Abstract
Learning Progress Motivation (LPM) was developed to intrinsically incent organizational members adapting both exploratory and exploitative learning in innovation processes. LPM was applied on 103 engineers in the experiments which simulated innovation contexts. The computational technique of LPM was developed to dynamically transform learning records to a Learning Progress Characteristic (LPC) curve at real-time base. Through analyzing LPC curve, LPM four-phase cycles and the steady-state condition of LPM innovation process were identified. Engineers are motivated intrinsically and continuously by LPM to pursue maximal learning progress. The learning progress, innovation performance, and steady-state condition can be diagnosed by interpreting LPC curve. Therefore, the visual aided LPC curve is a user-friendly tool for managerial leaders of organizations in diagnosing learning and innovation status in innovation processes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
March, J.G.: Exploration and exploitation in organizational learning. Organization Science 2(1), 71–87 (1991)
Oke, A., Munshi, N., Walumbwa, F.O.: The influence of leadership on innovation processes and activities. Organizational Dynamics 38(1), 64–72 (2009)
Benner, M., Tushman, M.L.: Exploitation, exploration, and process management: The productivity dilemma revisited. Academy of Management Review 28, 238–256 (2003)
Jansen, J.J.P., Vera, D., Crossan, M.: Strategic leadership for exploration and exploitation: The moderating role of environmental dynamism. The Leadership Quarterly 20, 5–18 (2009)
He, Z.L., Wong, P.K.: Exploration vs. exploitation: An empirical test of the ambidexterity hypothesis. Organization Science 15, 481–494 (2004)
Gibson, C.B., Birkinshaw, J.: The antecedents, consequences, and mediating role of organizational ambidexterity. Academy of Management Journal 47, 209–226 (2004)
O’Reilly, C.A., Tushman, M.L.: The ambidextrous organization. Harvard Business Review 82(4), 74–81 (2004)
Tushman, M.L., O’Reilly, C.A.: Ambidextrous organizations: Managing evolutionary and revolutionary change. California Management Review 38, 8–30 (1996)
Schmidhuber, J.: Curious model-building control system. In: IEEE International Joint Conference on Neural Networks, vol. 2, IEEE Press, Singapore (1991)
Crossan, M., Vera, D., Nanjad, L.: Transcendent leadership: Strategic leadership in dynamic environments. The Leadership Quarterly 19, 569–581 (2008)
Kaplan, F., Oudeyer, P.Y.: Maximizing learning progress: An internal reward system for development. Embodied Artificial Intelligence 3139, 259–270 (2004)
Oudeyer, P.Y., Kaplan, F., Hafner, V.V.: Intrinsic motivation systems for autonomous mental development. IEEE Transactions on Evolutionary Computation 11(2), 265–286 (2007)
Barto, A., Singh, S., Chentanez, N.: Intrinsically motivated learning of hierarchical collections of skills. In: 3rd International Conference on Development Learn, San Diego, CA, pp. 112–119 (2004)
Igari, I., Tani, J.: Incremental learning of sequence patterns with a modular network model. Neurocomputing 72(7–9), 1910–1919 (2009)
Oudeyer, P.Y., Kaplan, F.: Discovering communication. Connection Science 18(2), 189–206 (2006)
Takamukua, S., Arkinb, R.C.: Multi-method learning and assimilation. Robotics and Autonomous Systems 55(8), 618–627 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hsu, PS., Chang, TJ. (2011). Computational Technique of Learning Progress Motivation: Diagnosis of Learning and Innovation Status. In: Zhou, Q. (eds) Theoretical and Mathematical Foundations of Computer Science. ICTMF 2011. Communications in Computer and Information Science, vol 164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24999-0_24
Download citation
DOI: https://doi.org/10.1007/978-3-642-24999-0_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24998-3
Online ISBN: 978-3-642-24999-0
eBook Packages: Computer ScienceComputer Science (R0)