Harnessing Artificial Intelligence for New Business Development: A Study of Malaysian Entrepreneurial Students
Main Article Content
Abstract
This research explores the use of artificial intelligence (AI) in the new business development of entrepreneurial students in Malaysia. The study investigates the effects of AI-powered automation, AI-facilitated customer engagement, and AI-fueled innovation on business performance, with entrepreneurial self-efficacy as a mediating factor. A structured questionnaire using Likert scale items was administered to entrepreneurial students in four public sector universities in Malaysia. Data were analyzed using SPSS version 29 and Smart PLS 4, including regression analysis and mediation testing. The results indicate that artificial intelligence positively impacts overall business performance through data-driven improvement of company efficiency, customer relations and innovation, and that entrepreneurial self-efficacy mediates these relationships. The study adds to the literature on Class entrepreneurship by incorporating AI adoption as one of the dimensions of new ventures. In continuation of this, the paper’s contribution teaches educationist, policymakers and business incubators about how AI integration can be introduced in academic programs centered on entrepreneurship over the existing lean startup model. The purpose of this paper is to develop a framework of AI in entrepreneurship literature and provide future research directions to broaden the knowledge of the phenomenon concerning emerging markets.
Downloads
Article Details
Section

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
All articles published in Business Review of Digital Revolution (BRDR) are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
This license allows others to remix, adapt, and build upon the work non-commercially, and although their new works must also acknowledge the original author and be non-commercial, they don’t have to license their derivative works on the same terms.
Authors retain copyright and grant the journal the right of first publication.
How to Cite
References
Abaddi, S. (2024). GPT revolution and digital entrepreneurial intentions. Journal of Entrepreneurship in Emerging Economies, 16(6), 1903-1930. https://doi.org/10.1108/JEEE-07-2023-0260 DOI: https://doi.org/10.1108/JEEE-07-2023-0260
Apell, P., & Eriksson, H. (2023). Artificial intelligence (AI) healthcare technology innovations: the current state and challenges from a life science industry perspective. Technology Analysis and Strategic Management, 35(2), 179-193. https://doi.org/10.1080/09537325.2021.1971188 DOI: https://doi.org/10.1080/09537325.2021.1971188
Armour, J., & Sako, M. (2020). AI-enabled business models in legal services: From traditional law firms to next-generation law companies? Journal of Professions and Organization, 7(1), 27-46. https://doi.org/10.1093/jpo/joaa001 DOI: https://doi.org/10.1093/jpo/joaa001
Arora, M., & Sharma, R. L. (2023). Artificial intelligence and big data: ontological and communicative perspectives in multi-sectoral scenarios of modern businesses. Foresight, 25(1), 126-143. https://doi.org/10.1108/FS-10-2021-0216 DOI: https://doi.org/10.1108/FS-10-2021-0216
Bansal, C., Kumar, K., Goel, R., & Sharma, A. (2024). Analysis of barriers to AI banking chatbot adoption in India: An ISM and MICMAC approach. Issues in Information Systems, 25(4), 417-441. https://doi.org/10.48009/4_iis_2024_133 DOI: https://doi.org/10.48009/4_iis_2024_133
Blöcher, K., & Alt, R. (2021). AI and robotics in the European restaurant sector: Assessing potentials for process innovation in a high-contact service industry. Electronic Markets, 31(3), 529-551. https://doi.org/10.1007/s12525-020-00443-2 DOI: https://doi.org/10.1007/s12525-020-00443-2
Burström, T., Parida, V., Lahti, T., & Wincent, J. (2021). AI-enabled business-model innovation and transformation in industrial ecosystems: A framework, model and outline for further research. Journal of Business Research, 127, 85-95. https://doi.org/10.1016/j.jbusres.2021.01.016 DOI: https://doi.org/10.1016/j.jbusres.2021.01.016
Chotia, V., Cheng, Y., Agarwal, R., & Vishnoi, S. K. (2024). AI-enabled Green Business Strategy: Path to carbon neutrality via environmental performance and green process innovation. Technological Forecasting and Social Change, 202, 123315. https://doi.org/10.1016/j.techfore.2024.123315 DOI: https://doi.org/10.1016/j.techfore.2024.123315
Chowdhury, S., Budhwar, P., Dey, P. K., Joel-Edgar, S., & Abadie, A. (2022). AI-employee collaboration and business performance: Integrating knowledge-based view, socio-technical systems and organisational socialisation framework. Journal of Business Research, 144, 31-49. https://doi.org/10.1016/j.jbusres.2022.01.069 DOI: https://doi.org/10.1016/j.jbusres.2022.01.069
Cooper, R. G., & Brem, A. M. (2024). The Adoption of AI in New Product Development: Results of a Multi-Firm Study in the US and Europe. Research Technology Management, 67(3), 44-53. https://doi.org/10.1080/08956308.2024.2324241 DOI: https://doi.org/10.1080/08956308.2024.2324241
Czarnitzki, D., Fernández, G. P., & Rammer, C. (2023). Artificial intelligence and firm-level productivity. Journal of Economic Behavior and Organization, 211, 188-205. https://doi.org/10.1016/j.jebo.2023.05.008 DOI: https://doi.org/10.1016/j.jebo.2023.05.008
Dissanayake, H., Manta, O., Iddagoda, A., & Palazzo, M. (2024). AI applications in business: Trends and insights using bibliometric analysis. International Journal of Management Education, 22(3), 101075. https://doi.org/10.1016/j.ijme.2024.101075 DOI: https://doi.org/10.1016/j.ijme.2024.101075
El Khatib, M. M., & Ahmed, G. (2024). Achieving excellence in business practices through artificial intelligence: a case study of the Dubai public sector. International Journal of Public Sector Performance Management, 14(2), 262-277. https://doi.org/10.1504/IJPSPM.2024.140550 DOI: https://doi.org/10.1504/IJPSPM.2024.140550
Fosso Wamba, S., Queiroz, M. M., Chiappetta Jabbour, C. J., & Shi, C. V. (2023). Are both generative AI and ChatGPT game changers for 21st-Century operations and supply chain excellence? International Journal of Production Economics, 265, 109015. https://doi.org/10.1016/j.ijpe.2023.109015 DOI: https://doi.org/10.1016/j.ijpe.2023.109015
Giroux, M., Kim, J., Lee, J. C., & Park, J. (2022). Artificial Intelligence and Declined Guilt: Retailing Morality Comparison Between Human and AI. Journal of Business Ethics, 178(4), 1027-1041. https://doi.org/10.1007/s10551-022-05056-7 DOI: https://doi.org/10.1007/s10551-022-05056-7
Gupta, B. B., Gaurav, A., Panigrahi, P. K., & Arya, V. (2023). Analysis of artificial intelligence-based technologies and approaches on sustainable entrepreneurship. Technological Forecasting and Social Change, 186, 122152. https://doi.org/10.1016/j.techfore.2022.122152 DOI: https://doi.org/10.1016/j.techfore.2022.122152
Gurjar, K., Jangra, A., Baber, H., Islam, M., & Sheikh, S. A. (2024). An Analytical Review on the Impact of Artificial Intelligence on the Business Industry: Applications, Trends, and Challenges. IEEE Engineering Management Review, 52(2), 84-102. https://doi.org/10.1109/EMR.2024.3355973 DOI: https://doi.org/10.1109/EMR.2024.3355973
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a Silver Bullet. Journal of Marketing Theory and Practice, 19(2), 139-152. https://doi.org/10.2753/MTP1069-6679190202 DOI: https://doi.org/10.2753/MTP1069-6679190202
Hanandeh, A., Qudah, M. A. A., Mansour, A., Al-Qudah, S., Abualfalayeh, G., Kilani, Q., et al. (2024). The achievement of digital leadership sustainability and business performance through the implementation of business intelligence, artificial intelligence, and quality learning in private universities in Jordan. Uncertain Supply Chain Management, 12(4), 2581-2586. https://doi.org/10.5267/j.uscm.2024.5.012 DOI: https://doi.org/10.5267/j.uscm.2024.5.012
Helm, C., Herberger, T. A., & Gerold, N. (2022). Application of Cognitive Automation to Structuring Data, Driving Existing Business Models, and Creating Value between Legacy Industries. International Journal of Innovation and Technology Management, 19(2), 2250003. https://doi.org/10.1142/S0219877022500031 DOI: https://doi.org/10.1142/S0219877022500031
Hmoud, B. (2021). The adoption of artificial intelligence in human resource management. Forum Scientiae Oeconomia, 9(1), 105-118. https://doi.org/10.23762/FSO_VOL9_NO1_7
Hossain, M. K., Srivastava, A., Oliver, G. C., Islam, M. E., Jahan, N. A., Karim, R., et al. (2024). Adoption of artificial intelligence and big data analytics: an organizational readiness perspective of the textile and garment industry in Bangladesh. Business Process Management Journal, 30(7), 2665-2683. https://doi.org/10.1108/BPMJ-11-2023-0914 DOI: https://doi.org/10.1108/BPMJ-11-2023-0914
Issa, H., Jabbouri, R., & Palmer, M. (2022). An artificial intelligence (AI)-readiness and adoption framework for AgriTech firms. Technological Forecasting and Social Change, 182, 121874. https://doi.org/10.1016/j.techfore.2022.121874 DOI: https://doi.org/10.1016/j.techfore.2022.121874
Jochheim, E. (2021). AI powered management through individualization: The dawn of a new management era. IBIMA Business Review, 2021, 339133. https://doi.org/10.5171/2021.339133 DOI: https://doi.org/10.5171/2021.339133
Jorzik, P., Klein, S. P., Kanbach, D. K., & Kraus, S. (2024). AI-driven business model innovation: A systematic review and research agenda. Journal of Business Research, 182, 114764. https://doi.org/10.1016/j.jbusres.2024.114764 DOI: https://doi.org/10.1016/j.jbusres.2024.114764
Karinshak, E., & Jin, Y. (2023). AI-driven disinformation: a framework for organizational preparation and response. Journal of Communication Management, 27(4), 539-562. https://doi.org/10.1108/JCOM-09-2022-0113 DOI: https://doi.org/10.1108/JCOM-09-2022-0113
Kumar, M., Raut, R. D., Mangla, S. K., Ferraris, A., & Choubey, V. K. (2024). The adoption of artificial intelligence powered workforce management for effective revenue growth of micro, small, and medium scale enterprises (MSMEs). Production Planning and Control, 35(13), 1639-1655. https://doi.org/10.1080/09537287.2022.2131620 DOI: https://doi.org/10.1080/09537287.2022.2131620
McClure, C. E., Epler, R. T., Schmitt, L., & Rangarajan, D. (2024). AI in sales: Laying the foundations for future research. Journal of Personal Selling and Sales Management, 44(2), 108-127. https://doi.org/10.1080/08853134.2024.2329905 DOI: https://doi.org/10.1080/08853134.2024.2329905
Mishra, S., & Tripathi, A. R. (2021). AI business model: an integrative business approach. Journal of Innovation and Entrepreneurship, 10(1), 18. https://doi.org/10.1186/s13731-021-00157-5 DOI: https://doi.org/10.1186/s13731-021-00157-5
Moradi, M., & Dass, M. (2022). Applications of artificial intelligence in B2B marketing: Challenges and future directions. Industrial Marketing Management, 107, 300-314. https://doi.org/10.1016/j.indmarman.2022.10.016 DOI: https://doi.org/10.1016/j.indmarman.2022.10.016
Mosteanu, N. R. (2020). Artificial Intelligence and Cyber Security–A Shield against Cyberattack as a Risk Business Management Tool–Case of European Countries. Quality - Access to Success, 21(175), 148-156. https://www.proquest.com/openview/3ea188d7e260243caab6c9779fdbbf42
Ojeda, A., Valera, J., Medina, E., Samadian, H., & Padilla, R. (2024). AI implementation in big data: Shaping data analysis for business decisions. Issues in Information Systems, 25(4), 158-172. https://doi.org/10.48009/4_iis_2024_113 DOI: https://doi.org/10.48009/4_iis_2024_113
Petrescu, M., Krishen, A. S., Kachen, S., & Gironda, J. T. (2022). AI-based innovation in B2B marketing: An interdisciplinary framework incorporating academic and practitioner perspectives. Industrial Marketing Management, 103, 61-72. https://doi.org/10.1016/j.indmarman.2022.03.001 DOI: https://doi.org/10.1016/j.indmarman.2022.03.001
Raj, R., Singh, A., Kumar, V., & Verma, P. (2023). Analyzing the potential benefits and use cases of ChatGPT as a tool for improving the efficiency and effectiveness of business operations. BenchCouncil Transactions on Benchmarks, Standards and Evaluations, 3(3), 100140. https://doi.org/10.1016/j.tbench.2023.100140 DOI: https://doi.org/10.1016/j.tbench.2023.100140
Rodríguez-Espíndola, O., Chowdhury, S., Dey, P. K., Albores, P., & Emrouznejad, A. (2022). Analysis of the adoption of emergent technologies for risk management in the era of digital manufacturing. Technological Forecasting and Social Change, 178, 121562. https://doi.org/10.1016/j.techfore.2022.121562 DOI: https://doi.org/10.1016/j.techfore.2022.121562
Samadhiya, A., Kumar, A., Yadav, S., Luthra, S., Jabbour, C. J. C., & Agrawal, R. (2023). Artificial intelligence - partner relationships management for climate management in B2B firms to achieve sustainable competitiveness. Industrial Marketing Management, 115, 510-525. https://doi.org/10.1016/j.indmarman.2023.11.002 DOI: https://doi.org/10.1016/j.indmarman.2023.11.002
Sanny, L., Susastra, A. C., Roberts, C., & Yusramdaleni, R. (2020). The analysis of customer satisfaction factors which influence chatbot acceptance in Indonesia. Management Science Letters, 10(6), 1225-1232. https://doi.org/10.5267/j.msl.2019.11.036 DOI: https://doi.org/10.5267/j.msl.2019.11.036
Shaik, A. S., Alshibani, S. M., Jain, G., Gupta, B., & Mehrotra, A. (2024). Artificial intelligence (AI)-driven strategic business model innovations in small- and medium-sized enterprises. Insights on technological and strategic enablers for carbon neutral businesses. Business Strategy and the Environment, 33(4), 2731-2751. https://doi.org/10.1002/bse.3617 DOI: https://doi.org/10.1002/bse.3617
Sharma, K., Jain, M., & Dhir, S. (2022). Analysing the impact of artificial intelligence on the competitiveness of tourism firms: a modified total interpretive structural modeling (m-TISM) approach. International Journal of Emerging Markets, 17(4), 1067-1084. https://doi.org/10.1108/IJOEM-05-2021-0810 DOI: https://doi.org/10.1108/IJOEM-05-2021-0810
Sidaoui, K., Jaakkola, M., & Burton, J. (2020). AI feel you: customer experience assessment via chatbot interviews. Journal of Service Management, 31(4), 745-766. https://doi.org/10.1108/JOSM-11-2019-0341 DOI: https://doi.org/10.1108/JOSM-11-2019-0341
Sipola, J., Saunila, M., & Ukko, J. (2023). Adopting artificial intelligence in sustainable business. Journal of Cleaner Production, 426, 139197. https://doi.org/10.1016/j.jclepro.2023.139197 DOI: https://doi.org/10.1016/j.jclepro.2023.139197
Song, X., & Bonanni, C. (2024). AI-Driven Business Model: How AI-Powered Try-On Technology Is Refining the Luxury Shopping Experience and Customer Satisfaction. Journal of Theoretical and Applied Electronic Commerce Research, 19(4), 3067-3087. https://doi.org/10.3390/jtaer19040148 DOI: https://doi.org/10.3390/jtaer19040148
Srinivasan, S. M., Shah, P., & Surendra, S. S. (2021). An approach to enhance business intelligence and operations by sentimental analysis. Journal of System and Management Sciences, 11(3), 27-40. https://doi.org/10.33168/JSMS.2021.0302 DOI: https://doi.org/10.33168/JSMS.2021.0302
Srouji, J., & Bellè, S. (2022). Artificial intelligence and automated decision making: The new frontier of privacy challenges and opportunities. Journal of Data Protection and Privacy, 5(2), 162-172. https://doi.org/10.69554/YFLQ2786 DOI: https://doi.org/10.69554/YFLQ2786
Stone, M., Aravopoulou, E., Ekinci, Y., Evans, G., Hobbs, M., Labib, A., et al. (2020). Artificial intelligence (AI) in strategic marketing decision-making: a research agenda. Bottom Line, 33(2), 183-200. https://doi.org/10.1108/BL-03-2020-0022 DOI: https://doi.org/10.1108/BL-03-2020-0022
Vorzhakova, Y., & Boiarynova, K. (2020). The application of digitalization in enterprises on the basis of multiple criteria selection design. Central European Management Journal, 28(3), 127-148. https://doi.org/10.7206/cemj.2658-0845.29 DOI: https://doi.org/10.7206/cemj.2658-0845.29
Xiong, Y., Xia, S., & Wang, X. (2020). Artificial intelligence and business applications, an introduction. International Journal of Technology Management, 84(1-2), 1-7. https://doi.org/10.1504/IJTM.2020.112615 DOI: https://doi.org/10.1504/IJTM.2020.112615
Yadav, S., & Kapoor, S. (2024). Adopting artificial intelligence (AI) for employee recruitment: the influence of contextual factors. International Journal of System Assurance Engineering and Management, 15(5), 1828-1840. https://doi.org/10.1007/s13198-023-02163-0 DOI: https://doi.org/10.1007/s13198-023-02163-0
Yang, D., Zhao, W. G., Du, J., & Yang, Y. (2024). Approaching Artificial Intelligence in business and economics research: a bibliometric panorama (1966–2020). Technology Analysis and Strategic Management, 36(3), 563-578. https://doi.org/10.1080/09537325.2022.2043268 DOI: https://doi.org/10.1080/09537325.2022.2043268
Yang, X., Cao, D., Chen, J., Xiao, Z., & Daowd, A. (2020). AI and IoT-based collaborative business ecosystem: A case in Chinese fish farming industry. International Journal of Technology Management, 82(2), 151-171. https://doi.org/10.1504/IJTM.2020.107856 DOI: https://doi.org/10.1504/IJTM.2020.107856
Zhao, X. (2024). AI and organisational learning: exploring the impact of IoTs and innovation management on the organisational learning process with moderation of perceived risk. International Journal of Information Systems and Change Management, 14(1), 54-69. https://doi.org/10.1504/IJISCM.2024.138082 DOI: https://doi.org/10.1504/IJISCM.2024.138082