On the development of innovation diffusion model using stochastic differential equation incorporating change in the adoption rate

Kapur, P. K.; Chaudhary, Kuldeep; Aggarwal, Anu G.; Jha, Prakash C.

doi:10.1504/ijor.2012.047516

Cited by 8 publications

(5 citation statements)

References 4 publications

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“…Therefore, the diffusion parameters will undergo discontinuity. Hence, to include this effect, recently few studies have been carried out that integrated the concept of change-point in the diffusion process (Kapur et al , 2007; Singh et al , 2012; Kapur et al , 2012; Kapur et al , 2017; Panwar et al , 2019). Change-point refers to the time-point wherein the rate of adoption per remaining number of buyers is modified.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling two-dimensional technology diffusion process under dynamic adoption rate

Kapur

Panwar

Singh

2019

JM2

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Purpose This paper aims to develop a parsimonious and innovative model that captures the dynamics of new product diffusion in the recent high-technology markets and thus assist both academicians and practitioners who are eager to understand the diffusion phenomena. Accordingly, this study develops a novel diffusion model to forecast the demand by centering on the dynamic state of the product’s adoption rate. The proposed study also integrates the consumer’s psychological point of view on price change and goodwill of the innovation in the diffusion process. Design/methodology/approach In this study, a two-dimensional distribution function has been derived using Cobb–Douglas’s production function to combine the effect of price change and continuation time (goodwill) of the technology in the market. Focused on the realistic scenario of sales growth, the model also assimilates the time-to-time variation in the adoption rate (hazard rate) of the innovation owing to companies changing marketing and pricing strategies. The time-instance upon which the adoption rate alters is termed as change-point. Findings For validation purpose, the developed model is fitted on the actual sales and price data set of dynamic random access memory (DRAM) semiconductors, liquid crystal display (LCD) monitors and room air-conditioners using non-linear least squares estimation procedure. The results indicate that the proposed model has better forecasting efficiency than the conventional diffusion models. Research limitations/implications The developed model is intrinsically restricted to a single generation diffusion process. However, technological innovations appear in generations. Therefore, this study also yields additional plausible directions for future analysis by extending the diffusion process in a multi-generational environment. Practical implications This study aims to assist marketing managers in determining the long-term performance of the technology innovation and examine the influence of fluctuating price on product demand. Besides, it also incorporates the dynamic tendency of adoption rate in modeling the diffusion process of technological innovations. This will support the managers in understanding the practical implications of different marketing and promotional strategies on the adoption rate. Originality/value This is the first attempt to study the value-based diffusion model that includes key interactions between goodwill of the innovation, price dynamics and change-point for anticipating the sales behavior of technological products.

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Section: Literature Reviewmentioning

confidence: 99%

Modeling two-dimensional technology diffusion process under dynamic adoption rate

Kapur

Panwar

Singh

2019

JM2

Self Cite

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“…It may alter due to modification in packaging, transformations in advertising strategy, increase in the number of outlets, and availability of pack sizes, combinational offers and discounts offered. Also the entry and exit of competitors in the market affect the rate of diffusion of an innovation (Kapur et al, 2007(Kapur et al, , 2012a(Kapur et al, , 2012b. These changes may be reflected by a sudden shift in adoption curve called the change-point.…”

Section: Change Pointmentioning

confidence: 99%

“…Another line of research has paid attention at micro level factors of adoption by studying how consumer's attitudes and behaviours are affected by product characteristics (Labay and Kinnear, 1981;Mahajan et al, 1990;Rogers, 1995). Also some research originated by relaxing the basic set of assumptions as used by Bass (1969) to account more variability in the modelling framework (Kapur et al, 2012a(Kapur et al, , 2012bSingh at al., 2012). This stream of research contributes to the understanding of the two very important factors that determine the adoption by individual consumers.…”

Section: Introductionmentioning

confidence: 99%

Queuing theory-based innovation diffusion modelling incorporating change in adoption rate

Anand¹,

Agarwal²,

Aggrawal³

et al. 2018

IJMOR

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Adoption has always been an important process to discuss among marketers. Major work in the field of innovation adoption has been based on theory of first purchase by consumers. Of late attention has also been given to multi-stage nature of diffusion process. There are practitioners who have verified adoption as multi-stage process (depending on awareness and motivation). Researchers have lately also understood the value of change in marketing strategy and other factors that often lead to change in the rate of adoption. In this paper, we have made use of this stage wise approach of market penetration along with change point concept, have developed a methodical approach based on infinite server queuing theory and predicted sales for consumer durables. Experimental results estimated on sales of two different consumer durables show that present proposal can depict the change in adoption rates and predict the behaviour of the product in more accurate manner.

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“…Although, the adoption function for the second generation is assumed to remain smooth throughout. On substituting the functional form of the distribution function given by equation 12, into equations (10) and (11), the sales function for two consecutive generations become:…”

Section: Case 1: First Generation Experiences a Change-point Before Tmentioning

confidence: 99%

“…Thus, after putting equations (15) and (16), into equations (10) and (11), the total number of adopters of two generations can be described as:…”

Section: Case 2: First Generation Experiences a Change-point At Andmentioning

confidence: 99%

Multi-generation diffusion of technology

Kapur

Panwar

Shrivastava

et al. 2017

2017 6th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)

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In the era of demand-driven technology, the study of diffusion processes of high-technology products becomes crucial. The accelerated technological advancements and intergeneration substitutions illustrate the market of these innovations. In the past, attempts have been made to forecast the market growth of product generations meticulously. These studies have considered the diffusion parameters to be static; however, they are implausible to remain constant throughout the the product lifecycle and may experience considerable changes.To understand this phenomenon, the proposed diffusion model deals with the change-point technique in the multi-generation framework. Change-point is defined as a time instant where the growth rate in the diffusion process experiences a change due to varying market environment in which an innovation is placed. The main aim of the reseach is to develop an approach for analyzing the substitution effect among product generations when there exists change-points. Moreover, to demonstrate the applicability and efficiency of the constructed model, it is fitted to the sales data of DRAM (Dynamic Random Access Memory) industry.

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On the development of innovation diffusion model using stochastic differential equation incorporating change in the adoption rate

Cited by 8 publications

References 4 publications

Modeling two-dimensional technology diffusion process under dynamic adoption rate

Modeling two-dimensional technology diffusion process under dynamic adoption rate

Queuing theory-based innovation diffusion modelling incorporating change in adoption rate

Multi-generation diffusion of technology

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