Abstract
Entrepreneurship education has traditionally been based on business management education, which has been ineffective in teaching the complex, dynamic, and non-linear nature of entrepreneurship. Recent attempts at entrepreneurship education have involved using computer simulations to reduce time and cost. One type of computer simulation, agent-based modeling and simulation (ABMS), takes into account the behaviors, reactions, and interactions of individual agents and has the potential to capture the behavior of a complex system, such as a market, more accurately than traditional approaches. This study proposes the use of ABMS in a technology entrepreneurship program to teach students about the theory of diffusion of innovations and how to implement various technology strategies, such as pursuing technology leadership or behaving as an early adopter. The simulation will involve a minimal model implemented using NetLogo and students will be able to explore different strategies and experience the advantages and drawbacks of each. The learning experience will be designed based on entrepreneurial process theories, learning by trying/emergence, learning from failure, bounded rationality, effectuation, entrepreneurial cognition, and entrepreneurial management and growth. The goal of the simulation is to provide students with an experiential and adaptive learning environment that allows them to understand as entrepreneurs how consumers adopt new innovations and receive feedback on their decisions. The user interface will show an animation of the model’s results and output, some of which include: the adoption curve, adopter profiles, and the social network.
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Acknowledgements
I wish to thank Maria Mercedes T. Rodrigo, Ph.D., my adviser, for the support and encouragement given to me since the beginning of this journey.
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Ilagan, J.B., Ilagan, J.R. (2023). Teaching Diffusion of Innovations Involving Technology Startups Using Agent-Based Simulation Modeling: Architecture and Design Considerations. In: Sottilare, R.A., Schwarz, J. (eds) Adaptive Instructional Systems. HCII 2023. Lecture Notes in Computer Science, vol 14044. Springer, Cham. https://doi.org/10.1007/978-3-031-34735-1_21
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