Bass Diffusion Model Adaptation Considering Public Policies to Improve Electric Vehicle Sales—A Brazilian Case Study

The global fleet of electric vehicles (EV) has been rising in recent years, and public policies can play an important role in this scene. The objective of this work is to evaluate the impact of public policies in the diffusion of EVs in Brazil, based on Beck’s adaptation for the Bass diffusion model. This modification on the Bass model allows the estimation of EV diffusion, taking into account the direct and indirect economic influence of the main EV incentive instruments used worldwide. In addition, this work conducts a forecast of the total passenger cars in Brazil through a regression model, considering macroeconomic and social indicators. The results indicate that EV high prices may still be the major barrier for EV diffusion in Brazil over the studied horizon, keeping them inaccessible to the majority of the population. Therefore, policies aimed at subsidizing EVs may be more effective in stimulating EV sales.

DYNAMIC ANALYSIS OF SUSTAINABLE PRACTICES ADOPTION IN ROAD INFRASTRUCTURE DEVELOPMENT

Awareness of the importance of sustainable development has made many governments prioritize the adoption of green measures. The road infrastructure sector is no stranger to this and has been transforming its processes in such a way that they incorporate more sustainable practices. However, the inclusion of this type of practice has not been an easy task, since the adoption of these practices results from a decision-making process that is affected by the context of potential adopters, as well as other external parameters such as institutional, environmental, socioeconomic factors, etc. This study aims to identify the main variables that affect the adoption of sustainable practices in the development of roadways in Colombia. Through the application of the Bass Diffusion model concept, the most influential variables were identified and a Causal Loop Diagram (CLD) was built to understand the dynamics and feedback processes between them. External variables such as the popularity of the green technique, guidelines existence, and costs associated with them are significantly influential in the inclusion of green measures. Additionally, the level of influence of each variable is assessed through the formulation of a system dynamics model and the evaluation of different scenarios. Finally, a set of recommendations to overcome barriers in the process of green measures adoption is given. This model can be used as a support tool in formulating strategies that promote sustainable practices in road development.

Novel Optimization-Based Parameter Estimation Method for the 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.

Migration model of peripheral ideological concepts: possibilities of system-dynamic analysis

The work is devoted to the urgent problem of the danger of the growth of extremist sentiments among young people caused by unpredictable changes in ideological attitudes (attitudes) in the group consciousness. Objectives: study of the dynamics of peripheral ideological concepts – one of the structural elements of ideology; identifying the trend of changes in the ideological attitudes of student youth on the example of the South of Russia; assessment of the predictive capabilities of the system dynamic analysis methodology for calculating the dynamics of ideological processes. An attempt was made to approbate the author's method of studying ideological attitudes, which could be conditionally called quantitative narrative analysis. The empirical base was made up of data from a survey of 2500 students in the South of Russia and the results of a five-year monitoring of the development of the ideological situation. The theoretical basis is the morphological approach to the study of ideologies by Michael Frieden and the concept of the cognitive-ideological matrix. In the development of this concept, the focus of the study was shifted from the morphology (structure) of ideologies to implicit processes occurring at the border of the cognitiveideological matrix and the social environment. As a result, a descriptive mental model of peripheral ideological concepts and a conceptual model of their migration based on the Bass diffusion model were built, performed in the AnyLogic simulation system. The result of the study was the identification of a left-liberal trend using narrative analysis, which had replaced the significant predominance of conservative and national-patriotic ideological attitudes in the group youth consciousness. It is concluded that the model of migration of peripheral ideological concepts, created using the method of system dynamics and mathematical statistics, significantly expands the possibilities of forecasting ideological processes, but has some limitations.

New tourism product forecasting – application of Bass Diffusion Model and Grey Forecasting Model

Previous researches usually applied Bass diffusion model (BDM) in forecasting the new product in various areas. This is the first application of BDM to the new tourism product since the model had been developed by Frank M. Bass in 1969. On the other hand, Grey forecasting model able to deal with limited number of data. Both BDM and grey forecasting model have been used in various areas in the forecasting studies. Taking advantages of both models, the combination of both Bass and grey model, called grey Bass forecasting model is applied in the context of the new tourism product forecasting. The objective of this study is to forecast the new tourism product demand in Malaysia using the developed model. Yearly visitors from two ecotourism resorts in Pahang, Tanah Aina Fahad and Tanah Aina Farrah Soraya from 2014 until 2018 are used. The results show that both BDM and grey Bass forecasting model are suitable in forecasting the new tourism product. The authors also suggest other factors affecting the attendance of visitors to be included in further research to conclude which model perform better in the future.

Predicting Product Uptake Using Bass, Gompertz, and Logistic Diffusion Models: Application to a Broadband Product

In today’s competitive environment, broadband companies innovate to stay competitive, retain existing customers, and attract new customers. A recent innovative product in this industry is the deployment of the gigabit Internet service over fiber optic networks as a solution to the growing bandwidth demands from consumers. One determinant of the decision to deploy such product is the expectation of a positive return on investment (ROI) determined among others by the penetration or take rate of the product or service. Like any product, the adoption of the gigabit Internet is influenced by the reaction of customers to this innovation. Some customers are early adopters of the product while others might not be interested in higher bandwidth Internet connections or will simply adopt the product at a later time. The purpose of this paper was to identify a model that best predicts future trends in the uptake of the gigabit Internet product over fiber-to-the home (FTTH). To that effect, this study implemented three different models: Bass, Gompertz, and logistic diffusion models; analyzed their predictive abilities; and determined the best fit model in a FTTH brownfield scenario. The data used for the study were split into two sets: the first or training set was used to create the models and the second was used to validate their predicting abilities. The data analysis used the ordinary least squares (OLS) method to select the best fit model. The results suggested that while Gompertz best fitted the training data, Bass had a better forecasting power. In other words, the Bass diffusion model was best at forecasting future uptake of the gigabit Internet service, while Logistic optimistically forecasted above the take rate and Gompertz pessimistically forecasted below. These findings present various implications for researchers and practicians. For example, future research could replicate the study for different industries and products, while practicians could anticipate realistic financial results from the implementation of the findings.