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Social culture and innovation diffusion: a theoretically founded agent-based model

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Abstract

This study proposes an agent-based model to theoretically investigate the effects of social culture on innovation diffusion. The model assumes that social culture (i.e., individualism, power distance, and uncertainty avoidance from Hofstede’s cultural dimension theory) has a direct effect on the small-world network structure and individual characteristics. We further explore how the characteristics of innovation influence the diffusion process. We find that individualism has a positive effect on the diffusion speed in the early stage, whereas uncertainty avoidance and power distance have negative effects on innovation diffusion. The effect of uncertainty avoidance on the diffusion speed turns positive after the early stage of diffusion and the negative effect of power distance becomes positive in the late stage. We compare real-world diffusion data with the proposed agent-based model, finding some similarities in the diffusion patterns. The characteristics of innovation affect innovation diffusion when the uncertainty avoidance is high. However, when both uncertainty avoidance and individualism are low, the effect of the characteristics of an innovation on diffusion is restricted.

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References

  • Adler NJ, Gundersen A (2008) International dimensions of organizational behavior. Thomson/South-Western, Mason

    Google Scholar 

  • Bass FM (1969) A new product growth for model consumer durables. Manag Sci 15:215–227

    Google Scholar 

  • Belk RW (1988) Possessions and the extended self. J Cons Res 15:139–168

    Google Scholar 

  • Bohlmann JD, Calantone RJ, Zhao M (2010) The effects of market network heterogeneity on innovation diffusion: an agent-based modeling approach. J Prod Innovat Manag 27:741–760

    Google Scholar 

  • Broekhuizen TL, Delre SA, Torres A (2011) Simulating the cinema market: how cross-cultural differences in social influence explain box office distributions. J Prod Innovat Manag 28:204–217

    Google Scholar 

  • Burt RS (1987) Social contagion and innovation: cohesion versus structural equivalence. Amer J Sociol 92:1287–1335

    Google Scholar 

  • Burton-Jones A (1999) Knowledge capitalism: business, work, and learning in the new economy. Oxford University Press, Oxford

    Google Scholar 

  • Chandrasekaran D, Tellis GJ (2007) A critical review of marketing research on diffusion of new products. In: Malhorta NK (ed) Review of marketing research, vol 3. Emerald Group publishing limited, Bingley, pp 39–80

    Google Scholar 

  • Chao A, Schor JB (1998) Empirical tests of status consumption: evidence from women’s cosmetics. J Econ Psych 19:107–131

    Google Scholar 

  • Chircu AM, Mahajan V (2009) Perspective: revisiting the digital divide: an analysis of mobile technology depth and service breadth in the BRIC countries. J Prod Innovat Manag 26:455–466

    Google Scholar 

  • Cialdini RB, Goldstein NJ (2004) Social influence: compliance and conformity. Annu Rev Psychol 55:591–621

    Google Scholar 

  • Clark AE, Oswald AJ (1994) Unhappiness and unemployment. Econ J 104:648–659

    Google Scholar 

  • Cowan R, Jonard N (2004) Network structure and the diffusion of knowledge. J Econ Dynam Control 28:1557–1575

    Google Scholar 

  • Cowan R, Cowan W, Peter Swann G (2004) Waves in consumption with interdependence among consumers. Can J Econ 37:149–177

    Google Scholar 

  • Dawar N, Parker P (1994) Marketing universals: consumers’ use of brand name, price, physical appearance, and retailer reputation as signals of product quality. J Marketing 58:81–95

    Google Scholar 

  • De Mooij M (2004) Consumer behavior and culture: consequences for global marketing and advertising. Sage, Thousand Oaks

    Google Scholar 

  • Deffuant G, Huet S, Amblard F (2005) An individual-based model of innovation diffusion mixing social value and individual benefit. Amer J Sociol 110:1041–1069

    Google Scholar 

  • Delre SA, Jager W, Janssen MA (2007) Diffusion dynamics in small-world networks with heterogeneous consumers. Comput Math Organ Th 13:185–202

    Google Scholar 

  • Desiraju R, Nair H, Chintagunta P (2004) Diffusion of new pharmaceutical drugs in developing and developed nations. Int J Res Mark 21:341–357

    Google Scholar 

  • Desmarchelier B, Fang ES (2016) National culture and innovation diffusion. Exploratory insights from agent-based modeling. Technol Forecast Soc 105:121–128

    Google Scholar 

  • Duesenberry JS (1949) Income, saving, and the theory of consumer behavior. Harvard University Press, Cambridge

    Google Scholar 

  • Dwyer S, Mesak H, Hsu M (2005) An exploratory examination of the influence of national culture on cross-national product diffusion. J Int Mark 13:1–27

    Google Scholar 

  • Evans DS (2009) The online advertising industry: economics, evolution, and privacy. J Econ Perspect 23:37–60

    Google Scholar 

  • Faber A, Valente M, Janssen P (2010) Exploring domestic micro-cogeneration in the Netherlands: an agent-based demand model for technology diffusion. Energy Policy 38:2763–2775

    Google Scholar 

  • Ganesh J (1998) Converging trends within the European Union: insights from an analysis of diffusion patterns. J Int Mark 6:32–48

    Google Scholar 

  • Ganesh J, Kumar V, Subramaniam V (1997) Cross-national learning effects in global diffusion patterns: an exploratory investigation. J Acad Market Sci 25:214–228

    Google Scholar 

  • Gastwirth JL (1972) The estimation of the Lorenz curve and Gini index. Rev Econ Stat 54:306–316

    Google Scholar 

  • Gatignon H, Robertson TS (1985) A propositional inventory for new diffusion research. J Cons Res 11:849–867

    Google Scholar 

  • Gatignon H, Eliashberg J, Robertson TS (1989) Modeling multinational diffusion patterns: an efficient methodology. Market Sci 8:231–247

    Google Scholar 

  • Gilbert N (1997) A simulation of the structure of academic science. Sociol Res Online 2:1–15

    Google Scholar 

  • Goldenberg J, Libai B, Muller E (2001) Talk of the network: a complex systems look at the underlying process of word-of-mouth. Mark Lett 12:211–223

    Google Scholar 

  • Granovetter M (1978) Threshold models of collective behavior. Amer J Sociol 83:1420–1443

    Google Scholar 

  • Helsen K, Jedidi K, DeSarbo WS (1993) A new approach to country segmentation utilizing multinational diffusion patterns. J Marketing 57:60–71

    Google Scholar 

  • Hofstede G (1980) Culture’s consequences: international differences in work-related values. Sage, London

    Google Scholar 

  • Hofstede G (1984) Cultural dimensions in management and planning. Asia Pac J Manag 1:81–99

    Google Scholar 

  • Hofstede G (2001) Culture’s consequences: comparing values, behaviors, institutions, and organizations across cultures. Sage, Thousand Oaks

    Google Scholar 

  • Hofstede G, Bond MH (1988) The Confucius connection: from cultural roots to economic growth. Organ Dyn 16:5–21

    Google Scholar 

  • Hofstede G, Hofstede GJ, Minkov M (2010) Cultures and organizations: software of the mind: intercultural cooperation and its importance for survival. McGraw-Hill, New York

    Google Scholar 

  • House R, Javidan M, Hanges P, Dorfman P (2002) Understanding cultures and implicit leadership theories across the globe: an introduction to project GLOBE. J World Bus 37:3–10

    Google Scholar 

  • Iyengar R, Koenigsberg O, Muller E (2009) The effects of uncertainty on mobile phone package size and subscribers choice. Columbia University Working Paper

  • Jager W (2000) Modelling consumer behaviour. University of Groningen, Groningen

    Google Scholar 

  • Jain J, Maesincee S (1997) Cultural influences on new product acceptance in global markets. Monash Mt Eliza Bus Rev 1:62–71

    Google Scholar 

  • Janssen MA, Jager W (2001) Fashions, habits and changing preferences: simulation of psychological factors affecting market dynamics. J Econ Psych 22:745–772

    Google Scholar 

  • Janssen MA, Jager W (2002) Stimulating diffusion of green products. J Evol Econ 12:283–306

    Google Scholar 

  • Kacen JJ, Lee JA (2002) The influence of culture on consumer impulsive buying behavior. J Consum Psychol 12:163–176

    Google Scholar 

  • Katz ML, Shapiro C (1985) Network externalities, competition, and network effects. Amer Econ Rev 75:424–440

    Google Scholar 

  • Kaufmann P, Stagl S, Franks DW (2009) Simulating the diffusion of organic farming practices in two new EU member states. Ecol Econ 68:2580–2593

    Google Scholar 

  • Kiesling E, Günther M, Stummer C, Wakolbinger LM (2012) Agent-based simulation of innovation diffusion: a review. Central Europ J Operations Res 20:183–230

    Google Scholar 

  • Kim UE, Triandis HC, Kâğitçibaşi ÇE, Choi S-CE, Yoon GE (1994) Individualism and collectivism: theory, method, and applications. Sage Publications, Thousand Oaks

    Google Scholar 

  • Kirkman BL, Lowe KB, Gibson CB (2006) A quarter century of culture's consequences: a review of empirical research incorporating Hofstede’s cultural values framework. J Int Bus Stud 37:285–320

    Google Scholar 

  • Kluckhohn CE, Murray HA, Schneider DM (1953) Personality in nature, society, and culture. Knopf, New York

    Google Scholar 

  • Lindberg BC (1982) International comparison of growth in demand for a new durable consumer product. J Mark Res 19:364–371

    Google Scholar 

  • Loch KD, Straub DW, Kamel S (2003) Diffusing the internet in the Arab world: the role of social norms and technological culturation. IEEE Trans Eng Manag 50:45–63

    Google Scholar 

  • Mahajan V, Muller E (1994) Will the 1992 unification of the European Community accelerate diffusion of new ideas, products, and technologies? Technol Forecast Soc 45:221–235

    Google Scholar 

  • Mahajan V, Muller E, Wind J (2000) New-product diffusion models. Kluwer Academic Publishers, New York

    Google Scholar 

  • Mahler A, Rogers EM (1999) The diffusion of interactive communication innovations and the critical mass: the adoption of telecommunications services by German banks. Telecommun Pol 23:719–740

    Google Scholar 

  • McCoy D, Lyons S (2014) Consumer preferences and the influence of networks in electric vehicle diffusion: an agent-based microsimulation in Ireland. Energ Resear Soc Sci 3:89–101

    Google Scholar 

  • McIntyre E, Virzi AM (2018) CMO spend survey 2018–2019. Gartner. https://www.gartner.com/en/marketing/insights/articles/8-top-findings-in-gartner-cmo-spend-survey-2018-19. Accessed 15 Jan 2020

  • Meade N, Islam T (2006) Modelling and forecasting the diffusion of innovation: a 25-year review. Int J Forecast 22:519–545

    Google Scholar 

  • Milgram S (1967) The small world problem. Psychol Today 2:60–67

    Google Scholar 

  • Morgan J (1962) The anatomy of income distribution. Rev Econ State 44:270–283

    Google Scholar 

  • Mourali M, Laroche M, Pons F (2005) Individualistic orientation and consumer susceptibility to interpersonal influence. J Serv Mark 19:164–173

    Google Scholar 

  • Neumark D, Postlewaite A (1998) Relative income concerns and the rise in married women's employment. J Public Econ 70:157–183

    Google Scholar 

  • Newman ME (2000) Small worlds: the structure of social networks. Santa Fe Institute, Santa Fe

    Google Scholar 

  • Palmer J, Sorda G, Madlener R (2015) Modeling the diffusion of residential photovoltaic systems in Italy: an agent-based simulation. Technol Forecast Soc 99:106–131

    Google Scholar 

  • Peres R, Muller E, Mahajan V (2010) Innovation diffusion and new product growth models: a critical review and research directions. Int J Res Mark 27:91–106

    Google Scholar 

  • Rogers EM (2010) Diffusion of innovations, 4th edn. The Free Press, New York

    Google Scholar 

  • Roozmand O, Ghasem-Aghaee N, Hofstede GJ, Nematbakhsh MA, Baraani A, Verwaart T (2011) Agent-based modeling of consumer decision making process based on power distance and personality. Knowl Based Syst 24:1075–1095

    Google Scholar 

  • Roth MS (1995) The effects of culture and socioeconomics on the performance of global brand image strategies. J Mark Res 32:163–175

    Google Scholar 

  • Schumpeter J (1928) The instability of capitalism. Econ J 38:361–386

    Google Scholar 

  • Schumpeter J (1934) The theory of economic development. Harvard University Press, Cambridge

    Google Scholar 

  • Schumpeter JA (1939) Business cycles: a theoretical, historical, and statistical analysis of the capitalist process. McGraw-Hill, New York

    Google Scholar 

  • Schwarz N, Ernst A (2009) Agent-based modeling of the diffusion of environmental innovations: an empirical approach. Technol Forecast Soc 76:497–511

    Google Scholar 

  • Sivakumar K, Nakata C (2001) The stampede toward Hofstede’s framework: avoiding the sample design pit in cross-cultural research. J Int Bus Stud 32:555–574

    Google Scholar 

  • Śledzik K (2013) Schumpeter’s view on innovation and entrepreneurship. In: Hittmar S (ed) Management trends in theory and practice. University of Zilina & Institute of Management by University of Zilina, Zilina, pp 89–95

    Google Scholar 

  • Sopha BM, Klöckner CA, Hertwich EG (2013) Adoption and diffusion of heating systems in Norway: coupling agent-based modeling with empirical research. Environ Innov Soc Trans 8:42–61

    Google Scholar 

  • Steenkamp J-BE, Hofstede F, Wedel M (1999) A cross-national investigation into the individual and national cultural antecedents of consumer innovativeness. J Mark 63:55–69

    Google Scholar 

  • Takada H, Jain D (1991) Cross-national analysis of diffusion of consumer durable goods in Pacific rim countries. J Mark 55:48–54

    Google Scholar 

  • Tellis GJ, Stremersch S, Yin E (2003) The international takeoff of new products: the role of economics, culture, and country innovativeness. Market Sci 22:188–208

    Google Scholar 

  • Tian M, Deng P, Zhang Y, Salmador MP (2018) How does culture influence innovation? A systematic literature review. Manag Decis 56:1088–1107

    Google Scholar 

  • Triandis HC (1995) Individualism and collectivism. Westview Press, Boulder

    Google Scholar 

  • Valente TW, Davis RL (1999) Accelerating the diffusion of innovations using opinion leaders. Ann Am Acad Pol Soc Sci 566:55–67

    Google Scholar 

  • Valente TW, Rogers EM (1995) The origins and development of the diffusion of innovations paradigm as an example of scientific growth. Sci Commun 16:242–273

    Google Scholar 

  • Van den Bulte C, Joshi YV (2007) New product diffusion with influentials and imitators. Market Sci 26:400–421

    Google Scholar 

  • Van den Bulte C, Stremersch S (2004) Social contagion and income heterogeneity in new product diffusion: a meta-analytic test. Market Sci 23:530–544

    Google Scholar 

  • Van den Bulte C, Wuyts SHK (2007) Social networks in marketing. Marketing Science Institute, Cambridge

    Google Scholar 

  • Van Eck PS, Jager W, Leeflang PS (2011) Opinion leaders’ role in innovation diffusion: a simulation study. J Prod Innovat Manag 28:187–203

    Google Scholar 

  • van Eeden E, Chow W (2019) Global Entertainment & Media Outlook 2019–2023. PwC. https://www.pwc.com/us/en/industries/tmt/library/global-entertainment-media-outlook.html. Accessed 15 Jan 2020

  • Veblen T (1934) The theory of the leisure class. The Viking Press, New York

    Google Scholar 

  • Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393:440–442

    Google Scholar 

  • Wolf I, Nuss J, Schröder T, de Haan G (2012) The adoption of electric vehicles: an empirical agent-based model of attitude formation and change. Proceedings of the 8th conference of the European Association for Social Simulation, pp 93–98

  • Xiong H, Payne D, Kinsella S (2016) Peer effects in the diffusion of innovations: theory and simulation. J Behav Exp Econ 63:1–13

    Google Scholar 

  • Yalcinkaya G (2008) A culture-based approach to understanding the adoption and diffusion of new products across countries. Int Market Rev 25:202–214

    Google Scholar 

  • Yaveroglu IS, Donthu N (2002) Cultural influences on the diffusion of new products. J Int Consum Mark 14:49–63

  • Yeniyurt S, Townsend J (2003) Does culture explain acceptance of new products in a country? An empirical investigation. Int Mark Rev 20:377–396

  • Zhang H, Vorobeychik Y (2019) Empirically grounded agent-based models of innovation diffusion: a critical review. Artif Interll Rev 52:707–741

    Google Scholar 

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Acknowledgements

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Declarations of interest: none.

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  1. Technology Management, Economics, and Policy Program, College of Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea

    Meihan He & Jongsu Lee

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  1. Meihan He
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Appendices

Appendix A

Parameters and variables in the proposed model are shown in Table 5.

Table 5 Parameters and variables in the proposed model
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Input values in simulations are shown in Table 6.

Table 6 Input values in simulations
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Appendix B

We adjusted simulation time from 10,000 to 100,000. As shown in Fig. 11, the system with 10,000 iterations is large enough to identify the whole diffusion pattern with N = 1000. Population number is adjusted from N = 1000, N = 5000, N = 10,000 with simulation time t = 10,000, t = 50,000 and t = 100,000, respectively. Table 7 shows the proportion of averaged final adoption level and standard deviation. Average final adoption proportion does not change with the population number. We further adjust average number of linked neighbors k from 3 to 20 with population number N = 1000. As shown in Table 8, the system converges to a steady state with k larger than 8, and the increase in k does not reduce the standard deviation.

Fig. 11
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Sensitivity to Iteration Time

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Table 7 Sensitivity to Number of Population
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Table 8 Sensitivity to Number of Average Links
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Appendix C

Standard deviation of simulations of Fig. 4, Fig.5 and Fig. 9 are shown in Table 9, Table 10 and Table 11, respectively.

Table 9 Standard deviation of simulations of Fig. 4
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Table 10 Standard deviation of simulation of Fig. 5
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Table 11 Standard deviation of simulation of Fig. 9
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He, M., Lee, J. Social culture and innovation diffusion: a theoretically founded agent-based model. J Evol Econ 30, 1109–1149 (2020). https://doi.org/10.1007/s00191-020-00665-9

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