基于“刺激-反应”原理的战略联盟知识空间适应性演化
|
摘要
为明确以环境变化为导向的战略联盟知识空间适应性演化机理,建立基于"刺激-反应"的多层结构原理模型,采用Agent仿真方法进行模拟实验。研究结果表明:战略联盟知识空间演化的适应性取决于环境的刺激、原有知识结构以及规则集限定下联盟组织间的知识流动流量;演化过程中,知识空间内的知识基因具备主动追求更高适应性的需求,知识的复杂程度提高,知识的属性标识更加专业化也更具针对性;知识能力随演化的深入得到提升,但在无法获取异质性资源或遭遇瓶颈时提升速度趋于平缓;知识价值水平在演化初期有所下降,但随后呈现快速上升的趋势;知识空间的适应性演化过程不稳定性,演化存在渐进和突变两种路径,演化具有对称性破缺特征,且可以在无外力作用下自发演化至新的稳定态。
In order to specify the adaptive evolutionary mechanisms in knowledge space of strategic alliance driven by the environment, in this paper, a multi-layer structure model was established based on the stimulus-response theory, and a simulation experiment was conducted using agent-based modeling. The research results show that the adaptation of knowledge space in strategic alliance depends on the stimulus from the environment, the initial knowledge structure, and the knowledge flow among alliance organizations constrained by rule sets. In the evolutionary process, the kenes in the knowledge space are equipped with the demand for pursuing higher adaptation, thus the complexity of knowledge is improved, and knowledge capabilities are more specialized and targeted. Knowledge abilities are improved with the evolution, yet tend to have a gentle improving speed when failing to acquire heterogeneity resources or coming across bottleneck. Knowledge expertise fall in the early evolutionary process, but it presents a sharp rise later. The adaptive evolutionary process of knowledge space is unstable. Two types of evolutionary pathways which are incremental and mutational exist in the process. The evolution has the characteristic of symmetry breaking, and is able to evolve spontaneously into a new stable state without external forces.
In order to specify the adaptive evolutionary mechanisms in knowledge space of strategic alliance driven by the environment, in this paper, a multi-layer structure model was established based on the stimulus-response theory, and a simulation experiment was conducted using agent-based modeling. The research results show that the adaptation of knowledge space in strategic alliance depends on the stimulus from the environment, the initial knowledge structure, and the knowledge flow among alliance organizations constrained by rule sets. In the evolutionary process, the kenes in the knowledge space are equipped with the demand for pursuing higher adaptation, thus the complexity of knowledge is improved, and knowledge capabilities are more specialized and targeted. Knowledge abilities are improved with the evolution, yet tend to have a gentle improving speed when failing to acquire heterogeneity resources or coming across bottleneck. Knowledge expertise fall in the early evolutionary process, but it presents a sharp rise later. The adaptive evolutionary process of knowledge space is unstable. Two types of evolutionary pathways which are incremental and mutational exist in the process. The evolution has the characteristic of symmetry breaking, and is able to evolve spontaneously into a new stable state without external forces.
引文
[1] Zhao J Y, Xi X, Su Y. Resource allocation under a strategic alliance: How a cooperative network with knowledge flow spurs co-evolution[J]. Knowledge-Based Systems, 2015, 89: 497-508.
[2] Jiang X, Bao Y C, Xie Y, et al. Partner trustworthiness, knowledge flow in strategic alliances, and firm competitiveness: A contingency perspective[J]. Journal of Business Research, 2016, 69(2): 804-814.
[3] Holland J H. Hidden order: How adaptation builds complexity[M]. New York: Basic Books, 1995.
[4] Sherif K, Xing B. Adaptive processes for knowledge creation in complex systems: The case of a global IT consulting firm [J]. Information and Management, 2006, 43(4): 530-540.
[5] 王小磊,杨育,曾强,等.客户协同创新的复杂性及主体刺激—反应模型[J].科学学研究, 2009, 27(11): 1729-1735.
[6] Popoveniuc B. Self-reflexivity. The ultimate end of knowledge [J]. Procedia-Social and Behavioral Sciences, 2014, 163: 2014-213.
[7] 田钢,张永安,兰卫国.基于刺激—反应模型的集群创新网络形成机理研究[J].管理评论, 2009, 21(7): 49-55.
[8] Huang Y-P, Chang Y-T, Hsieh S-L, et al. An adaptive knowledge evolution strategy for finding near-optimal solutions of specific problems[J]. Expert Systems with Applications, 2011, 38(4): 3806-3818.
[9] Valverde-Albacete F J, González-Calabozo J M, Peňas A, et al. Supporting scientific knowledge discovery with extended, generalized formal concept analysis [J]. Expert Systems with Applications, 2016, 44: 196-216.
[10] 孙冰,袭希,姚洪涛,等.技术在环境中适应性演化的“刺激-反应”三层模型研究[J].科学学与科学技术管理, 2013, 34(4): 19-27.
[11] Chen L, Fong P S W. Evaluation of knowledge management performance: An organic approach[J]. Information & Management, 2015, 52(4): 431-453.
[12] Nonaka I, Konno N. The concept of ba: Building a foundation for knowledge creation[J]. California Management Review, 1998, 40(3): 40-54.
[13] Doignon J-P, Falmagne J-C L. Knowledge spaces[M]. Springer-Verlag, 1999.
[14] Barti E, Belohlavek R. Knowledge spaces with graded knowledge states[J]. Information Sciences, 2011, 181(8): 1426-1439.
[15] Carvalho L, Santos I P, Van Winden W. Knowledge spaces and places: From the perspective of a “born-global” start-up in the field of urban technology[J]. Expert Systems with Applications, 2014, 41(12): 5647-5655.
[16] Gilbert N, Ahrweiler P, Pyka A. Learning in innovation networks: Some simulation experiments[J]. Physica A: Statistical Mechanics and its Applications, 2007, 378: 100-109.
[17] Dawkins R. The selfish gene[M]. Oxford: Oxford University Press, 1976.
[18] 赵健宇,李柏洲.对企业知识创造类生物现象及知识基因论的再思考[J].科学学与科学技术管理, 2014, 35(8): 18-28.
[19] Ziman J. Technological innovation as an evolutionary process[M]. Cambridge: Cambridge University Press, 2000.
[20] Grebel T. Technological change: A microeconomic approach to the creation of knowledge[J]. Structural Change and Economic Dynamic, 2009, 20(4): 301-312.
[21] Lazer D, Friedman A. The network structure of exploration and exploitation[J]. Administrative Science Quarterly, 2007, 52(4): 667-694.
[22] Lopolito A, Morone P, Taylor R. Emerging innovation niches: An agent based model[J]. Research Policy, 2013, 42(6-7): 1225-1238.
[23] Das T K, Teng B. The dynamics of alliance conditions in the alliance development process[J]. Journal of Management Studies, 2002, 39(5): 725-746.
[24] Amaldoss W, Staelin R. Cross-function and aame-function alliances: How does alliance structure affect the behavior of partnering firms? [J]. Management Science, 2010, 56(2): 302-317.
[25] Lavie D, Rosenkopf L. Balancing exploration and exploitation in alliance formation[J]. Academy of Management Journal, 2006, 49(4): 797-818.
[26] Morone P, Taylor R. Knowledge diffusion dynamics and network properties of face-to-face interactions[J]. Journal of Evolutionary Economics, 2004, 14(3): 327-351.
[27] Zeppini P, Frenken K, Kupers R. Thresholds models of technological transitions [J]. Environmental Innovation and Societal Transitions, 2014, 11: 54-70.
[28] 袭希,孙冰.群落演替观点下产业技术的演化模型[J].系统管理学报, 2015, 24(4): 569-579.
[29] Kim H, Park Y T. Structural effects of R&D collaboration network on knowledge diffusion performance[J]. Expert Systems with Applications, 2009, 36(5): 8986-8992.
[30] Ahuja G, Katila R. Technological acquisition and the innovation performance of acquiring firms: A longitudinal study[J]. Strategic Management Journal, 2001, 22(3): 3197-3220.
[31] 赵健宇,袭希,苏屹.知识流动网络演化中小世界效应的仿真研究[J].管理评论, 2015, 27(5): 70-81.
[32] Sornette D. Stock market speculation: Spontaneous symmetry breaking of economic valuation[J]. Physica A: Statistical Mechanics and Its Applications, 2000, 284(1-4): 355-375.
[33] Lee C, Kim J, Kwon O, et al. Stochastic technology life cycle analysis using multiple patent indicators[J]. Technological Forecasting and Social Change, 2016, 106: 53-64.
[34] Gnyawali D R, Park B-J. Co-opetition between giants: Collaboration with competitors for technological innovation[J]. Research Policy, 2011, 40(5): 650-663.
[35] Dussauge P, Garrette B, Mitchell W. Learning from competing partners: Outcomes and durations of scale and link alliances in Europe, North America and Asia[J]. Strategic Management Journal, 2000, 21(2): 99-126.
[36] Frankort H T W. When does knowledge acquisition in R&D alliances increase new product development? The moderating roles of technological relatedness and product-market competition[J]. Research Policy, 2016, 45(1): 291-302.
[37] Inkpen A C, Beamish P W. Knowledge, bargaining power and the instability of international joint ventures[J]. Academy of Management Review, 1997, 22(1): 177-202.
[38] 孙耀吾, 卫英平.高新技术企业联盟知识扩散研究—基于小世界网络的视角[J].管理科学学报, 2011, 14(12): 17-26.
[39] 梁有明,刘成林,刘飞.异质性多资助主体系统的异质性[J].系统工程学报, 2012, 27(5): 583-592.
[40] Wu C Y, Mathews J A. Knowledge flow the solar photovoltaic industry: Insights from patenting by Taiwan, Korea and China[J]. Research Policy, 2012, 41(3): 524-540.
[41] Inkpen A C, Tsang E W K. Social capital, networks and knowledge transfer[J].Academy of Management Review, 2005, 30(1): 146-165.
[42] 赵健宇.知识创造行为对知识网络演化的影响—以知识贬值和知识活性为参数[J].系统管理学报, 2016, 25(1): 175-184.
[43] Das T K, Teng B-S. A risk perception model of alliance structuring[J].Journal of International Management, 2001, 7(1): 1-29.
[2] Jiang X, Bao Y C, Xie Y, et al. Partner trustworthiness, knowledge flow in strategic alliances, and firm competitiveness: A contingency perspective[J]. Journal of Business Research, 2016, 69(2): 804-814.
[3] Holland J H. Hidden order: How adaptation builds complexity[M]. New York: Basic Books, 1995.
[4] Sherif K, Xing B. Adaptive processes for knowledge creation in complex systems: The case of a global IT consulting firm [J]. Information and Management, 2006, 43(4): 530-540.
[5] 王小磊,杨育,曾强,等.客户协同创新的复杂性及主体刺激—反应模型[J].科学学研究, 2009, 27(11): 1729-1735.
[6] Popoveniuc B. Self-reflexivity. The ultimate end of knowledge [J]. Procedia-Social and Behavioral Sciences, 2014, 163: 2014-213.
[7] 田钢,张永安,兰卫国.基于刺激—反应模型的集群创新网络形成机理研究[J].管理评论, 2009, 21(7): 49-55.
[8] Huang Y-P, Chang Y-T, Hsieh S-L, et al. An adaptive knowledge evolution strategy for finding near-optimal solutions of specific problems[J]. Expert Systems with Applications, 2011, 38(4): 3806-3818.
[9] Valverde-Albacete F J, González-Calabozo J M, Peňas A, et al. Supporting scientific knowledge discovery with extended, generalized formal concept analysis [J]. Expert Systems with Applications, 2016, 44: 196-216.
[10] 孙冰,袭希,姚洪涛,等.技术在环境中适应性演化的“刺激-反应”三层模型研究[J].科学学与科学技术管理, 2013, 34(4): 19-27.
[11] Chen L, Fong P S W. Evaluation of knowledge management performance: An organic approach[J]. Information & Management, 2015, 52(4): 431-453.
[12] Nonaka I, Konno N. The concept of ba: Building a foundation for knowledge creation[J]. California Management Review, 1998, 40(3): 40-54.
[13] Doignon J-P, Falmagne J-C L. Knowledge spaces[M]. Springer-Verlag, 1999.
[14] Barti E, Belohlavek R. Knowledge spaces with graded knowledge states[J]. Information Sciences, 2011, 181(8): 1426-1439.
[15] Carvalho L, Santos I P, Van Winden W. Knowledge spaces and places: From the perspective of a “born-global” start-up in the field of urban technology[J]. Expert Systems with Applications, 2014, 41(12): 5647-5655.
[16] Gilbert N, Ahrweiler P, Pyka A. Learning in innovation networks: Some simulation experiments[J]. Physica A: Statistical Mechanics and its Applications, 2007, 378: 100-109.
[17] Dawkins R. The selfish gene[M]. Oxford: Oxford University Press, 1976.
[18] 赵健宇,李柏洲.对企业知识创造类生物现象及知识基因论的再思考[J].科学学与科学技术管理, 2014, 35(8): 18-28.
[19] Ziman J. Technological innovation as an evolutionary process[M]. Cambridge: Cambridge University Press, 2000.
[20] Grebel T. Technological change: A microeconomic approach to the creation of knowledge[J]. Structural Change and Economic Dynamic, 2009, 20(4): 301-312.
[21] Lazer D, Friedman A. The network structure of exploration and exploitation[J]. Administrative Science Quarterly, 2007, 52(4): 667-694.
[22] Lopolito A, Morone P, Taylor R. Emerging innovation niches: An agent based model[J]. Research Policy, 2013, 42(6-7): 1225-1238.
[23] Das T K, Teng B. The dynamics of alliance conditions in the alliance development process[J]. Journal of Management Studies, 2002, 39(5): 725-746.
[24] Amaldoss W, Staelin R. Cross-function and aame-function alliances: How does alliance structure affect the behavior of partnering firms? [J]. Management Science, 2010, 56(2): 302-317.
[25] Lavie D, Rosenkopf L. Balancing exploration and exploitation in alliance formation[J]. Academy of Management Journal, 2006, 49(4): 797-818.
[26] Morone P, Taylor R. Knowledge diffusion dynamics and network properties of face-to-face interactions[J]. Journal of Evolutionary Economics, 2004, 14(3): 327-351.
[27] Zeppini P, Frenken K, Kupers R. Thresholds models of technological transitions [J]. Environmental Innovation and Societal Transitions, 2014, 11: 54-70.
[28] 袭希,孙冰.群落演替观点下产业技术的演化模型[J].系统管理学报, 2015, 24(4): 569-579.
[29] Kim H, Park Y T. Structural effects of R&D collaboration network on knowledge diffusion performance[J]. Expert Systems with Applications, 2009, 36(5): 8986-8992.
[30] Ahuja G, Katila R. Technological acquisition and the innovation performance of acquiring firms: A longitudinal study[J]. Strategic Management Journal, 2001, 22(3): 3197-3220.
[31] 赵健宇,袭希,苏屹.知识流动网络演化中小世界效应的仿真研究[J].管理评论, 2015, 27(5): 70-81.
[32] Sornette D. Stock market speculation: Spontaneous symmetry breaking of economic valuation[J]. Physica A: Statistical Mechanics and Its Applications, 2000, 284(1-4): 355-375.
[33] Lee C, Kim J, Kwon O, et al. Stochastic technology life cycle analysis using multiple patent indicators[J]. Technological Forecasting and Social Change, 2016, 106: 53-64.
[34] Gnyawali D R, Park B-J. Co-opetition between giants: Collaboration with competitors for technological innovation[J]. Research Policy, 2011, 40(5): 650-663.
[35] Dussauge P, Garrette B, Mitchell W. Learning from competing partners: Outcomes and durations of scale and link alliances in Europe, North America and Asia[J]. Strategic Management Journal, 2000, 21(2): 99-126.
[36] Frankort H T W. When does knowledge acquisition in R&D alliances increase new product development? The moderating roles of technological relatedness and product-market competition[J]. Research Policy, 2016, 45(1): 291-302.
[37] Inkpen A C, Beamish P W. Knowledge, bargaining power and the instability of international joint ventures[J]. Academy of Management Review, 1997, 22(1): 177-202.
[38] 孙耀吾, 卫英平.高新技术企业联盟知识扩散研究—基于小世界网络的视角[J].管理科学学报, 2011, 14(12): 17-26.
[39] 梁有明,刘成林,刘飞.异质性多资助主体系统的异质性[J].系统工程学报, 2012, 27(5): 583-592.
[40] Wu C Y, Mathews J A. Knowledge flow the solar photovoltaic industry: Insights from patenting by Taiwan, Korea and China[J]. Research Policy, 2012, 41(3): 524-540.
[41] Inkpen A C, Tsang E W K. Social capital, networks and knowledge transfer[J].Academy of Management Review, 2005, 30(1): 146-165.
[42] 赵健宇.知识创造行为对知识网络演化的影响—以知识贬值和知识活性为参数[J].系统管理学报, 2016, 25(1): 175-184.
[43] Das T K, Teng B-S. A risk perception model of alliance structuring[J].Journal of International Management, 2001, 7(1): 1-29.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |