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Price Models for New and Remanufactured High-Technology Products across Generations
Published in Ammar Y. Alqahtani, Elif Kongar, Kishore K. Pochampally, Surendra M. Gupta, Responsible Manufacturing, 2019
Liangchuan Zhou, Surendra M. Gupta
Price models are based on theories and practices. In a theoretical price model, the demand function is the critical part. One of the most famous demand functions for high-technology products is the Bass model (Bass, 1969). The Bass model is a traditional diffusion model in which the demand trend for new products goes through initial, mutual, and diffusion phases. This means that the demand for recently launched products will decrease significantly after the mutual phase, when the new generation is about to be released. Nowadays, the Bass model is still widely used in pricing decisions. Several of its applications are included in the Literature Review section. Another widely used model is the customer-surplus value model. The demand is dependent on the differential between the price and the customer’s perceived value for the product. A product with a higher value surplus receives more demand than a product with a lower value surplus. Negative value surplus represents little demand. Customer discrete choice models are also used, such as the hierarchical pricing decision model (Ding et al., 2016) and the conditional multinomial logit choice model (Kwak and Kim, 2013). These demand models are based on customers’ choices according to the product’s characteristics in the context of a competing product. Additionally, different sale-channels have different channel powers that influence the theoretical demand model. Examples of pricing models are included in the Literature Review section.
Market segmentation based modeling: An approach to understand multiple modes in diffusion curves
Published in Mangey Ram, J. Paulo Davim, Advanced Mathematical Techniques in Engineering Sciences, 2018
A. Anand, R. Aggarwal, O. Singh
The diffusion of innovation is an essential topic of research in the field of marketing management. Since the 1960s, plenty of innovation diffusion models have been introduced to study the diffusion process of a product. A plethora of diffusion models based on the highly pertinent work of Bass (1969) are available in literature. Bass (1969) has contributed greatly to the understanding of a variety of diffusion models. The simple structure of the Bass model has led to its higher number of applications over the last few decades. The Bass model perceives that an innovation spreads throughout the market by two main channels: mass media (external influence) and word of mouth (internal influence). His model assumes the nature of consumers to be homogenous with respect to their response behavior (Agliari et al. 2009). The inexorable expansion of the market forces the researchers to explore alternative diffusion models with high explanatory power. The variation in customers’ buying behavior requires a renewed focus toward the segmented market structure, which directly affects the expected profit of the firm (Wedel and Kamakura 2012).
Socioeconomic and Institutional Constraints on the Adoption of Soil Conservation Practices in the USA: Implications for Sustainable Agriculture
Published in Josef Křeček, G.S. Rajwar, Martin J. Haigh, Hydrological Problems and Environmental Management in Highlands and Headwaters, 2017
Practically all contemporary strategies used to motivate land owner-operators to adopt soil and water conservation practices are some variation of the “Traditional Diffusion Model” (Rogers, 1993). The diffusion model is very well respected in a number of professional disciplines because it has been used successfully to predict adoption of technologies and techniques in many geographical regions of the world. While the traditional diffusion model has a number of serious limitations, it provides a useful starting point for analyzing adoption of technologies and techniques.
Recent progress in solar wood drying: An updated review
Published in Drying Technology, 2023
Bilal Lamrani, Naoual Bekkioui, Merlin Simo-Tagne, Macmanus Chinenye Ndukwu
Different methods exist to model wood drying. Generally, the three main classes of models are diffusion models, multiphase models, and models based on Luikov’s approach.[26,27] Diffusion models based on Fick's law were extensively used by several researchers[28–34] and good simulation results were obtained for solar wood drying.[4–6,14,16,17,35–43] However, multiphase models were not widely used in recent wood drying studies. Only a few works have considered various phases to describe water transfer, including liquid water, bound water, and water vapor, in wood during drying. For instance, Zhao et al[2] applied a multiphase model that considered the moving evaporation interface in simulating the coupled heat and mass transfer during convective drying of wood. Simo Tagne et al[27] established a multiphase drying model to dry tropical woods and validated their results by those obtained by Luikov’s model. The latter is considered according to Martines-López et al[44] as the most used in the calculation of drying curves of solid materials. The models based on Luikov’s approach, in which wood is assumed to be homogeneous and isotropic and is modeled as a capillary porous medium, have been successfully carried out in the literature to explain the drying of wood,[45] as cited by Zadin et al.[26] and Simo Tagne et al.[27]
Gas desorption diffusion behavior in coal particles under constant volume conditions: Experimental research and model development
Published in Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2022
Chengwu Li, Zhen Qiao, Min Hao, Yilin Wang, Boshi Han
The diffusion behavior of gas is a significant factor affecting the desorption of coal particles. Currently, there are two widely used models, namely unipore diffusion (Barrer 1951) model and bidsperse diffusion model (Ruckenstein, Vaidyanathan, and Youngquist 1971). However, the two models analyze the gas diffusion behavior under a constant-pressure condition, that is, the methane concentration at the boundary of coal particles remains unchanged, which is apparently contrary to experimental conditions in this paper. Crank (1979) calculated the analytical solution of the three-dimensional spherical diffusion at adsorption state under constant-volume conditions. But these models all consider the diffusion coefficient as an invariant constant, while Sen (2010), Staib, Sakurovs, and Gray (2015) and others put forward a different opinion. They believe that the diffusion coefficient is a function associated with time. Consequently, based on the above problems, a variable-pressure dynamic diffusion model is proposed.
Optimal advertising duration for profit maximization
Published in Journal of Management Analytics, 2020
Adarsh Anand, Shakshi Singhal, Ompal Singh
Some studies have linked the optimal advertising strategies with the diffusion process of innovation. For instance, Bass, Krishnan, and Jain (1994) considered that the decision variables, such as price and advertising, influence the diffusion of an innovation. Diffusion phenomenon refers to the process of adoption of a new product into a marketplace and explains the sales behavior of the product over time (Singhal, Anand, & Singh, 2019). Mathematically, the diffusion process is described through diffusion models. A diffusion model illustrates the number of successive buyers, who with time adopt innovation, and empirically describes the adoption curve (Mahajan, Muller, & Bass, 1993). Bass (1969) has given one of the earliest diffusion models, which develop the expected life cycle of a new product to predict the initial-purchase sales volume. The underlying assumption of the model is that the diffusion of a new product follows an S-shaped path. According to an S-shaped structure, in the beginning, sales grow slowly and then it continues to progress with an exponential rate until a maximum point is reached (market saturation level), after which sales start increasing with a decreasing rate due to fixed market size (Sachdeva, Kapur, & Singh, 2016).