Complex adaptive system theory, agent-based modeling, and simulation in dominant technology formation

doi:10.23919/JSEE.2023.000160

Abstract

Abstract:

Dominant technology formation is the key for the high-tech industry to “cross the chasm” and gain an established foothold in the market (and hence disrupt the regime). Therefore, a stimulus-response model is proposed to investigate the dominant technology by exploring its formation process and mechanism. Specifically, based on complex adaptive system theory and the basic stimulus-response model, we use a combination of agent-based modeling and system dynamics modeling to capture the interactions between dominant technology and the socio-technical landscape. The results indicate the following: (i) The dynamic interaction is “stimulus-reaction-selection”, which promotes the dominant technology ’s formation. (ii) The dominant technology’s formation can be described as a dynamic process in which the adaptation intensity of technology standards increases continuously until it becomes the leading technology under the dual action of internal and external mechanisms. (iii) The dominant technology’s formation in the high-tech industry is influenced by learning ability, the number of adopting users and adaptability. Therein, a “critical scale” of learning ability exists to promote the formation of leading technology: a large number of adopting users can promote the dominant technology’s formation by influencing the adaptive response of technology standards to the socio-technical landscape and the choice of technology standards by the socio-technical landscape. There is a minimum threshold and a maximum threshold for the role of adaptability in the dominant technology ’s formation. (iv) The socio-technical landscape can promote the leading technology’s shaping in the high-tech industry, and different elements have different effects. This study promotes research on the formation mechanism of dominant technology in the high-tech industry, presents new perspectives and methods for researchers, and provides essential enlightenment for managers to formulate technology strategies.

Key words: complex adaptive system theory, agent-based modeling and simulation, dominant technology, socio-technical landscape, adaptation-choice

Ruihan ZHANG, Bing SUN. Complex adaptive system theory, agent-based modeling, and simulation in dominant technology formation[J]. Journal of Systems Engineering and Electronics, 2024, 35(1): 130-153.

Figures/Tables 20

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Table 1

Main equations of variable relationships"

Number	Equation	Unit	Category
1	Number of 5G mobile phone users = INTEG (new increment of 5G mobile phone users, the initial number of 5G mobile phone users)	Ten thousand	Level variable
2	New increment of 5G mobile phone users= annual output of 5G mobile phone users × consumption intention of 5G mobile phone users	10000/year	Rate variable
3	Consumption intention of 5G mobile phone users = price influence of 5G traffic + word-of-mouth influence + advertising influence + price influence of 5G mobile phone users	Dmnl	Auxiliary variable
4	Total benefit of 5G mobile phone users = (purchase price-production cost) × the number of 5G mobile phone users	Ten thousand yuan	Auxiliary variable
5	Cumulative investment in the 5G mobile phone user industry chain = INTEG (total benefit of 5G mobile phone users × production reinvestment coefficient of manufacturer’s profit + government subsidies × distribution coefficient of manufacturer’s financial subsidy, cumulative investment in the initial 5G mobile phone user industry chain)	Ten thousand yuan	Level variable
6	Research and development investment = cumulative investment in the 5G mobile phone user industry chain × research and development investment coefficient	Ten thousand yuan	Auxiliary variable
7	Technology level = research and development investment/research and development investment transformation	Individual	Auxiliary variable
8	Infrastructure perfection coefficient = number of 5G mobile phone users/number of 5G base stations	Individual	Auxiliary variable
9	Per capita disposable income = gross domestic product/populations	10000 yuan/person	Auxiliary variable
10	Total gross domestic product = INTEG (gross domestic product growth, initial gross domestic product)	One hundred million yuan	Level variable
11	Gross domestic product growth = total gross domestic product × annual gross domestic product growth rate	Ten thousand yuan	Rate variable
12	Annual gross domestic product growth rate	Dmnl	Constant
13	Total population = INTEG (population growth, initial population)	100 million people	Level variable
14	Population growth = total population × annual population growth rate	Ten thousand people	Rate variable
15	Annual population growth rate	Dmnl	Constant
16	Number of 5G base stations= INTEG (growth of 5G base stations, number of initial 5G base stations)	Individual	Level variable
17	5G base station increase = infrastructure construction investment × investment conversion	10000 yuan / piece	Rate variable
18	Infrastructure improvement coefficient = number of 5G base stations/number of 5G mobile phone users	Dmnl	Auxiliary variable
19	Product attraction factor = technology level + infrastructure perfection	Dmnl	Auxiliary variable
20	Purchase price = unit production cost × (1 + supply chain profit margin)-government subsidy × consumer financial subsidy distribution coefficient	Ten thousand yuan	Auxiliary variable
21	Supply chain profit margin	Dmnl	Constant
22	Purchase subsidy	Dmnl	Constant
23	5G data price reduction value =5G data price reduction rate ×5G data price	Ten thousand yuan/ year	Rate variable
24	5G data price = INTEG (5G data price reduction value, 5G data price)	Ten thousand yuan	Level variable
25	Distribution coefficient of manufacturer’s financial subsidy	Dmnl	Table functions
26	5G data price reduction rate	Dmnl	Table functions
27	Government subsidy = INTEG (increase rate of government subsidy, government subsidy)	Ten thousand yuan	Level variable
28	Distribution coefficient of consumer financial subsidies	Dmnl	Table functions
29	Increase rate of government subsidies = government subsidies × growth coefficient of government subsidies	Ten thousand yuan/ year	Rate variable
30	Government subsidies or preferential policies	Dmnl	Table functions
31	5G data price impact	Dmnl	Table functions
32	Annual output of 5G mobile phone = production input / unit production cost	Individual	Auxiliary variable
33	Production investment = cumulative investment in 5G mobile phone industrialization × proportion of capital investment for production	Ten thousand yuan	Auxiliary variable
34	5G technology patent application = INTEGER (5G technology patent increase, 5G technology patent application)	Individual	Level variable
35	Increase in 5G technology patents = research and development investment × investment conversion rate + technology level	Per/year	Rate variable
36	5G baseband chip production = INTEG (5G baseband chip added value, 5G baseband chip production)	Ten thousand	Level variable
37	Added value of 5G baseband chip = accumulated investment in 5G mobile phone industrialization × proportion of investment in research and development and production of chip	Ten thousand/year	Rate variable
38	Degree of perfection of 5G network related hardware = 1 / (production of 5G supporting antenna + production of 5G baseband chip)	Dmnl	Auxiliary variable

Table 1

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References 35

1	ABERNATHY W J, UTTERBACK J Patterns of industrial innovation. Technology Review, 1978, 80 (7): 40- 47.
2	SUAREZ F F, UTTERBACK J M Dominant designs and the survival of firms. Strategic Management Journal, 1995, 16 (6): 415- 430. doi: 10.1002/smj.4250160602
3	CHRISTENSE C M, SUAREZ F, UTTERBACK J M Strategies for survival in fast-changing industries. Management Science, 1998, 44 (12): 207- 220.
4	SUAREZ F F Battles for technological dominance: an integrative framework. Research Policy, 2004, 33 (2): 271- 286. doi: 10.1016/j.respol.2003.07.001
5	HOLLAND J H. Hidden order: how adaptation builds complexity. New York: Addison Wesley, 1995.
6	LOPOLITO A, MORONE P, TAYLOR R Emerging innovation niches: an agent based model. Research Policy, 2013, 42 (6/7): 1225- 1238. doi: 10.1016/j.respol.2013.03.002
7	BERGMAN N, HAXELTINE A, WHITMARSH L. Modelling socio-technical transition patterns and pathways. Journal of Artificial Societies & Social Simulation, 2008, 11(3): 29.
8	ZHANG F W The research for the attachment of coal industry’s related technology and leading technology. China Mining Magine, 2010, 19 (6): 41- 43.
9	TUSHAMN M L, ANDERSON P Technological discontinuities and dominant designs. Administrative Science Quarterly, 1990, 35, 604- 633.
10	FERNANDEZ E, VALLE S Battle for dominant design: a decision-making model. European Research on Management & Business Economics, 2019, 25 (2): 72- 78.
11	WU D Y, ZHANG Z J Dominant design: the new paradigm of market entry barriers. East China Economic Management, 2006, 4, 126- 129.
12	LIU H Y, PENG F Y, WU C S. The formation mechanism and growth path of strategic emerging industries. Science & Technology Progress and Policy, 2012, 29(11): 46−49.
13	LU J Z Development direction of agricultural science and technology in China during new era and important proposition analysis. Journal of Agricultural Science and Technology, 2014, 16 (6): 1- 6.
14	CHESBROUGH H Arrested developmen: the experience of European hard disk drive firms in comparison with US and Japanese firms. Evolutionary Economics, 1999, 9, 287- 329.
15	SRINIVASAN R, LILIEN G L, RANGASWAMY A The emergence of dominant designs. Journal of Marketing, 2006, 70 (2): 1- 17.
16	TAN J, XUE H Z The formation mechanism of dominant design and its contributing strategic factors. Chinese Soft Science, 2007, 7, 41- 53.
17	LI D, LIU W, SONG Z Appropriablity strategy, technology flexibility and the emergence of dominant designs: a comparative case study. Science & Technology Progress and Policy, 2019, 36 (11): 1- 8.
18	MIU X M, ZHAO H The roles of government in the evolution of dominant design: study based on China’s telecom industry. Journal of Information, 2008, 11, 155- 158.
19	DAI H, ZENG D, QUALLS J W, et al Do social ties matter for the emergence of dominant design? The moderating roles of technological turbulence and IRP enforcement. Journal of Engineering and Technology Management, 2018, 47, 96- 109.
20	BREM A, NYLUND P, VIARDOT E The impact of the 2008 financial crisis on innovation: a dominant design perspective. Journal of Business Research, 2020, 110, 360- 369.
21	GEELS F W Technological transitions as evolutionary reconfiguration processes: a multi-level perspective and a case study. Research Policy, 2002, 31 (8/9): 1257- 1274. doi: 10.1016/S0048-7333(02)00062-8
22	SMITH A, VOSS J P, GRIN J Innovation studies and sustainability transitions: the allure of the multi-level perspective and its challenges. Research Policy, 2010, 39 (4): 435- 448. doi: 10.1016/j.respol.2010.01.023
23	GEELS F W Disruption and low-carbon system transformation: Progress and new challenges in socio-technical transitions research and the multi-level perspective. Energy Research & Social Science, 2018, 37, 224- 231.
24	RIP A, KEMP R. Technological change. RAYNER S, MALONE E L, ed. Human choice and climate change. Ohio: Battelle Press, 1998.
25	HEKKERT M, HOED R V D Competing technologies and the struggle towards a new dominant design. Greener Management International, 2004, 47 (16): 28- 43.
26	ANDERSON P, TUSHMAN M L Technological discontinuities and dominant designs: a cyclical model of technological change. Administrative Science Quarterly, 1990, 35 (4): 604- 633. doi: 10.2307/2393511
27	BREM A, NYLUND A P, SCHUSTER G Innovation and de facto standardization: the influence of dominant design on innovative performance, radical innovation, and process innovation. Technovation, 2016, 50, 79- 88.
28	MURMANN J P, FRENKEN K Toward a systematic framework for research on dominant designs, technological innovations, and industrial change. Research Policy, 2006, 35, 925- 952.
29	UTTERBACK J M, ABERNATHY W J A dynamic model of process and product innovation. Omega, 1975, 3 (6): 639- 656. doi: 10.1016/0305-0483(75)90068-7
30	BERKELEY N, BAILEY D, JONES A, et al Assessing the transition towards battery electric vehicles: a multilevel perspective on drivers of, and barriers to, take up. Transportation Research Part A Policy & Practice, 2017, 106, 320- 332.
31	HOLLAND J H. Complex adaptive system. Reading: Addison Wesley, 1995.
32	HAURAND M D, STUMMER C Stakes or garlic? Studying the emergence of dominant designs through an agent-based model of a vampire economy. Central European Journal of Operations Research, 2018, 26 (2): 373- 394. doi: 10.1007/s10100-017-0492-9
33	RAND W M, RUST R T Agent-based modeling in marketing: guidelines for rigor. International Journal of Research in Marketing, 2011, 28 (3): 181- 193. doi: 10.1016/j.ijresmar.2011.04.002
34	AHRWEILER P, PYKA A, GILBERT N. Simulating knowledge dynamics in innovation networks (SKIN). LEOMBRUNI R, RICHIARDI M, ed. Industry and labor dynamics: the agent-based computational economics approach. Singapore: World Scientific Press, 2004.
35	MA Q, WU W W, LIU Y X, et al Impact of the synergy between technology management and technological capability on new product development: a system dynamics approach. Journal of Systems Engineering and Electronics, 2022, 33 (1): 105- 119.