Leveraging Machine Learning Models to Enhance Startup Collaboration and Drive Technopreneurship

Authors

DOI:

https://doi.org/10.34306/att.v6i3.462

Keywords:

Machine Learning Models, Historical Data, Startup Collaboration, Long-term Partnership Success

Abstract

In the dynamic and competitive realm of startups, identifying and cultivating effective collaborations is crucial for sustained success. This research evaluates how machine learning (ML) technologies can enhance startup collaborations by advancing decision-making processes through the analysis of historical data. Employing the SmartPLS methodology, this study collected data from 220 stakeholders, including 207 actively engaged in startups that are either utilizing or integrating ML technologies. The investigation focuses on understanding ML models, the importance of historical data, and the dimensions of collaboration critical to the success of startups. Through analysis with PLS-SEM, it was found that ML models significantly boost inter-startup synergy and the effectiveness of collaborative efforts. The results provide vital insights for industry practitioners and strategic decision-makers, offering practical strategies to employ ML in optimizing collaboration and ensuring sustainable growth within the technopreneurship arena. This study not only highlights the benefits of ML in fostering cooperative ventures but also aims to refine the strategic frameworks essential to the startup ecosystem.

References

J. Abbas, S. Mahmood, H. Ali, M. A. Raza, G. Ali, J. Aman, S. Bano, and M. Nurunnabi, “The effects of corporate social responsibility practices and environmental factors through a moderating role of social media marketing on sustainable performance of business firms,” Sustainability, vol. 11, no. 12, p. 3434, 2019.

M. Al-Emran, V. Mezhuyev, and A. Kamaludin, “PLS-SEM in information systems research: a comprehensive methodological reference,” in Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2018, vol. 4, 2019, pp. 644–653.

I. Khong, N. A. Yusuf, A. Nuriman, and A. B. Yadila, “Exploring the impact of data quality on decision-making processes in information intensive organizations,” APTISI Transactions on Management, vol. 7, no. 3, pp. 253–260, 2023.

United Nations, “Sustainable Development Goals,” https://sdgs.un.org/goals, 2024, [Accessed: Sept. 9, 2024].

D. Wang, L. Dong, and S. Di, “Data-driven comparison of urban sustainability towards sustainable urban development under sustainable development goals (sdgs): a review based on bibliometric analysis,” Frontiers in Energy Research, vol. 11, p. 1168126, 2023.

K. Ali, D. Jianguo, and D. Kirikkaleli, “How do energy resources and financial development cause environmental sustainability?” Energy Reports, vol. 9, pp. 4036–4048, 2023.

F. Dal Mas, M. Massaro, P. Rippa, and G. Secundo, “The challenges of digital transformation in healthcare: An interdisciplinary literature review, framework, and future research agenda,” Technovation, vol. 123, p. 102716, 2023.

X. Zhang, Y. Y. Xu, and L. Ma, “Information technology investment and digital transformation: the roles of digital transformation strategy and top management,” Business Process Management Journal, vol. 29, no. 2, pp. 528–549, 2023.

M. AlHamad, I. Akour, M. Alshurideh, A. Al-Hamad, B. Kurdi, and H. Alzoubi, “Predicting the intention to use google glass: A comparative approach using machine learning models and PLS-SEM,” International Journal of Data and Network Science, vol. 5, no. 3, pp. 311–320, 2021.

O. Allal-Ch´erif, M. Guijarro-Garcia, and K. Ulrich, “Fostering sustainable growth in aeronautics: Open social innovation, multifunctional team management, and collaborative governance,” Technological Forecasting and Social Change, vol. 174, p. 121269, 2022.

K. Sharifani and M. Amini, “Machine learning and deep learning: A review of methods and applications,” World Information Technology and Engineering Journal, vol. 10, no. 07, pp. 3897–3904, 2023.

N. Anantrasirichai and D. Bull, “Artificial intelligence in the creative industries: A review,” Artificial Intelligence Review, vol. 55, no. 1, 2022.

S. L. Martiniano, R. Wu, P. M. Farrell, C. L. Ren, M. K. Sontag, A. Elbert, and S. A. McColley, “Late diagnosis in the era of universal newborn screening negatively affects short-and long-term growth and health outcomes in infants with cystic fibrosis,” The Journal of Pediatrics, vol. 262, p. 113595, 2023.

C. Digital. (2024) Machine learning trends you should know. Accessed: Sep. 9, 2024. [Online]. Available: https://copperdigital.com/blog/machine-learning-trends-you-should-know/

O. Olaniyi, A. Abalaka, and S. O. Olabanji, “Utilizing big data analytics and business intelligence for improved decision-making at leading fortune company,” Journal of Scientific Research and Reports, vol. 29, no. 9, pp. 64–72, 2023.

H. A. Javaid, “Ai-driven predictive analytics in finance: Transforming risk assessment and decision-making,” Advances in Computer Sciences, vol. 7, no. 1, 2024.

K. Shulla and W. Leal-Filho, “Achieving the un agenda 2030: Overall actions for the successful implementation of the sustainable development goals before and after the 2030 deadline,” European Union Parliament, 2023.

I. A. N. Numa, K. E. Wolf, and G. M. Pastore, “Foodtech startups: technological solutions to achieve sdgs,” Food and Humanity, vol. 1, pp. 358–369, 2023.

N. P. L. Santoso, R. A. Sunarjo, and I. S. Fadli, “Analyzing the factors influencing the success of business incubation programs: A smartpls approach,” ADI Journal on Recent Innovation, vol. 5, no. 1, pp. 60–71, 2023.

M. C. Annosi, F. Brunetta, F. Bimbo, and M. Kostoula, “Digitalization within food supply chains to prevent food waste. drivers, barriers and collaboration practices,” Industrial Marketing Management, vol. 93, pp. 208–220, 2021.

C. Lukita, N. Lutfiani, A. R. S. Panjaitan, U. Rahardja, M. L. Huzaifah et al., “Harnessing the power of random forest in predicting startup partnership success,” in 2023 Eighth International Conference on Informatics and Computing (ICIC). IEEE, 2023, pp. 1–6.

Y. Q. Ang, A. Chia, and S. Saghafian, Using machine learning to demystify startups’ funding, post-money valuation, and success. Springer, 2022.

A. Leffia, S. A. Anjani, M. Hardini, S. V. Sihotang, and Q. Aini, “Corporate strategies to improve platform economic performance: The role of technology, ethics, and investment management,” CORISINTA, vol. 1, no. 1, pp. 16–25, 2024.

M. Basheer, M. Siam, A. Awn, and S. Hassan, “Exploring the role of TQM and supply chain practices for firm supply performance in the presence of information technology capabilities and supply chain technology adoption: A case of textile firms in pakistan,” Uncertain Supply Chain Management, vol. 7, no. 2, pp. 275–288, 2019.

A. Sumanri, M. Mansoer, U. A. Matin et al., “Exploring the influence of religious institutions on the implementation of technology for stunting understanding,” Aptisi Transactions on Technopreneurship (ATT), vol. 6, no. 1, pp. 1–12, 2024.

S. L. Chaudhari and M. Sinha, “A study on emerging trends in indian startup ecosystem: big data, crowd funding, shared economy,” International Journal of Innovation Science, vol. 13, no. 1, pp. 1–16, 2021.

T. Domingues, T. Brand˜ao, and J. C. Ferreira, “Machine learning for detection and prediction of crop diseases and pests: A comprehensive survey,” Agriculture, vol. 12, no. 9, p. 1350, 2022.

A. Ghezzi, “Digital startups and the adoption and implementation of lean startup approaches: Effectuation, bricolage and opportunity creation in practice,” Technological Forecasting and Social Change, vol. 146, pp. 945–960, 2019.

U. Rahardja, Q. Aini, D. Manongga, I. Sembiring, and I. D. Girinzio, “Implementation of tensor flow in air quality monitoring based on artificial intelligence,” International Journal of Artificial Intelligence Research, vol. 6, no. 1, 2023.

Y. Xue, C. Tang, H. Wu, J. Liu, and Y. Hao, “The emerging driving force of energy consumption in china: Does digital economy development matter?” Energy Policy, vol. 165, p. 112997, 2022.

A. Ghezzi, “How entrepreneurs make sense of lean startup approaches: Business models as cognitive lenses to generate fast and frugal heuristics,” Technological Forecasting and Social Change, vol. 161, p. 120324, 2020.

R. Ahli, M. F. Hilmi, and A. Abudaqa, “Moderating effect of perceived organizational support on the relationship between employee performance and its determinants: A case of entrepreneurial firms in uae,” Aptisi Transactions on Technopreneurship (ATT), vol. 6, no. 2, pp. 199–212, 2024.

Y. Shi, E. Eremina, and W. Long, “Machine learning models for early-stage investment decision making in startups,” Managerial and Decision Economics, vol. 45, no. 3, pp. 1259–1279, 2024.

G. C. Kane, A. G. Young, A. Majchrzak, and S. Ransbotham, “Avoiding an oppressive future of machine learning: A design theory for emancipatory assistants,” MIS Quarterly, vol. 45, no. 1, pp. 371–396, 2021.

X. Yang, S. L. Sun, and X. Zhao, “Search and execution: Examining the entrepreneurial cognitions behind the lean startup model,” Small Business Economics, vol. 52, pp. 667–679, 2019.

N. Mubarak, S. Safdar, S. Faiz, J. Khan, and M. Jaafar, “Impact of public health education on undue fear of covid-19 among nurses: The mediating role of psychological capital,” International Journal of Mental Health Nursing, vol. 30, no. 2, pp. 544–552, 2021.

M. Z. B. Mustafa, M. B. Nordin, A. R. B. A. Razzaq, and B. bin Ibrahim, “Organizational commitment of vocational college teachers in malaysia,” PalArch’s Journal of Archaeology of Egypt/Egyptology, vol. 17, no. 9, pp. 5023–5029, 2020.

S. Pouyanfar, S. Sadiq, Y. Yan, H. Tian, Y. Tao, M. P. Reyes, M.-L. Shyu, S.-C. Chen, and S. S. Iyengar, “A survey on deep learning: Algorithms, techniques, and applications,” ACM Computing Surveys (CSUR), vol. 51, no. 5, pp. 1–36, 2018.

S. Kosasi, C. Lukita, M. H. R. Chakim, A. Faturahman, and D. A. R. Kusumawardhani, “The influence of digital artificial intelligence technology on quality of life with a global perspective,” Aptisi Transactions on Technopreneurship (ATT), vol. 5, no. 3, pp. 240–250, 2023.

D. Rolnick, P. L. Donti, L. H. Kaack, K. Kochanski, A. Lacoste, K. Sankaran, A. S. Ross, N. Milojevic Dupont, N. Jaques, and A. Waldman-Brown, “Tackling climate change with machine learning,” ACM Computing Surveys (CSUR), vol. 55, no. 2, pp. 1–96, 2022.

M. W. Wicaksono, M. B. Hakim, F. H. Wijaya, T. Saleh, E. Sana et al., “Analyzing the influence of artificial intelligence on digital innovation: A smartpls approach,” IAIC Transactions on Sustainable Digital Innovation (ITSDI), vol. 5, no. 2, pp. 108–116, 2024.

E. Purwanto and J. Loisa, “The intention and use behaviour of the mobile banking system in indonesia: UTAUT model,” Technology Reports of Kansai University, vol. 62, no. 6, pp. 2757–2767, 2020.

E. A. Beldiq, B. Callula, N. A. Yusuf, and A. R. A. Zahra, “Unlocking organizational potential: Assessing the impact of technology through smartpls in advancing management excellence,” APTISI Transactions on Management, vol. 8, no. 1, pp. 40–48, 2024.

D. Sj¨odin, V. Parida, M. Palmi´e, and J. Wincent, “How AI capabilities enable business model innovation: Scaling AI through co-evolutionary processes and feedback loops,” Journal of Business Research, vol. 134, pp. 574–587, 2021.

M. G. Hardini, N. A. Yusuf, A. R. A. Zahra et al., “Convergence of intelligent networks: Harnessing the power of artificial intelligence and blockchain for future innovations,” ADI Journal on Recent Innovation, vol. 5, no. 2, pp. 200–209, 2024.

N. Tripathi, M. Oivo, K. Liukkunen, and J. Markkula, “Startup ecosystem effect on minimum viable product development in software startups,” Information and Software Technology, vol. 114, pp. 77–91, 2019.

J. van der Merwe, S. M. Wahid, G. P. Cesna, D. A. Prabowo et al., “Improving natural resource management through ai: Quantitative analysis using smartpls,” International Transactions on Artificial Intelligence, vol. 2, no. 2, pp. 135–142, 2024.

M. T. Alshurideh, S. Hamadneh, B. Al Kurdi, I. A. Akour, and E. K. Alquqa, “The interplay between artificial intelligence and innovation and its impact on b2b marketing performance,” in 2023 International Conference on Business Analytics for Technology and Security (ICBATS). IEEE, 2023, pp. 1–5.

Downloads

Published

2024-09-13

How to Cite

Wijono, S., Rahardja, U., Purnomo, H. D., Lutfiani, N., & Yusuf, N. A. (2024). Leveraging Machine Learning Models to Enhance Startup Collaboration and Drive Technopreneurship. Aptisi Transactions on Technopreneurship (ATT), 6(3), 432−442. https://doi.org/10.34306/att.v6i3.462

Issue

Section

Articles

Most read articles by the same author(s)

1 2 > >>