Difference between revisions of "Complexity of Commerce"
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'''SFI ACtioN <br />'''
Latest revision as of 15:04, 26 August 2019
SFI ACtioN Roundtable
September 12, 2019
Shopify Kit Office
33 New Montgomery St #750
San Francisco, CA 95104
SFI will explore the nature of physical and e-commerce in a special ACtioN roundtable discussion on the Complexity of Commerce. Key ideas we plan to explore include:
- Complexity science, and in particular network theory, provides key insights into the retail sector’s current disruption by exploring how the material and the digital world are combining.
- Theories of increasing returns, preferential attachment, and first mover effects all derive from ‘network effects’ which promise to provide practical insights into the future of commerce.
- Network induced scaling effects also appear to impact the mortality of firms and the distribution of company types in urban economies - offering clues as to how the aforementioned changes in commercial industrial structure might manifest .
- Furthermore, social networks play an important role in moderating the evolution of tastes through peer-groups, and conformity, and infectivity.
Increasing returns and network effects have frequently been invoked to explain the highly concentrated organizational structure of technology related-industries (see for example Arthur 1996). Similar arguments have been used to explain the disproportionate market shares of e-commerce giants. With the growing importance of third-party algorithms that exploit data from both consumers and merchants, there is speculation that the economics of increasing returns will continue to concentrate the retail sector into a small number of powerful merchants (see Gibbs & Harrison 2019).
Research on social networks provides a number of key strategic insights relevant to the commerce. In the science of cities, social networks emerge as a universal generative mechanisms that generate scaling effects - for example doubling the size of a city is correlated with an approximately 15% increase in a host of per capita socio-economic indicators such as GDP, innovation, and social crime. (West 2017 provides a through summary of work in this field.) Given the rapid rate of urbanization (2% global annual increase, ~1% US annual increase), these predictable and repeatable socio-economic changes will have significant impact on commerce.
Relatedly, urban scaling theory also predicts a universal frequency distribution of firm diversity within cities of all sizes (see Youn, et al. 2016). Thus while firm composition might change, universal patterns of abundance remain unchanged. Against the backdrop of significant retail store closures this work suggests mechanisms for diversification and potential resilience.
Social networks also influence our understanding of innovation in both fashion and retail business models. Work by Salganik et al. (2006) demonstrates how small initial differences in perceived peer approval can result in significant differences in ultimate the popularity of songs, in stance of the network dynamics described as preferential attachment. Coupled with more recent work on the propagation of influence over social networks, these mechanisms provide a deeper understanding of emergent fashion trends (See for example Klimek, Kreuzbauer, and Thurner 2019). Relatedly, work by Padgett and Powell (2012) demonstrates how interactions across different networks stimulate the development of business innovation. And how a theory of overlapping networks is required to understand centers of innovation.
Mathematical work on network structure can also be applied to understand the trade-offs between retail locations and distribution centers. As the distinction between on-line and brick-and-mortar distributors continue to blur, questions related to geographic trade-off are becoming more pressing. These techniques could also help add empirical rigor to local-market positions which many argue is a key factor in retail profitability (see for example Greenwald & Kahn 2005).
Additional computational tools related to complexity science, such as agent based modeling, can help explore changes that are accompanying new levels of automation in retail, such as why Amazon’s random warehouse sorting system out-performs traditional similarity-based sorting system.
And looking further afield we can take inspiration from models of growth and extinction in the ecological domain, in particular models of seed dispersion that could be applied to analyze a retailer’s footprint and potential expansion strategy. Work using models from physics and biology to characterize the lifespan of firms, can also be used to benchmark and contextualize recent waves of retail closures and bankruptcies (see Daepp et al. 2015).
SFI will begin exploring these topics in a 2019 ACtioN Topical Meeting on the Complexity of Commerce.
Arthur, W.B., 1996. Increasing returns and the new world of business. Harvard Business Review, 74(4).
Daepp, M.I., Hamilton, M.J., West, G.B. and Bettencourt, L.M., 2015. The mortality of companies. Journal of The Royal Society Interface, 12(106).
Gibbs, B., & Harrison N., 2019. How retail changes when algorithms curate everything we buy. Harvard Business Review, online pre-print.
Greenwald, B. and Kahn, J., 2005. All strategy is local. Harvard Business Review, 83(9), pp.94-104.
Klimek, P., Kreuzbauer, R. and Thurner, S., 2019. Fashion and art cycles are driven by counter-dominance signals of elite competition: quantitative evidence from music styles. Journal of the Royal Society Interface, 16(151), p.20180731.
Padgett, J.F. and Powell, W.W., 2012. The emergence of organizations and markets. Princeton University Press.
Salganik, M.J., Dodds, P.S. and Watts, D.J., 2006. Experimental study of inequality and unpredictability in an artificial cultural market. Science, 311(5762), pp.854-856.