Extent and impact of incubation time in new product diffusion

Kohli, Rajeev

doi:10.1016/s0737-6782(98)00040-x

Cited by 56 publications

(12 citation statements)

References 33 publications

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“…On the one hand, the diffusion acceleration literature compares the rate of growth across product markets over time. By and large, these studies conclude that more recent product markets show faster diffusion than older ones (Agarwal and Bayus 2002;Chandrasekaran and Tellis 2008;Kohli et al 1999 1 ; Van den Bulte 2000 and 2002; Stremersch 2004 and. Exceptions to this generalized finding are rare (Bayus 1994) and contested on the grounds of estimation bias and invalid inference (Van den Bulte 2004).…”

Section: Introductionmentioning

confidence: 88%

Does new product growth accelerate across technology generations?

2009

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The academic literature on the growth acceleration of new products presents a paradox. On the one hand, the diffusion literature concludes that more recently introduced products show faster diffusion than older ones. On the other hand, technology generation literature argues that growth rate, at least as measured by diffusion parameters, remains constant across generations. We resolve this apparent paradox by testing whether growth acceleration occurs across technology generations while controlling for the passing of time. We check acceleration across 39 distinct technology generations in 12 product markets. The results show that intergeneration acceleration occurs in time to takeoff but not with respect to Market Lett (2010) The Wharton School, University of Pennsylvania, Philadelphia, PA, USA e-mail: peresren@wharton.upenn.edu diffusion parameters (i.e., p and q). We show that takeoff acceleration is mostly driven by technology vintage (i.e., the passage of time) rather than generational shifts. Thus, time is a factor that accelerates early growth, but generational shifts do not. This result also holds when controlling for the effects of market vintage when the market is either business-to-business or business-to-consumer as well as when the technology is process-or product-based.

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“…Kohli et al (1999) find that the "incubation time" of an innovation before market launch relates to subsequent diffusion. Golder and Tellis (1997) estimate the time from introduction to sales take-off of 31 innovations in the US and find significant variation as a function of price and market penetration.…”

Section: Industry Lifecyclesmentioning

confidence: 96%

“…This result contributes to the debate on accelerated diffusion. Studies of multiple consumer durables show no evidence of a shorter incubation time (Kohli et al, 1999) or of diffusion acceleration over time (Stremersch et al, 2010;Peres et al, 2010). However, Golder and Tellis (1997) find evidence of decreasing time to takeoff of products introduced after World War II, and Meade and Islam (2006) similarly discuss studies that suggest an increase of diffusion speed over the past century.…”

Section: Average Durationsmentioning

confidence: 99%

Measuring the duration of formative phases for energy technologies

Bento

Wilson

2016

Environmental Innovation and Societal Transitions

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Innovation processes during the early period of a technology's development establish the conditions for widespread commercialization. For comparative analysis of innovation processes across technologies, a common operational definition of the formative phase is needed. This paper develops a set of indicators to measure the start and end points of formative phases with reference to key innovation processes including experimentation and market formation. The indicators are then applied to measure the formative phase durations of sixteen energy technologies covering a range of historical periods and applications. Formative phases are found to last 22 years on average. Determinants of formative phase duration are explored. Duration does not appear to be explained by unit scale, up-scaling, nor initial cost. However, technologies that are ready substitutes for incumbents have shorter formative phases, ceteris paribus. Policy implications include the potentials and risks of accelerating formative phases to push low carbon technologies into the market.

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“…RACs were launched in 1933 (Kohli et al 1999, Table 1). Prior to 1949, we only have (Electrical Merchandising) scattered sales data that go back to 1936; the available total is about 217.…”

Section: An Empirical Examplementioning

confidence: 99%

A Piecewise-Diffusion Model of New-Product Demands

Niu

2006

Operations Research

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The Bass Model (BM) is a widely-used framework in marketing for the study of new-product sales growth. Its usefulness as a demand model has also been recognized in production, inventory, and capacity-planning settings. The BM postulates that the cumulative number of adopters of a new product in a large population approximately follows a deterministic trajectory whose growth rate is governed by two parameters that capture (i) an individual consumer's intrinsic interest in the product, and (ii) a positive force of influence on other consumers from existing adopters. A finite-population purebirth-process (re)formulation of the BM, called the Stochastic Bass Model (SBM), was proposed recently by the author in a previous paper, and it was shown that if the size of the population in the SBM is taken to infinity, then the SBM and the BM agree (in probability) in the limit. Thus, the SBM "expands" the BM in the sense that for any given population size, it is a well-defined model. In this paper, we exploit this expansion and introduce a further extension of the SBM in which demands of a product in successive time periods are governed by a history-dependent family of SBMs (one for each period) with different population sizes. A sampling theory for this extension, which we call the Piecewise-Diffusion Model (PDM), is also developed. We then apply the theory to a typical product example, demonstrating that the PDM is a remarkably accurate and versatile framework that allows us to better understand the underlying dynamics of new-product demands over time. Joint movement of price and advertising levels, in particular, is shown to have a significant influence on whether or not consumers are "ready" to participate in product purchase.

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“…Isto era factível até mesmo para variáveis críticas incluídas na análise ,tais como ano de entrada e tecnologia usada. No entanto, há da mesma forma, um grande arcabouço de evidências sugerindo que o padrão temporal de difusão das inovações acelera ao longo do tempo (BULTE; STREMERSCH, 2006;KOHLI;PAE, 1999 Estas duas linhas de pesquisa formam um paradoxo intrigante: aparentemente, na mesma economia, a aceleração de difusão do produto sobre o tempo deve refletir na aceleração de difusão de tecnologias posteriores; no entanto, as taxas de difusão das tecnologias que sucedem uma à outra; permanecem constante. Uma possível resolução desta questão foi sugerido recentemente por Stremersch e Tellis (2002), ao notar uma constância nos parâmetros de crescimento, mas um encolhimento no comprimento de tempo para decolagem em cada geração sucessiva.…”

Section: Geração Tecnológicaunclassified

Estratégias de preço na difusão de inovação: simulação baseada em agentes aplicado ao mercado brasileiro de carros elétricos

Yun¹

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Extent and impact of incubation time in new product diffusion

Cited by 56 publications

References 33 publications

Does new product growth accelerate across technology generations?

Measuring the duration of formative phases for energy technologies

A Piecewise-Diffusion Model of New-Product Demands

Estratégias de preço na difusão de inovação: simulação baseada em agentes aplicado ao mercado brasileiro de carros elétricos

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