Bass Model with Explanatory Parameters

Sokele, Mladen; Moutinho, Luiz

doi:10.1007/978-3-319-64394-6_7

Cited by 9 publications

(5 citation statements)

References 4 publications

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“…The findings are validated using statistical modeling techniques to capture and represent refueling behaviors or patterns. This is in line with numerous studies, such as those in [14,[38][39][40], which have used empirical datasets as the foundation for the design of their simulation models. Both mention the use of the Python programming language for model development and implementation.…”

Section: Similaritiessupporting

confidence: 76%

“…Modeling approach: In terms of the modeling approach, the first algorithm is founded on the MCP to capture the random nature of refueling of HFV users. This approach has been used in several studies; however, applications specifically to HFVs are limited [38,42]. On the other hand, the second model also considers weather conditions and the time of the year as factors influencing driving patterns and refueling behavior.…”

mentioning

confidence: 99%

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Analysis of Refueling Behavior Models for Hydrogen-Fuel Vehicles: Markov versus Generalized Poisson Modeling

Isaac,

Saha

2023

Sustainability

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This paper presents an analysis of two methodologies that can be used to predict refueling behavior. Both models aim to provide insights into hydrogen-fuel vehicle users’ refueling patterns and behaviors. The first model leverages probabilistic transitions between refueling states to simulate and predict the refueling behavior of hydrogen vehicle users. In contrast, the GP-1 model employs Gaussian processes to capture the underlying patterns and uncertainties in hydrogen-fuel vehicle refueling behavior, taking into consideration additional factors such as weather conditions and the time of the year. The model demonstrates statistical significance and accuracy in predicting trips while identifying the insignificance of precipitation and high ambient temperatures. The methodologies, findings, strengths, and limitations of the two models were tested and compared to identify their relevant contributions. By contrasting their methodologies and evaluating their predictive performance, using performance metrics such as accuracy, precision, and recall values, this study provides valuable insights into the strengths and limitations of each approach. Limitations include assuming a stationary refueling process and excluding external factors and limitations related to data availability, as well as the absence of a specific focus on hydrogen-fuel vehicles. By understanding the differences and similarities between these two models, this paper aims to provide a unique perspective on gaps and further requirements for accurate prediction and modeling of refueling behavior to guide policymakers, infrastructure planners, and stakeholders in making informed decisions regarding the design and optimization of hydrogen refueling infrastructure.

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Section: Similaritiessupporting

confidence: 76%

mentioning

confidence: 99%

Analysis of Refueling Behavior Models for Hydrogen-Fuel Vehicles: Markov versus Generalized Poisson Modeling

Isaac,

Saha

2023

Sustainability

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“…The Bass model is currently presented in a manner proposed by [79], where it is emphasized that in addition to parameters p, q and m, the starting point is also important. It means the moment when an innovation appeared on the market, and the time interval-the accuracy with which the model is calculated.…”

Section: Conflicts Of Interestmentioning

confidence: 99%

Determination of Electricity Demand by Personal Light Electric Vehicles (PLEVs): An Example of e-Motor Scooters in the Context of Large City Management in Poland

2020

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Personal light electric vehicles (PLEVs) are a phenomenon that can currently be observed in cities, intended to be an ecological form of transport. The authors of the paper make an attempt to determine electricity consumption by PLEVs in the context of managing a large city in accordance with the concept of sustainable development. The article is of a cognitive nature. Research questions posed against the background of the goal formulated are as follows: how strong will the demand for PLEVs be (in the example of e-motor scooters, taking into consideration the number of vehicles) and for the electricity consumed by PLEVs. The method used is a simulation model. The conducted analyses demonstrate that a dynamic growth of PLEVs will result in an increased energy demand, which must be taken into account by the cities, developing according to the sustainable development conception.

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“…Unlike the classic logistic curve, the Bass model assumes that innovators are present at all stages of diffusion process, which seems more appropriate for radical high-tech new products (McDade et al, 2010). Similar to Sokele and Moutinho (2018), our GBM aims to find the time at which demand reaches the peak. While modeling demand using the DLDM or the GBM, the time at which the financial gap (expected revenuecost of commercialization) is greatest marks the firm's strategic decision point to choose whether to continue or exit in the next period.…”

Section: Introductionmentioning

confidence: 99%

“…, 2010). Similar to Sokele and Moutinho (2018), our GBM aims to find the time at which demand reaches the peak. While modeling demand using the DLDM or the GBM, the time at which the financial gap (expected revenue – cost of commercialization) is greatest marks the firm's strategic decision point to choose whether to continue or exit in the next period.…”

Section: Introductionmentioning

confidence: 99%

Scenarios for optimizing timing for new product exits: a trifecta of models' predictive performances

Sharma

Lichtenthal

2022

BIJ

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PurposeThe purpose of the study is applying and comparing models that predict optimal time for new product exit based on its demand pattern and survivability. This is to decide whether or not to continue investing in new product development (NPD).Design/methodology/approachThe study investigates the optimal time for new product exit within the hi-tech sector by applying three models: the dynamic learning demand model (DLDM), the generalized Bass model (GBM) and the hazard model (HM). Further, for inter- and intra-model comparison, the authors conducted a simulation, considering Weiner and exponential price functions to enhance generalizability.FindingsWhile higher price volatility signifies an unstable technology, greater investment into research and development (R&D) and marketing results in higher product adoption rates. Imitators have a more prominent role than innovators in determining the longevity of hi-tech products.Originality/valueThe study conducts a comparison of three different models considering time-varying parameters. There are four scenarios, considering variations in advertising intensity and content, word-of-mouth (WOM) effect, price volatility effect and sunk cost effect.

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Bass Model with Explanatory Parameters

Cited by 9 publications

References 4 publications

Analysis of Refueling Behavior Models for Hydrogen-Fuel Vehicles: Markov versus Generalized Poisson Modeling

Analysis of Refueling Behavior Models for Hydrogen-Fuel Vehicles: Markov versus Generalized Poisson Modeling

Determination of Electricity Demand by Personal Light Electric Vehicles (PLEVs): An Example of e-Motor Scooters in the Context of Large City Management in Poland

Scenarios for optimizing timing for new product exits: a trifecta of models' predictive performances

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