Abstract
This chapter focuses on the two popular business models, namely, online group buying and crowdfunding. Both models use variations of all-or-nothing mechanisms, where transactions will take place only if the total number of committed purchases/pledges exceeds a specified threshold within a certain period. We seek to understand the impact of all-or-nothing mechanisms on consumer behavior, as well as the optimal design of such mechanisms, from the perspective of third-party platforms like Groupon and Kickstarter. First, using a dataset from the online group buying industry, we empirically identify two types of threshold-induced effects on consumer behavior. Next, we study optimal information disclosure and pricing strategies under all-or-nothing mechanisms. We show that it is always beneficial for the firm to disclose the cumulative number of sign-ups to reduce the uncertainty for later arrivals. Regarding pricing, we show that the introduction of a price menu for the same product can be a win-win for both the creator and buyers.
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Notes
- 1.
On equity crowdfunding sites, funders are also investors for the creators’ financial endeavors. This chapter focuses on non-equity crowdfunding where buyers are also funders but not investors.
- 2.
The price menu may not be far stretched from practice. Many projects on Kickstarter contain levels with minute quality differences. For each interpretation, one may consider a trivial quality for the price menu in this section.
- 3.
Our model can be easily adapted to situations where a not-for-profit creator wants to maximize the success rate, subject to raising enough funds to cover setup costs in advance. The fixed setup costs can become the exogenous target. Table 14.6 can be used as a guide for the optimal pricing strategy given the exogenous target. For example, if the exogenous target T falls in the range (2L, (1 − α)H + (1 + α)L], the menu strategy maximizes the success rate.
References
Arellano M, Bond S (1991) Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Rev Econ Stud 58(2):277–297
Bass F, Krishnan T, Jain D (1994) Why the bass model fits without decision variables. Market Sci 13(3):203–223
Bound J, Jaeger D, Baker R (1995) Problems with instrumental variables estimation when the correlation between the instruments and the endogeneous explanatory variable is weak. J Am Stat Assoc 90(430):443–450
Chen Y, Li X (2013) Group buying commitment and fundraisers’ competitive advantages. J Econ Manag Strateg 22(1):164–183
Du L, Ming H, Wu J (2017) Contingent stimulus in crowdfunding, Working paper. http://ssrn.com/abstract=2925962
Harsanyi J (1968) Games with incomplete information played by “Bayesian” players parts II. Baysian equilibrium points. Manag Sci 14(5):320–334
Häubl G, Popkowski Leszczyc P (2004) Bidding frenzy: intensity of competitive interaction among bidders and product valuation in auctions. Adv Consum Res 31:90–93
Heyman J, Orhun Y, Ariely D (2004) Auction fever: the effect of opponents and quasi-endowment on product valuations. J Interact Market 18(4):7–21
Hu M, Shi M, Wu J (2013) Simultaneous vs. sequential group-buying mechanisms. Manag Sci 59(12):2805–2822
Hu M, Li X, Shi M (2015) Product and pricing decisions in crowdfunding. Market Sci 34(3): 331–345
Jackson M, Yariv L (2007) Diffusion of behavior and equilibrium properties in network games. Am Econ Rev 97(2):92–98
Jing X, Xie J (2011) Group buying: a new mechanism for selling through social interactions. Manag Sci 57(8):1354–1372
Kauffman R, Wang B (2002) Bid together, buy together: on the efficacy of group-buying business models in Internet-based selling. In: Lowry PB, Cherrington JO, Watson RR (eds) Handbook of electronic commerce in business and society, CRC Press, Boca Raton
Ku G, Malhotra D, Murnighan J (2005) Towards a competitive arousal model of decision-making: a study of auction fever in live and internet auctions. Organ Behav Hum Decis Process 96(2): 89–103
Nickell S (1981) Biases in dynamic models with fixed effects. Econometrica 49(6):1417–1426
Roodman D (2009) A note on the theme of too many instruments. Oxf Bull Econ Stat 71(1): 135–158
Staiger D, Stock J (1997) Instrumental variables regressions with weak instruments. Econometrica 65(3):557–586
Tabarrok A (1998) The private provision of public goods via dominant assurance contracts. Public Choice 96(3–4):345–362
Varian H (1994) Sequential contributions to public goods. J Public Econ 53(2):165–186
Villas-Boas J, Winer R (1999) Endogeneity in brand choice models. Manag Sci 45(10):1324–1338
Wu J, Shi M, Hu M (2014) Threshold effects in online group buying. Manag Sci 61(9):2025–2040
Zhang J (2010) The sound of silence: observational learning in the US kidney market. Market Sci 29(2):315–335
Zhang J, Liu P (2012) Rational herding in microloan markets. Manag Sci 58(5):892–912
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Hu, M., Shi, M., Wu, J. (2019). Online Group Buying and Crowdfunding: Two Cases of All-or-Nothing Mechanisms. In: Hu, M. (eds) Sharing Economy. Springer Series in Supply Chain Management, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-030-01863-4_14
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