Abstract
When deciding whether to adopt an innovative product, consumers often experience different levels of anxiety that prompt them to resist purchase. In some cases, consumers’ anxiety is mitigated by “validation” through externality (e.g., the number of early adopters). To reduce consumers’ anxiety, firms can also invest in “familiarization” through promotion (e.g., free trials). In this chapter, we conceptualize an innovative product as a product that engenders anxiety, and present a model that employs a consumer utility model focusing on the psychological dimension. We examine the firm’s profit-maximizing promotion and pricing decisions when selling to forward-looking consumers in the presence of externality. Our equilibrium analysis reveals that, unlike the conventional wisdom for promoting new version of an existing product, for anxiety-inducing innovations with externality, accelerating the speed of adoption through promotion can actually be detrimental to the firm.
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Notes
- 1.
The subjective component of utility is different from models of bounded rationality, which considers consumers with cognitive limitations or psychological biases. We do not assume that consumers use simple heuristics to make complex decisions, or display certain intrinsic psychological tendencies. See Ren and Huang (2018) for a recent review of modeling bounded rationality in operations management.
- 2.
These objective functional benefits are essentially the additional willingness to pay over comparable traditional products. This additional valuation can be measured via lab experiments and it is well examined by consumer behavior researchers (Sheth et al. 1991; Dahl and Moreau 2002; Mukherjee and Hoyer 2001).
- 3.
Our model is easily generalizable to the case where the x i is uniformly distributed over [0, R] for any R.
- 4.
We treat K and α as parameters for ease of analysis, but α, in principle, can be a function of K. Specifically, because our focus is on the pricing decisions, we shall consider the case when K and α are exogenously given to simplify our analysis. However, if investment K is the focused decision, then one needs to model K as a function of α, and the functional form and the associated value can be estimated through lab experiments. We shall relegate this issue to a future research topic.
- 5.
It can also be adjusted upward if D 1 < β, i.e., if there are few early adopters, then it can actually raise the anxiety levels for the remaining consumers. This characterization is consistent with the notion that consumers are more (less) willing to enter a restaurant when it is full (empty).
- 6.
These thresholds will be determined endogenously as we determine the equilibrium purchasing strategy.
- 7.
When K = 0, the two regions will be delineated by a curve resembling a horizontal line around β = 0.2. The detrimental effect of familiarization when β is low holds even when α is a decreasing function of K.
References
Abrahamson E, Rosenkopf L (1993) Institutional and competitive bandwagons: using mathematical modeling as a tool to explore innovation diffusion. Acad Manag Rev 18(3):487–517
Bandejee AV (1992) A simple model of herd behavior. Q J Econ 107(3):797–817
Bass FM (1969) A new product growth for model of consumer durables. Manag Sci 15(5):215–227
Becker GS (1991) A note on restaurant pricing and other examples of social influences on price. Eur J Polit Econ 99(5):1109–1116
Bensaid B, Lesne JP (1996) Dynamic monopoly pricing with network externalities. Int J Ind Organ 14(6):837–855
Bikhchandani S, Hirshleifer D, Welch I (1998) Learning from the behavior of others: conformity, fads, and informational cascades. J Econ Perspect 12(3):151–170
Brynjolfsson E, Kemerer CF (1996) Network externalities in microcomputer software: an econometric analysis of the spreadsheet market. Manag Sci 42(12):1627–1647
Castano R, Kacker M, Sujan H (2008) Managing consumer uncertainty in the adoption of new products: temporal distance and mental simulation. J Market Res 45(3):320–336
Chattergee R, Eliashberg J (1990) The innovation diffusion process in a heterogeneous population: a micromodeling approach. Manag Sci 36(9):1057–1079
Dahl DW, Moreau P (2002) The influence and value of analogical thinking during new product ideation. J Market Res 39(1):47–60
Dhebar A, Oren SS (1985) Optimal dynamic pricing for expanding networks. Market Sci 4(4):336–351
Fournier S (1998) Consumers and their brands: developing relationship theory in consumer research. J Consum Res 24(4):343–353
Gabszewicz JJ, Garcia F (2008) A note on expanding networks and monopoly pricing. Econ Lett 98(1):9–15
Gao SY, Lim WS, Tang CS (2016) Entry of copycats of luxury brands. Market Sci 36(2):272–289
Gatignon H, Robertson TS (1985) A propositional inventory for new diffusion research. J Consum Res 11(4):849–867
Hellofs LL, Jacobson R (1999) Market share and customers’ perceptions of quality: when can firms grow their way to higher versus lower quality? J Market 63(1):16–25
Hoch SJ, Loewenstein GF (1991) Time-inconsistent preferences and consumer self-control. J Consum Res 17(4):492–507
Hoeffler S (2003) Measuring preferences for really new products. J Market Res 40(4):406–420
Huang Y, Gokpinar B, Tang CS, Yoo OS (2018) Selling innovative products in the presence of externalities. Prod Oper Manag 27(7):1236–1250
Johnson JP, Myatt DP (2006) On the simple economics of advertising, marketing, and product design. Am Econ Rev 96(3):756–784
Katz ML, Shapiro C (1985) Network externalities, competition, and compatibility. Am Econ Rev 75(3):424–440
Lilien GL, Kotler P, Moorthy KS (1992). Marketing models. Prentice Hall, Englewood Cliffs, NJ
Moretti E (2011) Social learning and peer effects in consumption: evidence from movie sales. Rev Econ Stud 78(1):356–393
Mukherjee A, Hoyer W (2001) The effect of novel attributes on product evaluation. J Consum Res 28(3):462–472
Oliver RL (1980) A cognitive model of the antecedents and consequences of satisfaction decisions. J Market Res 17(4):460–469
Papanastasiou Y, Savva N (2016). Dynamic pricing in the presence of social learning and strategic consumers. Manag Sci 63(4):919–939
Peres R, Muller E, Mahajan V (2010) Innovation diffusion and new product growth models: a critical review and research directions. Int J Res Market 27(22):91–106
Ram S (1989) Successful innovation using strategies to reduce consumer resistance: an empirical test. J Prod Innov Manag 6(1):20–34
Ram S, Sheth JN (1989) Consumer resistance to innovation: the marketing problem and its solutions. J Consum Market 6(2):5–14
Ren H, Huang T (2018) Modeling customer bounded rationality in operations management: a review and research opportunities. Comput Oper Res 91:48–58
Sheth JN, Newman BI, Gross BL (1991) Why we buy what we buy: a theory of consumption values. J Bus Res 22(2):159–170
Thaler R (1985) Mental accounting and consumer choice. Market Sci 4(3):199–214
Wang H (1997) Treatment of “don’t-know responses” in contingent valuation surveys: a random valuation model. J Environ Econ Manag 32(2):219–232
Yu M, Debo L, Kapuscinski R (2016) Strategic waiting for consumer-generated quality information: dynamic pricing of new experience goods. Manag Sci 62(2):410–435
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Huang, Y., Gokpinar, B., Tang, C.S., Yoo, O.S. (2020). Selling Innovative Products to Anxious Consumers. In: Ray, S., Yin, S. (eds) Channel Strategies and Marketing Mix in a Connected World. Springer Series in Supply Chain Management, vol 9. Springer, Cham. https://doi.org/10.1007/978-3-030-31733-1_2
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