The forecasting of the mobile Internet in Taiwan by diffusion model

Chu, Chih‐Peng; Pan, Jin-Gu

doi:10.1016/j.techfore.2007.11.012

Cited by 54 publications

(21 citation statements)

References 18 publications

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“…Firth et al (2006) used the Bass model to forecast the number of people that join the Internet community. Further, the Bass model has been applied to analyse the diffusion pattern of mobile phones (Wu and Chu, 2010;Michalakelis et al, 2008) and mobile Internet (Chu and Pan, 2008) with results confirming the general utility of the Bass model in predicting the diffusion pattern of technological products. However, modelling the diffusion pattern of online shopping has received little attention from researchers.…”

Section: Technological Market Growth Modelsmentioning

confidence: 69%

The diffusion of online shopping in Australia: Comparing the Bass, Logistic and Gompertz growth models

Naseri¹,

Elliott

2013

J Market Anal

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is currently employed by a major multinational telecommunications company in a senior management role. His research has been published in international academic journals and proceedings of international conferences. His research and professional interests are focused on direct and database marketing, data mining, empirical model comparison and modelling the diffusion of new products and technologies. Greg Elliottis a Professor of Business (Marketing) at Macquarie University, Australia. He has held academic positions at a number of Australian and overseas universities. His research has been concentrated in the fields of marketing theory, financial services, services marketing and international marketing. He is also active in researching Chinese consumer behaviour.Correspondence: Greg Elliott, Marketing and Management, Macquarie University, Herring Road, North Ryde, NSW, 2109, Australia ABSTRACT The results of past studies that compared the performance of alternative growth models are generally inconclusive. The objective of the current study is to provide further empirical evidence regarding the performance of three popular growth curves, namely, the Bass, Logistic and Gompertz models in the context of online shopping diffusion in Australia. The results of model fitting to an online shopping time series (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009) show that all three models represent the diffusion curve quite well and adequately; however, the Bass model described the online shopping diffusion curve more accurately than the other two models. Forecasting with early diffusion data (1998)(1999)(2000)(2001)(2002) suggests that the Bass, Logistic and Gompertz models are unable to adequately describe the diffusion curve from limited data. Nevertheless, the Bass model with the adjusted market potential coefficient (m) produced forecast accuracy that is comparable to the Bass model fitted to the full data (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009). Overall, our results suggest that the Bass model outperforms the Logistic and Gompertz models.

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Section: Technological Market Growth Modelsmentioning

confidence: 69%

The diffusion of online shopping in Australia: Comparing the Bass, Logistic and Gompertz growth models

Naseri¹,

Elliott

2013

J Market Anal

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“…The Bass model has been used for a wide variety of real-world problems such as forecasting (Chu and Pan, 2008;Tseng and Hu, 2009), new product growth (Cheng, 2012;Chiang and Wong, 2011;Kaldasch, 2011), and innovation diffusion (Cho and Koo, 2012;Meade and Islam, 2006;Peres et al, 2010).…”

Section: Renewable Energy Technology Diffusion Modelingmentioning

confidence: 99%

Renewable energy technology diffusion: an analysis of photovoltaic-system support schemes in Medellín, Colombia

Radomes

Arango

2015

Journal of Cleaner Production

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“…The adoption growth for these models is also analyzed with respect to promotional eff ort intensity function. Chu et al (2008) proposed a diff usion model that reveals the growth pattern of the mobile Internet subscriber in Taiwan utilizing the concepts of the "technical substitution" and the "multi-product competition". …”

Section: Literature Reviewmentioning

confidence: 99%

Innovation Diffusion Model for a Product Incorporating Segment-specific Strategy and the Spectrum Effect of Promotion

Jha¹,

Aggarwal²,

Gupta

et al. 2014

Journal of Statistics and Management Systems

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Promotion is an important component of a fi rm's marketing mix. It assists in dispersing the product information into minds of the customers and takes them to the fi nal stage of purchase. Various promotional strategies are used by fi rms to capture maximum potential adopters in the market. Firms adopt segment driven marketing to best utilize its fi nite marketing resources. Mass market promotion and diff erentiated market promotion are amongst the two widely used techniques of promotion used in a heterogeneous market, segmented into homogeneous segments. Through mass market promotion, a product is promoted in the entire market, using a common promotional strategy, thereby creating a spectrum eff ect in all the segments of the market. Whereas, diff erentiated market promotion targets each segment of the market with distinct promotional strategies. In a segmented market, both types of promotional strategies play diff erent and important roles for product adoption in the market. Innovation diff usion models are used to describe the adoption growth of durable technology products over their product life cycle. There is vast literature of innovation diff usion modeling. The existing literature mostly assumes a homogeneous market while formulating the model and eff ect of mass and diff erentiated promotion techniques on adoption growth in a segmented market is not yet analyzed. In this paper, we develop an innovation diff usion model for a durable technology consumer product considering the combined eff ect of mass and diff erentiated promotion along with the internal infl uence factors on adoption growth in segmented market. Model validity is tested on a real time data in four segments. Fairly good results have been obtained.

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The forecasting of the mobile Internet in Taiwan by diffusion model

Cited by 54 publications

References 18 publications

The diffusion of online shopping in Australia: Comparing the Bass, Logistic and Gompertz growth models

The diffusion of online shopping in Australia: Comparing the Bass, Logistic and Gompertz growth models

Renewable energy technology diffusion: an analysis of photovoltaic-system support schemes in Medellín, Colombia

Innovation Diffusion Model for a Product Incorporating Segment-specific Strategy and the Spectrum Effect of Promotion

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