scholarly journals How can imitation counterbalance innovation? An ABM Bass model for competing products

2014 ◽  
Author(s):  
Philippe Collard ◽  
Wilfried Segretier
Keyword(s):  
Bass Model

scholarly journals Novel Optimization-Based Parameter Estimation Method for the Bass Diffusion Model

SAGE Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 215824402110269
Author(s):  
Lang Liang
Keyword(s):  
Parameter Estimation ◽  
Least Squares Method ◽  
Weighted Least Squares ◽  
Estimation Method ◽  
Reference Value ◽  
Diffusion Problem ◽  
Bass Model ◽  
Weighted Least Squares Method ◽  
Estimated Parameters ◽  
Bass Diffusion Model

The Bass model is the most popular model for forecasting the diffusion process of a new product. However, the controlling parameters in it are unknown in practice and need to be determined in advance. Currently, the estimation of the controlling parameters has been approached by various techniques. In this case, a novel optimization-based parameter estimation (OPE) method for the Bass model is proposed in the theoretical framework of system dynamics ( SD). To do this, the SD model of the Bass differential equation is first established and then the corresponding optimization mathematical model is formulated by introducing the controlling parameters as design variable and the discrepancy of the adopter function to the reference value as objective function. Using the VENSIM software, the present SD optimization model is solved, and its effectiveness and accuracy are demonstrated by two examples: one involves the exact solution and another is related to the actual user diffusion problem from Chinese Mobile. The results show that the present OPE method can produce higher predicting accuracy of the controlling parameters than the nonlinear weighted least squares method and the genetic algorithms. Moreover, the reliability interval of the estimated parameters and the goodness of fitting of the optimal results are given as well to further demonstrate the accuracy of the present OPE method.

scholarly journals Diffusion Mechanism of Leading Technology in the New Energy Industry Based on the Bass Model

2021 ◽  
Vol 9 ◽  
Author(s):  
Hongying Wang ◽  
Bing Sun
Keyword(s):  
Expected Utility ◽  
Simulation Analysis ◽  
Diffusion Mechanism ◽  
Effective Diffusion ◽  
Energy Industry ◽  
Bass Model ◽  
Property Development ◽  
Industrial Transformation ◽  
New Energy ◽  
The Government

With the increasing difficulties associated with heating, the new energy industry has become the mainstay for property development. The effective diffusion of leading technologies supplies a social edge for enterprise core technologies, and this is also a necessary topic for industrial transformation and optimization. Within the international context of energy conservation and emission reduction, the scientific and in-depth study of the diffusion mechanisms underlying leading technologies in the new energy industry have vital theoretical significance for the promotion of the diffusion of leading technologies. Based on the introduction of the Bass model and one extension model, this paper constructs the diffusion model of the new energy industry’s leading technology and analyzes its diffusion mechanism. The identified mechanism indicates that in the case of imperfect market and policy environments, the diffusion of the leading technology of the new energy industry is mainly influenced by the “expected utility” of innovators and the “actual utility” of imitators. The diffusion of the leading technology in innovator enterprises of the new energy industry is mainly affected by the “expected utility,” while the diffusion in imitator enterprises is affected by the “actual utility.” These influences are verified by simulation analysis. Based on the diffusion mechanism, several suggestions are presented for the promotion of the diffusion mechanism of leading technology, with the aim to provide references for the government, industry associations, and enterprises for relevant decision-making.

Bass Model with Explanatory Parameters

2017 ◽  
pp. 145-164 ◽  
Author(s):  
Mladen Sokele ◽  
Luiz Moutinho
Keyword(s):  
Bass Model

A demand forecasting model based on the improved Bass model for fast fashion clothing

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiaoxi Zhou ◽  
Jianfei Meng ◽  
Guosheng Wang ◽  
Qin Xiaoxuan
Keyword(s):  
Historical Data ◽  
Consumer Preferences ◽  
Demand Forecasting ◽  
Impact Factors ◽  
Bass Model ◽  
Demand Forecast ◽  
Fast Fashion ◽  
Content Type ◽  
Sales Data ◽  
Demand Forecasting Model

PurposeThis paper examines the problem of lack of historical data and inadequate consideration of factors influencing demand in the forecasting of demand for fast fashion clothing and proposes an improved Bass model for the forecasting of such a demand and the demand for new clothing products.Design/methodology/approachFrom the perspective of how to solve the lack of data and improve the precision of the clothing demand forecast, this paper studies the measurement of clothing similarity and the addition of demand impact factors. Using the fuzzy clustering–rough set method, the degree of resemblance of clothing is determined, which provides a basis for the scientific utilisation of historical data of similar clothing to forecast the demand for new clothing. Besides, combining the influence of consumer preferences and seasonality on demand forecasting, an improved Bass model for a fast fashion clothing demand forecast is proposed. Finally, with a forecasting example of demand for clothing, this study also tests the validity of the method.FindingsThe objective measurement method of clothing similarity in this paper solves the problem of the difficult forecasting of demand for fast fashion clothing due to a lack of sales data at the preliminary stage of the clothing launch. The improved Bass model combines, comprehensively, consumer preferences and seasonality and enhances the forecast precision of demand for fast fashion clothing.Originality/valueThe paper puts forward a scientific, quantitative method for the forecasting of new clothing products using historical sales data of similar clothing, thus solving the problem of lack of sales data of the fashion.

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