Determining adoption pattern with pricing using two-dimensional innovation diffusion model

Kapur, P. K.; Singh, Ompal; Chanda, Udayan; Basirzadeh, Mashaallh

doi:10.1016/j.hitech.2010.05.001

Cited by 28 publications

(14 citation statements)

References 29 publications

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“…Besides the expert-chosen attributes, we included price as a fourth attribute, as it plays a particularly important role when purchasing or renting emerging, innovative technologies [22]. More specifically, in shared autonomous taxis, travel costs are among the most relevant factors predicting adoption [24].…”

Section: Research Methodology and Study Setupmentioning

confidence: 99%

Trust is Good, Control is Better - Customer Preferences Regarding Control in Teleoperated and Autonomous Taxis

Keller¹,

Zimmermann²,

Zibuschka³

et al. 2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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Autonomous vehicles are continually being developed and are the focus of many research projects. While the focus so far has been primarily on technical features and the implementation of autonomous vehicles, we are turning to the consumer side to analyze the preferences of potential users regarding teleoperable robotaxis. After all, user acceptance is a necessary condition for the success of a new technology. Using a choice-based conjoint analysis, we investigate preferences for three different control dependent attributes and the price. We observe price to be the most important attribute, followed by the possibility of intervention, pilot, and interior monitoring-in this order. However, respondents' preferences turn out to be very heterogeneous. Within the framework of a cluster analysis, we look at the results in segments and analyze possible moderating effects using an analysis of variance.

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Section: Research Methodology and Study Setupmentioning

confidence: 99%

Trust is Good, Control is Better - Customer Preferences Regarding Control in Teleoperated and Autonomous Taxis

Keller¹,

Zimmermann²,

Zibuschka³

et al. 2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…Another severe limitation of BM was the absence of the effect of marketing-mix variables, such as price and advertising intensity on the diffusion process of innovations. Therefore, attempts have been made in the past to cater the influence of these marketing-mix variables in the diffusion modeling (Kamakura and Balasubramanian, 1988; Horsky, 1990; Bass et al , 1994; Speece and MacLachlan, 1995; Kapur et al , 2010; Tsai, 2013; Kapur et al , 2015; Sachdeva et al , 2016). Bass et al (1994) asserted that the conditional likelihood of adoption of an innovation by an individual is an output of two attributes: diffusion force (BM) and the marketing efforts consumed at time t .…”

Section: Literature Reviewmentioning

confidence: 99%

“…One of the pivotal management strategy associated with the innovation is its pricing structure that control the growth of the product. It has been established that the demand for new products is price sensitive (Speece and MacLachlan, 1995; Wang and Chang, 2009; Kapur et al , 2010). Therefore, pricing is the most decisive criterion in ascertaining consumer-purchasing decisions.…”

Section: Introductionmentioning

confidence: 99%

“…This behavior can be characterized as a consumer’s psychological viewpoint on the price revision of the innovation (Tsai, 2013). Several researchers have investigated the diffusion phenomenon of technology innovation by adding the effect of price variation as well (Kalish, 1985; Bass et al , 1994; Speece and MacLachlan, 1995; Wang and Chang, 2009; Kapur et al , 2010; Tsai, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Modeling two-dimensional technology diffusion process under dynamic adoption rate

Kapur

Panwar

Singh

2019

JM2

Self Cite

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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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“…There are many relevant factors affecting the potential, including socioeconomic conditions in the social system, increases or decreases in the population of the social system, government actions, and efforts to influence the diffusion process, such as advertising and pricing. Such a proposition was followed and developed by Lackman, Dodson, and several other researchers [17][18][19][20][21][22][23][24]. Kalish, for example, conjectured that the qualitative structure of an optimal pricing policy will remain the same upon the inclusion of additional marketing variables such as advertising, in the diffusion model.…”

Section: Introductionmentioning

confidence: 99%

A Dynamical Innovation Diffusion Model with Fuzzy Coefficient and Its Application to Local Telephone Diffusion in China

Liao

Yao

2013

Discrete Dynamics in Nature and Society

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This paper studies the innovation diffusion problem with the affection of urbanization, proposing a dynamical innovation diffusion model with fuzzy coefficient, and uses the shifting rate of people from rural areas stepping into urban areas to show the process of urbanization. The numerical simulation shows the diffusion process for telephones in China with Genetic Algorithms and this model is effective to show the process of innovation diffusion with the condition of urbanization process.

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Determining adoption pattern with pricing using two-dimensional innovation diffusion model

Cited by 28 publications

References 29 publications

Trust is Good, Control is Better - Customer Preferences Regarding Control in Teleoperated and Autonomous Taxis

Trust is Good, Control is Better - Customer Preferences Regarding Control in Teleoperated and Autonomous Taxis

Modeling two-dimensional technology diffusion process under dynamic adoption rate

A Dynamical Innovation Diffusion Model with Fuzzy Coefficient and Its Application to Local Telephone Diffusion in China

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