A model for commercial adoption of photovoltaic systems in California

Wang, Wenyu; Yu, Nanpeng; Johnson, R. Barry

doi:10.1063/1.4979899

Cited by 22 publications

(9 citation statements)

References 19 publications

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“…While considering different exogenous variables (representing marketing mix), researchers compared GMB's prediction accuracy with that of the classical model (Bass et al, 1994;Danaher et al, 2001;Boehner & Gold, 2012). For instance, in forecasting the adoption of solar photovoltaic systems, government subsidies and policies were included into the modes as exogenous variables (Guidolin & Mortarino, 2010;Yamaguchi et al, 2013;Wang et al, 2017).…”

Section: Literature Review On Innovation Diffusion and Bass Modelmentioning

confidence: 99%

The Use of Big Data in Tourism Sales Forecasting

Kachniewska

2020

IJCM

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Background. The explosion of big data (BD), automation, and machine learning have allowed contemporary businesses to better understand and predict human behavior. In scientific research big data have been widely used to study consumer journey and opinions. One of the tools enabling forecasting of sales volume is the Bass diffusion model, which universal nature has been proven in many applications in forecasting the sale of products belonging to various market segments. This article considers the use of BD as exogenous variables in the Bass model to predict the sales of tourist packages. Research aims. The purpose of the research is to assess the impact of using big data on improving the accuracy of forecasts for the sale of tourist packages. The Generalized Bass Model (GBM) has been thus expanded to include big data, which means that exogenous variables include: (1) marketer-generated content (MGC) and (2) user-generated content (UGC), including volume of web search and blog posts. Methodology. This article analyzes online news, blog posts and web search traffic volume related to tourist packages, and then integrates the information into the Bass model, treating it as part of the exogenous variables representing the marketing efforts of tour operators. It has been assumed that the volume of tour operators’ web news is a proxy for content generated by marketers (MGC), while the volume of blog posts and web search traffic constitute user-generated content (UGC). Key findings. The empirical analysis found that by incorporating big data into the Bass model provides more accurate prediction of tourist packages’ sales volume. In addition, UGC (as an exogenous variable) is better at predicting sales volume than MGC. UGC is a fairly good tool explaining the level of interest and involvement of potential tourists. However, it has been shown that forecasting efficiency is different for blog posts and web search traffic volumes.

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Section: Literature Review On Innovation Diffusion and Bass Modelmentioning

confidence: 99%

The Use of Big Data in Tourism Sales Forecasting

Kachniewska

2020

IJCM

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“…For instance, Bass et al [3], Danaher et al [19] and Boehner and Gold [20] proposed a GBM that included exogenous variables representing marketing mix and compared its prediction accuracy with that of the Bass diffusion model. Researchers also frequently considered government subsidies and policies as exogenous variables in forecasting the adoption of solar photovoltaic (PV) systems [21][22][23].…”

Section: Literature Reviewmentioning

confidence: 99%

The Use of Big Data and Its Effects in a Diffusion Forecasting Model for Korean Reverse Mortgage Subscribers

2020

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In recent years, big data has been widely used to understand consumers’ behavior and opinions. With this paper, we consider the use of big data and its effects in the problem of projecting the number of reverse mortgage subscribers in Korea. We analyzed web-news, blog post, and search traffic volumes associated with Korean reverse mortgages and integrated them into a Generalized Bass Model (GBM) as a part of the exogenous variables representing marketing effort. We particularly consider web-news volume as a proxy for marketer-generated content (MGC) and blog post and search traffic volumes as proxies for user-generated content (UGC). Empirical analysis provides some interesting findings: First, the GBM by incorporating big data is helpful for forecasting the sales of Korean reverse mortgages, and second, the UGC as an exogenous variable is more useful for predicting sales volume than the MGC. The UGC can explain consumers’ interest relatively well. Additional sensitivity analysis supports that the UGC is important for increasing sales volume. Finally, prediction performance is different between blog posts and search traffic volumes.

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“…Data requirements are generally modest, but depend on the specification (e.g., historical independent and dependent variables for each region of interest). Bass diffusion models are currently the most frequently used method to forecast DER adoption (Wang, Yu, and Johnson 2017;Guidolin and Mortarino 2010). These methods are popular because they are easy to specify and are intended to represent the growth patterns of a new product.…”

Section: Predicting Future Der Growthmentioning

confidence: 99%