1177/09504222241295625
  22. Müller JM, Kiel D, Voigt KI. What drives the implementation of Industry 4.0? The role of opportunities and challenges in the context of sustainability. Sustainability. 2018;10(1):247. https://doi.org/10.3390/su10010247
  23. Samo AH, Channa NA, Qureshi NA. The role of universities and entrepreneurial ecosystem in producing entrepreneurs for industry 4.0. J High Educ Theory Pract. 2021;21(11):28-40. https://doi.org/10.33423/jhetp.v21i11.4661
  24. Ferreira NC, Ferreira JJ. Enhancing sustainable entrepreneurship in SMEs: a multi-criteria analysis of internal initiatives and their causal relationships. Ann Oper Res. Published online 2025. https://doi.org/10.1007/s10479-024-06445-z
  25. Göçoğlu V, Göksu S, Kotter R. Unleashing urban technology dynamics: the interplay of AI patents, metropolitan area population, and R&D expenditures in sustainable urban development. J Urban Technol. 2025;32(3):139-161. https://doi.org/10.1080/10630732.2023.2289818
  26. Partridge A, Batchelder C. Startup Boosting in an Industry 4.0 Context. Saint-Denis, France: URBACT; 2022. Accessed October 27, 2026. https://urbact.eu/sites/default/files/2022-12/3.Startup%20Boosting%20thematic%20report.pdf
  27. Kumar JS, Balasubramani M. Digital entrepreneurship and startup ecosystem development in India in the era of Industry 4.0. SSRN. 2026. https://doi.org/10.2139/ssrn.6948878
  28. Hair JF, Risher JJ, Sarstedt M, Ringle CM. When to use and how to report the results of PLS-SEM. Eur Bus Rev. 2019;31(1):2-24. https://doi.org/10.1108/EBR-11-2018-0203
  29. Fayolle A, Liñán F. The future of research on entrepreneurial intentions. J Bus Res. 2014;67(5):663-666. https://doi.org/10.1016/j.jbusres.2013.11.024
  30. Nambisan S. Digital entrepreneurship: toward a digital technology perspective of entrepreneurship. Entrep Theory Pract. 2017;41(6):1029-1055. https://doi.org/10.1111/etap.12254
  31. Khin S, Ho TCF. Digital technology, digital capability and organizational performance: a mediating role of digital innovation. Int J Innov Sci. 2019;11(2):177-195. https://doi.org/10.1108/IJIS-08-2018-0083
  32. Teece DJ. Explicating dynamic capabilities: the nature and microfoundations of (sustainable) enterprise performance. Strateg Manag J. 2007;28(13):1319-1350. https://doi.org/10.1002/smj.640
  33. Prajogo DI, Ahmed PK. Relationships between innovation stimulus, innovation capacity, and innovation performance. R&D Manag. 2006;36(5):499-515. https://doi.org/10.1111/j.1467-9310.2006.00450.x
  34. Assink M. Inhibitors of disruptive innovation capability: a conceptual model. Eur J Innov Manag. 2006;9(2):215-233. https://doi.org/10.1108/14601060610663587
  35. Podsakoff PM, MacKenzie SB, Lee JY, Podsakoff NP. Common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl Psychol. 2003;88(5):879-903. https://doi.org/10.1037/0021-9010.88.5.879
  36. Kock N. Common method bias in PLS-SEM: a full collinearity assessment approach. Int J e-Collab. 2015;11(4):1-10. https://doi.org/10.4018/ijec.2015100101
  37. Henseler J, Ringle CM, Sarstedt M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J Acad Mark Sci. 2015;43(1):115-135. https://doi.org/10.1007/s11747-014-0403-8
  38. Shmueli G, Sarstedt M, Hair JF, et al. Predictive model assessment in PLS-SEM: guidelines for using PLSpredict. Eur J Mark. 2019;53(11):2322-2347. https://doi.org/10.1108/EJM-02-2019-0189
  39. Liengaard BD, Sharma PN, Hult GTM, et al. Prediction: coveted, yet forsaken? Introducing a cross-validated predictive ability test in partial least squares path modeling. Decis Sci. 2021;52(2):362-392. https://doi.org/10.1111/deci.12445
  40. Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol. 1986;51(6):1173-1182. https://doi.org/10.1037/0022-3514.51.6.1173
  41. Preacher KJ, Hayes AF. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behav Res Methods. 2008;40(3):879-891. https://doi.org/10.3758/BRM.40.3.879
Next article in this issue
Share
Back to top
An International Journal of Optimization and Control: Theories & Applications, Electronic ISSN: 2146-5703 Print ISSN: 2146-0957, Published by AccScience Publishing
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here
Accept