Stocks of Knowledge and Organizational Performance a Dynamic Relationship.Pdf
STOCKS OF KNOWLEDGE AND ORGANIZATIONAL PERFORMANCE: A DYNAMIC RELATIONSHIP
Fernando Arenas Universidad ICESI Calle 18 122-135, Cali, Colombia tel: +57 2 5552334 x8858 faarenas@icesi.edu.co Abstract The relationship between the level of knowledge and organizational performance has been studied by the academic community and is receiving growing attention from decision makers in organizations. However it is not the case of the feedback relationship between performance and the level of knowledge, although this relationship has a dynamic pattern of behavior on both variables. This research poses a conceptual approach that involves a causal model of this feedback relationship, theoretically founded on the resource based view and the behavioral theory of the firm. The methodology involves the design and use of a system dynamics simulation model based on a pharmaceutical company which relates stocks of knowledge, innovation capability, financial performance and investments on knowledge stocks. The concept of “managerial dynamic hypothesis” is defined and used to explain, via the prospect theory, how much managers decide to invest over time on knowledge stocks. Simulations, based on managerial dynamic hypotheses with two different levels of complexity, were carried out. The results show that the more complex the hypothesis is the more stable the investment flow is and a better performance is achieved. Keywords: knowledge stocks, organizational performance, organizational knowledge, prospect theory, simulation. system dynamics,
1. INTRODUCTION The influence of variables related to knowledge (knowledge management, stocks of knowledge, organizational learning) on organizational performance, has been the subject of numerous studies. It has not occurred in the same way with the study of the influence of the performance itself on these variables. It should then be asked, is organizational performance just a result of these variables, or does it, in turn, influence them? If
Fernando Arenas Universidad ICESI Calle 18 122-135, Cali, Colombia tel: +57 2 5552334 x8858 faarenas@icesi.edu.co Abstract The relationship between the level of knowledge and organizational performance has been studied by the academic community and is receiving growing attention from decision makers in organizations. However it is not the case of the feedback relationship between performance and the level of knowledge, although this relationship has a dynamic pattern of behavior on both variables. This research poses a conceptual approach that involves a causal model of this feedback relationship, theoretically founded on the resource based view and the behavioral theory of the firm. The methodology involves the design and use of a system dynamics simulation model based on a pharmaceutical company which relates stocks of knowledge, innovation capability, financial performance and investments on knowledge stocks. The concept of “managerial dynamic hypothesis” is defined and used to explain, via the prospect theory, how much managers decide to invest over time on knowledge stocks. Simulations, based on managerial dynamic hypotheses with two different levels of complexity, were carried out. The results show that the more complex the hypothesis is the more stable the investment flow is and a better performance is achieved. Keywords: knowledge stocks, organizational performance, organizational knowledge, prospect theory, simulation. system dynamics,
1. INTRODUCTION The influence of variables related to knowledge (knowledge management, stocks of knowledge, organizational learning) on organizational performance, has been the subject of numerous studies. It has not occurred in the same way with the study of the influence of the performance itself on these variables. It should then be asked, is organizational performance just a result of these variables, or does it, in turn, influence them? If
References: Barney, J. (1991). Firm resources and sustained competitive advantage, Journal of Management, 17 (1): 99-120. Bettis, R. A., & Prahalad, C. K. (2000): The dominant logic: retrospective and extension, Strategic Management Journal, 16 (1), 5-14. Boekestein, B. (2006). The relation of intellectual capital and intangible assets of pharmaceutical companies, Journal of Intellectual Capital, 7 (2): 241-253. Bontis, N. (1999). Managing organizational knowledge by diagnosing intellectual capital: framing and advancing the state of the field. International Journal of Technology Management, 18: 433-462. Bontis N. (2001). Assessing knowledge assets: a review of the models used to measure intellectual capital. International Journal of Management Reviews, 3:41-60. Bontis, N., Crossan, M. M. & Hulland, J. (2002): Managing an organizational learning system by aligning stocks and flows, Journal of Management Studies, 39 (4), 437-469. Bromiley, P. (2009). A prospect theory model of resource allocation. Decision Analysis, 6 (3): 124-138. Chen, M., Cheng, S. & Hwang, Y. (2005). An empirical investigation of the relationship between intellectual capital, and firm’s market value and financial performance. Journal of Intellectual Capital, 6 (2): 159-176. Davis, J.P., Eisenhardt, K.M. & Bingham, C.B. (2007). Developing theory through simulation methods, Academy of Management Review, 32: 480-499. Dierickx, I. & Cool, K. (1989). Asset stock accumulation and sustainability of competitive advantage, Management Science, 35 (12), 1504-1511. Donaldson, G. & Lorsch, J. (1983): Decision Making at the Top, Basic Books, New York. Firer, S. & Williams, S. M. (2003). Intellectual capital and traditional measures of corporate performance, Journal of Intellectual Capital, 4 (3): 348-360. Forrester, J. W. (1961): Industrial Dynamics, MIT Press, Cambridge, MA. Gary, M. S., Wood, R. E. (2011). Mental models, decision rules, and performance heterogeneity, Strategic Management Journal, 32: 569-594. Grant, R. M. (2010): Contemporary Strategy Analysis (7th ed.), Wiley. Greve, H. R. (2003): Organizational Learning from Performance Feedback: A Behavioral Perspective on Innovation and Change, Cambridge University Press, Cambridge, United Kingdom. Helfat, C.E. & Lieberman, M. (2002). The birth of capabilities: market entrance and the importance of prehistory, Industrial and Corporate Change, 12: 725-760. Kahneman, D. & Tversky, A. (eds) (2000a): Choices, Values, and Frames, Cambridge University Press, Cambridge, United Kingdom. Kahneman, D. & Tversky, A. (2000b). Prospect theory: an analysis of decision under risk. In Kahneman, D. & Tversky, A. (eds) (2000a): Choices, Values, and Frames: 17-43, Cambridge University Press, Cambridge, United Kingdom. Kaplan, R. S. & Norton, D. P. (1992). The balanced scorecard: measures that drive performance, Harvard Business Review, 70 (1): 71-79. Kaplan, R. S. & Norton, D. P. (2004): Strategy Maps, Harvard Business School Press, Cambridge, MA, USA. Maditinos, D., Chatzoudes, D., Tsairidis, D. & Theriou, G. (2011). The impact of intellectual capital on firm’s market value and financial performance. Journal of Intellectual Capital, 12 (1): 132-151. Mollona, E. (2007). A competence view of firms as resource accumulation systems: a synthesis of resource-based and evolutionary models of strategy making. En Morecroft, J., Sanchez, R. & Heene, A. (eds). Systems Perspectives on Resources, Capabilities, and Management Processes: 93-125, Emerald Group Publishing Limited, Bingley, Reino Unido. Morecroft, J., Sanchez, R. & Heene, A. (eds) (2007). Systems Perspectives on Resources, Capabilities, and Management Processes, Bingley: Emerald Group Publishing Limited. Nazari, J. A., Herremans, I. M. (2007). Extended VAIC model: measuring intellectual capital components. Journal of Intellectual Capital, 8 (4): 595-609. Oltra, V. (2002). Influencia de las políticas de recursos humanos en los procesos de desarrollo y gestión del conocimiento. Tesis Doctoral. Universidad de Valencia: Facultad de Economía. Prahalad, C. K. & Bettis, R. A. (1986). The dominant logic: a new linkage between diversity and performance, Strategic Management Journal, 7, 485-501. Prahalad, C. K. & Hamel, G. (1990). The Core Competence of the Corporation. Harvard Business Review, 30 (3): 79-91. Pulic, A. (2000). VAIC – an accounting tool for IC management. International Journal of Technology Management, 20 (5-8): 702-714. Pulic, A. (2004). Intellectual capital: does it create or destroy value? Measuring Business Excelence, 8 (1): 62-68. Rahmandad, H., Repenning, N., & Sterman, J. (2009). Effects of feedback delay on learning. System Dynamics Review, 25 (4): 309-338. Repenning, N. (2002). A simulation-based approach to understanding the dynamics of innovation implementation, Organization Science, 13: 109–127. Riahi-Belkaoui, A. (2003). Intellectual capital and financial performance on US multinational firms. Journal of Intellectual Capital, 4 (2): 215-226. Roos, J., Roos, G., Dragonetti, N. C. & Edvinsson, L. (1997). Intellectual Capital: Navigating in the New Business Landscape. London: MacMillan Press. Sanchez, R. & Heene, A. (1997). Managing for an uncertain future: a systems view of strategic organizational change, International Studies of Management & Organization, 27 (2), 21-42. Sawicka, A. & Gonzalez, J. J. (2003). Choice under risk in IT environments according to cumulative prospect theory. Proceedings of the 21st Conference of the System Dynamics Society, New York, USA. Shiu, H. J. (2006). The application of the value added intellectual coefficient to measure corporate performance: evidence from technological firms, International Journal of Management, 23 (2): 356-365. Sveiby, K. E. (1998-2001). Intellectual capital and knowledge management, [documento de WWW] URL http://www.sveiby.com/articles/IntellectualCapital.html, accedido en 11/02/2011. Tan, H. P., Plowman, D. & Hancock, P. (2007). Intellectual capital and financial return of companies. Journal of Intellectual Capital, 8 (1): 76-95 Tversky, A. & Kahneman, D. (2000). Advances in prospect theory: cumulative representation of uncertainty. In Kahneman, D. & Tversky, A. (eds) (2000a): Choices, Values, and Frames: 44-65, Cambridge University Press, Cambridge, United Kingdom. Von Neumann, J., Morgestern, O. (1947): Theory of Games and Economic Behavior (2nd ed.), Princeton University Press, Princeton, N.J. Von Krogh, G., Erat, P. & Macus, M. (2000). Exploring the link between dominant logic and company performance, Creativity and Innovation Management, 9 (2), 82-93. Zégal, D. & Maaloul, A. (2010). Analysing value added as an indicator of intellectual capital and its consequences on company performance, Journal of Intellectual Capital, 11 (1): 39-60. Zott, C. (2003). Dynamic capabilities and the emergence of intra-industry differential firm performance: Insights from a simulation study. Strategic Management Journal, 24: 97–125.