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2020 | OriginalPaper | Buchkapitel

11. Disruptive Growth

verfasst von : Richard M. Adler

Erschienen in: Bending the Law of Unintended Consequences

Verlag: Springer International Publishing

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Abstract

This chapter examines the challenges of modeling customer behaviors in critical decisions involving disruptive business growth. The example focuses on decisions to build or join Internet marketplaces for business-to-business (B2B) trading, a topic that was introduced in Chap. 2. Section 11.1 explains the genesis of net markets during the dot-com boom in the late 1990s. Section 11.2 describes our test drive model for critical decisions regarding building or joining net markets, while Sect. 11.3 presents the results from our testing of that solution. Section 11.4 explains why this decision test drive solution is superior to competing analytic approaches.

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Vorheriges Kapitel Competitive Marketing Strategy

Nächstes Kapitel Managing Enterprise Risk

Modeling sales in drug markets is particularly complex because while patients are the ultimate consumers, their purchases are dictated (or mediated) by physicians, hospitals or clinics, and payers.

Slywotzky and Wise [13].

Disruptors today wield digital strategies that leverage internet, big data, and AI technologies to challenge incumbent leaders in markets such as transportation, lodging, media, and retail sectors. Examples include Google, Facebook, Amazon, Uber, and Airbnb.

See, for example Moore [9], Christensen [4], and Rogers [11].

Just-in-time purchasing consists of buying products and negotiating prices on the spot market. Each trade represents a new transaction, rather than a purchase or sale against a standing business account and contract.

Analysts for brokerage houses and investment bankers published these extravagant projections including Jupiter Communications, Gartner Group, Merrill Lynch, and Bear Stearns.

Examples included software packages for managing product catalogs, orders, and marketing, as well as “engines” for configuring complex products, conducting auctions and other models for dynamic pricing. Other start-ups offered on-line services for payment, credit management, and brokering transportation. Hardware vendors also prospered, selling servers to host net market software, as did consulting companies, who helped entrepreneurs design net market architectures and integrate and customize software packages to support their suites of B2B services. In short, an elaborate ecosystem of “arms merchants” sprang up to support the B2B exchange craze.

Sculley and Woods [12], Kaplan and Sawhney [8].

With suitably robust software platforms, the incremental costs to support new members and their activities were relatively low, allowing for excellent margins when operating at scale.

This type of 360-degree perspective that encompasses all decision-makers is also important in financial markets. See, for example, Casti [3], Axelrod and Cohen [1], Epstein and Axtell [7], Bookstaber [2].

Chapter 8 described Monte Carlo methods, which sample distributions randomly to populate the values of a model (i.e., a scenario) for a simulation run. Similar statistical sampling, called random variants or random deviates can be applied to market demographics to create a model population of companies.

It is also important to note that this decision model is stochastic; the populations of buyer and seller companies are generated by random sampling. Thus, simulation results for a given dynamic scenario are not reproducible. In contrast, the decision models in Chaps. 10, 12, and 13 are fully deterministic: results are identical from one simulation run of a dynamic scenario to another.

This bare bones simulation of sourcing focused on the dynamics of trading liquidity from the buyer’s side. It didn’t explore dynamic pricing models such as auctions or negotiation models. It also didn’t simulate any ancillary services for sellers, such as catalogs, advertising, or relationship management, nor post-trade fulfillment services such as logistics, insurance or sales tax collection.

Coase [5] proposed a theory of the corporation that compares internal vs. outsources costs to determine whether a function should be performed in-house or purchases externally.

What little trading data was available from net market pioneers was usually not relevant across industrial markets (e.g., commodity chemicals vs specialty steel) because of difference in market histories, power structures, the roles played by brokers, trading structures, automation levels, etc.

By analogy, consider creating a regression model to project market shares for a set of related drugs compared to a new surgical method.

Economist [6]. A few net markets did survive for a while, mostly owned and developed by industry leaders (e.g., Covisint for the automobile industry and private exchanges run by large companies). The preponderance of on-line trading moved onto generic e-sourcing platforms and seller web sites.

See for example, Rogers [11] Christensen [4], and Moore [9].

S-shaped curves can be modeled with sigmoid functions. Rogers [11].

Net markets had more compelling answers to related problems of trust, including the reliability of trading partners and assurance of product quality including member screening, product qualification systems, and performance rating systems to assess member reputations.

Porter [10] argued that any industry could be analyzed in terms of five forces: Intensity of Rivalry, Bargaining Power of Suppliers, Bargaining Power of Buyers, Threat of New Entrants, and Threat of Substitutes. Suppliers sometimes hold more power in some commodity markets, while there is more balance between large suppliers and large buyers in other industrial markets.

B2B trading today is conducted largely through brokers or sales forces using their company’s proprietary web sites. None of the handful of B2B marketplaces still operating today achieved runaway success; at best, they are stable trading venues.

The biggest weakness in our initial model is that our method for assessing the value of net market services in the membership decision rule doesn’t reflect System 1 stability biases such as resistance to change.

Axelrod, Robert, and Michael D. Cohen. 2000. Harnessing Complexity: Organizational Implications of a Scientific Frontier. New York: Simon and Schuster.

Bookstaber, Richard. 2017. The End of Theory: Financial Crises, the Failure of Economics, and the Sweep of Human Interaction. Princeton: Princeton University Press.

Casti, John L. 2000. Bizsim: The World of Business – in a box. Artificial Life and Robotics 4(3): 125–129.

Christensen, Clayton M., 1997. The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail. Boston, MA: Harvard Business School Press.

Coase, Ronald 1937. The Nature of the Firm. Economica. 4(16): 386–405. doi:https://doi.org/10.1111/j.1468-0335.1937.tb00002.x.

Economist. 2001. B2B Exchanges: Time to Rebuild. 359 (8222): 79. May 17, 2001. Available at http://www.economist.com/node/627416. Accessed URL 05 Jul 2019.

Epstein, Joshua M., and Robert Axtell. 1996. Growing Artificial Sciences: Social Science from the Bottom Up. Washington, DC: Brookings Institution Press.

Kaplan, Steven and Mohanbir Sawhney. 2000. E-Hubs: The New B2B Marketplaces. Harvard Business Review 78(3): 97–103.

Moore, Geoffrey. 1991. Crossing the Chasm: Marketing and Selling Technology Products to Mainstream Customers. New York: HarperCollins.

10.

Porter, Michael E. 1980. Competitive Strategy: Techniques for Analyzing Industries and Competitors. New York: Free Press.

11.

Rogers, Everett M. 2003. Diffusion of Innovations. (Fifth Edition) New York: Free Press.

12.

Sculley, Arthur B., and William A. Woods. 1999. B2B Exchanges: The Killer Application in the Business-to-Business Internet Revolution. Stamford, CT: Thomson ISI.

13.

Slywotzky, Adrian J., and Richard Wise. 2003. How to Grow When Markets Don’t. New York: Warner Business Books.

Titel: Disruptive Growth
verfasst von: Richard M. Adler
Verlag: Springer International Publishing
Buch: Bending the Law of Unintended Consequences
Print ISBN: 978-3-030-32713-2

Electronic ISBN: 978-3-030-32714-9

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-32714-9_11