Understanding multi-stage diffusion process in presence of attrition of potential market and related pricing policy

Singhal, Shakshi; Anand, Adarsh; Singh, Ompal

doi:10.2298/yjor180315001s

Cited by 8 publications

(1 citation statement)

References 42 publications

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“…The diffusion models incorporating the impact of pricing strategies have a long history in the marketing literature. Some notable studies include the work of Horsky (1990), Bass et al (1994), Speece and MacLachlan (1995), Kapur et al (2010a), Tsai (2013), Kapur et al (2015), and Singhal et al (2019) who have used novel methods to examine the influence of price change in the growth of the innovation.…”

Section: Related Literaturementioning

confidence: 99%

Technology diffusion model with change in adoption rate and repeat purchases: a case of consumer balking

Panwar

Kapur

Singh

2020

Int J Syst Assur Eng Manag

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Understanding the diffusion paradigm of emerging technological products is crucial for continuous development. In this study, a value-based diffusion model is proposed to forecast the market performance of new products. The model analyzes the interaction between the consumer's psychological perspective on dynamic price and goodwill of the new product using a two-dimensional framework. A Cobb-Douglas production function is applied to model the different dimensions of product adoption. The model overcomes the limitation of previous studies by incorporating the variation in the adoption rate. In addition, this study also introduces a concept of changepoint in the percentage of repeat purchases. A time instance at which the rate of occurrence of a phenomenon alters is called change-point. Moreover, the rapid introduction of new and improved version of products makes a potential buyer balk (i.e. hesitate or wait). Therefore, the actual potential market is always lower than the estimated market size. Thus, the concept of consumer balking is incorporated in the present study in a quantitative manner. Furthermore, the LCD monitor actual sales data is used to validate the model. Findings recommend that the proposed model have superior fitting and prediction ability as compared with conventional diffusion models. The novelty of the study is that it is flexible and has the ability to describe the real market scenario. Moreover, it includes multiple factors that drive the diffusion of new technology in data analysis. Keywords Technology diffusion models Á Change-point Á Cobb-Douglas production function Á Consumer balking Á Price change Á Repeat purchasing Á Two-dimensional framework

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