• Title, Summary, Keyword: Bass Model

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Two Pieces Extension of the Bass Diffusion Model (Bass 확산모형의 이분 확장)

  • Hong, Jung-Sik;Eom, Seok-Jun
    • Journal of the Korean Operations Research and Management Science Society
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    • v.34 no.4
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    • pp.15-26
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    • 2009
  • Bass diffusion model have played a central role in studying the diffusion of the new products since 1969, the year of publication of Bass model. Almost 750 publications based on the Bass diffusion model have explored extensions and applications. Extension models can be divided into two types. One is the model containing marketing-mix variables and the other is the model containing additional parameters. This paper presents another extension model of the latter type. Our model allows the time varying coefficients of innovation and imitation. Two pieces approximation of time varying coefficients is introduced and it's parameters are estimated based on NLS(Non-Linear Mean Square) method. Empirical studies are performed and the results show that our model is superior to the basic Bass model and the NUI(Non-Uniform Influence) model which is the well-known extension of the Bass model. The model developed in this paper is, also, transformed into the Bass model with the ready potential adopters in order to enhance the descriptive power.

The Demand Forecasting of Game Products by Bass Model (Bass모델을 응용한 게임제품의 수요예측)

  • Lee, Ji-Hun;Jung, Heon-Soo;Kim, Hyoung-Gil;Jang, Chang-Ik
    • Journal of Korea Game Society
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    • v.4 no.1
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    • pp.34-40
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    • 2004
  • This study introduces and empirically test the validity of Bass model that helps demand forecasting of new game products. The application of Bass model to new game products show that Bass model predicts the demand of new game accurately. In particular, it showed very good predictability of on-line game products.

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Prediction of movie audience numbers using hybrid model combining GLS and Bass models (GLS와 Bass 모형을 결합한 하이브리드 모형을 이용한 영화 관객 수 예측)

  • Kim, Bokyung;Lim, Changwon
    • The Korean Journal of Applied Statistics
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    • v.31 no.4
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    • pp.447-461
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    • 2018
  • Domestic film industry sales are increasing every year. Theaters are the primary sales channels for movies and the number of audiences using the theater affects additional selling rights. Therefore, the number of audiences using the theater is an important factor directly linked to movie industry sales. In this paper we consider a hybrid model that combines a multiple linear regression model and the Bass model to predict the audience numbers for a specific day. By combining the two models, the predictive value of the regression analysis was corrected to that of the Bass model. In the analysis, three films with different release dates were used. All subset regression method is used to generate all possible combinations and 5-fold cross validation to estimate the model 5 times. In this case, the predicted value is obtained from the model with the smallest root mean square error and then combined with the predicted value of the Bass model to obtain the final predicted value. With the existence of past data, it was confirmed that the weight of the Bass model increases and the compensation is added to the predicted value.

Forecasting the Diffusion of Innovative Products Using the Bass Model at the Takeoff Stage: A Review of Literature from Subsistence Markets

  • Mitra, Suddhachit
    • Asian Journal of Innovation and Policy
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    • v.8 no.1
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    • pp.141-161
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    • 2019
  • A considerable amount of research has been directed at subsistence markets in the recent past with the belief that these markets can be tapped profitably by marketers. Consequently, such markets have seen the launch of a number of innovative products. However, marketers of such forecasts need timely and accurate forecasts regarding the diffusion of their products. The Bass model has been widely used in marketing management to forecast diffusion of innovative products. Given the idiosyncrasies of subsistence markets, such forecasting requires an understanding of effective estimation techniques of the Bass model and their use in subsistence markets. This article reviews the literature to achieve this objective and find out gaps in research. A finding is that there is a lack of timely estimates of Bass model parameters for marketers to act on. Consequently, this article sets a research agenda that calls for timely forecasts at the takeoff stage using appropriate estimation techniques for the Bass model in the context of subsistence markets.

Spatial effect on the diffusion of discount stores (대형할인점 확산에 대한 공간적 영향)

  • Joo, Young-Jin;Kim, Mi-Ae
    • Journal of Distribution Research
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    • v.15 no.4
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    • pp.61-85
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    • 2010
  • Introduction: Diffusion is process by which an innovation is communicated through certain channel overtime among the members of a social system(Rogers 1983). Bass(1969) suggested the Bass model describing diffusion process. The Bass model assumes potential adopters of innovation are influenced by mass-media and word-of-mouth from communication with previous adopters. Various expansions of the Bass model have been conducted. Some of them proposed a third factor affecting diffusion. Others proposed multinational diffusion model and it stressed interactive effect on diffusion among several countries. We add a spatial factor in the Bass model as a third communication factor. Because of situation where we can not control the interaction between markets, we need to consider that diffusion within certain market can be influenced by diffusion in contiguous market. The process that certain type of retail extends is a result that particular market can be described by the retail life cycle. Diffusion of retail has pattern following three phases of spatial diffusion: adoption of innovation happens in near the diffusion center first, spreads to the vicinity of the diffusing center and then adoption of innovation is completed in peripheral areas in saturation stage. So we expect spatial effect to be important to describe diffusion of domestic discount store. We define a spatial diffusion model using multinational diffusion model and apply it to the diffusion of discount store. Modeling: In this paper, we define a spatial diffusion model and apply it to the diffusion of discount store. To define a spatial diffusion model, we expand learning model(Kumar and Krishnan 2002) and separate diffusion process in diffusion center(market A) from diffusion process in the vicinity of the diffusing center(market B). The proposed spatial diffusion model is shown in equation (1a) and (1b). Equation (1a) is the diffusion process in diffusion center and equation (1b) is one in the vicinity of the diffusing center. $$\array{{S_{i,t}=(p_i+q_i{\frac{Y_{i,t-1}}{m_i}})(m_i-Y_{i,t-1})\;i{\in}\{1,{\cdots},I\}\;(1a)}\\{S_{j,t}=(p_j+q_j{\frac{Y_{j,t-1}}{m_i}}+{\sum\limits_{i=1}^I}{\gamma}_{ij}{\frac{Y_{i,t-1}}{m_i}})(m_j-Y_{j,t-1})\;i{\in}\{1,{\cdots},I\},\;j{\in}\{I+1,{\cdots},I+J\}\;(1b)}}$$ We rise two research questions. (1) The proposed spatial diffusion model is more effective than the Bass model to describe the diffusion of discount stores. (2) The more similar retail environment of diffusing center with that of the vicinity of the contiguous market is, the larger spatial effect of diffusing center on diffusion of the vicinity of the contiguous market is. To examine above two questions, we adopt the Bass model to estimate diffusion of discount store first. Next spatial diffusion model where spatial factor is added to the Bass model is used to estimate it. Finally by comparing Bass model with spatial diffusion model, we try to find out which model describes diffusion of discount store better. In addition, we investigate the relationship between similarity of retail environment(conceptual distance) and spatial factor impact with correlation analysis. Result and Implication: We suggest spatial diffusion model to describe diffusion of discount stores. To examine the proposed spatial diffusion model, 347 domestic discount stores are used and we divide nation into 5 districts, Seoul-Gyeongin(SG), Busan-Gyeongnam(BG), Daegu-Gyeongbuk(DG), Gwan- gju-Jeonla(GJ), Daejeon-Chungcheong(DC), and the result is shown

    . In a result of the Bass model(I), the estimates of innovation coefficient(p) and imitation coefficient(q) are 0.017 and 0.323 respectively. While the estimate of market potential is 384. A result of the Bass model(II) for each district shows the estimates of innovation coefficient(p) in SG is 0.019 and the lowest among 5 areas. This is because SG is the diffusion center. The estimates of imitation coefficient(q) in BG is 0.353 and the highest. The imitation coefficient in the vicinity of the diffusing center such as BG is higher than that in the diffusing center because much information flows through various paths more as diffusion is progressing. A result of the Bass model(II) shows the estimates of innovation coefficient(p) in SG is 0.019 and the lowest among 5 areas. This is because SG is the diffusion center. The estimates of imitation coefficient(q) in BG is 0.353 and the highest. The imitation coefficient in the vicinity of the diffusing center such as BG is higher than that in the diffusing center because much information flows through various paths more as diffusion is progressing. In a result of spatial diffusion model(IV), we can notice the changes between coefficients of the bass model and those of the spatial diffusion model. Except for GJ, the estimates of innovation and imitation coefficients in Model IV are lower than those in Model II. The changes of innovation and imitation coefficients are reflected to spatial coefficient(${\gamma}$). From spatial coefficient(${\gamma}$) we can infer that when the diffusion in the vicinity of the diffusing center occurs, the diffusion is influenced by one in the diffusing center. The difference between the Bass model(II) and the spatial diffusion model(IV) is statistically significant with the ${\chi}^2$-distributed likelihood ratio statistic is 16.598(p=0.0023). Which implies that the spatial diffusion model is more effective than the Bass model to describe diffusion of discount stores. So the research question (1) is supported. In addition, we found that there are statistically significant relationship between similarity of retail environment and spatial effect by using correlation analysis. So the research question (2) is also supported.

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  • A Study on Forecast of Penetration Amount of High-Efficiency Appliance Using Diffusion Models (확산 모형을 이용한 고효율기기의 보급량 예측에 관한 연구)

    • Park, Jong-Jin;So, Chol-Ho;Kim, Jin-O
      • Journal of Energy Engineering
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      • v.17 no.1
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      • pp.31-37
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      • 2008
    • At present, the target amount of demand-side management and investment cost of EE (Energy Efficiency) program, which consists of high-efficiency appliances, has been estimated simply by the diffusion function based on the real historical data in the past or last year. In the internal and external condition, the penetration amount of each appliance has been estimated by Bass diffusion model which is expressed by time and three coefficients. And enough acquisition of real historical data is necessary for reasonable estimation of coefficients. In energy efficiency, to estimate the target amount of demand-side management, the penetration amount of each appliance should be primarily forecasted by Bass diffusion model in Korea. On going programs, however, lightings, inverters, vending machine and motors have a insufficient real historical data which is a essential condition to forecast the penetration amount using a Bass diffusion model due to the short period of program progress. In other words, the forecast of penetration amount may not be exact, so that it is necessary for the method of forecast to apply improvement of method. In this paper, the penetration amount of high-efficiency appliances is forecasted by Bass, virtual Bass, Logistic and Lawrence & Lawton diffusion models to analyze the diffusion progress. And also, by statistic standards, each penetration is compared with historical data for model suitability by characteristic of each appliance. Based on the these result, in the forecast of penetration amount by diffusion model, the reason for error occurrence caused by simple application of diffusion model and preferences of each diffusion model far a characteristic of data are analyzed.

    An Empirical Study of Technology Diffusion on the Internet using Bass Model (Bass 모형을 이용한 인터넷에서의 기술 확산에 대한 실증분석)

    • Nam, Ho-Hun;Yang, Kwang-Min
      • Journal of Digital Convergence
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      • v.6 no.2
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      • pp.55-64
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      • 2008
    • The Internet possesses not only features of mass media but also features of word of mouth communication. Communication channel is considered as one of most important variables in diffusion process. In this paper, we examined functionality of technology diffusion on the Internet through the use of meta tags. We have measured the coefficients of the Bass diffusion model which has been well-established in new product diffusion. This research shows that the Bass model is appropriate for describing technology diffusion on the Internet. The external influence as represented by the coefficient of innovation was found to be much smaller while the internal influence dominates in all meta tag diffusion. In meta tag diffusion, the internal influence as represented by the coefficient of imitation was increased at least twice bigger than that of consumer durables and information technology. Collecting necessary data in social sciences research can be a burden. This research shows that it can be alleviated through the use of software agents over the Internet. The research made use of software agents for collecting longitudinal data from publicly accessible archive such as Archive.org.

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    Comparison of the Bass Model and the Logistic Model from the Point of the Diffusion Theory (확산이론 관점에서 로지스틱 모형과 Bass 모형의 비교)

    • Hong, Jung-Sik;Koo, Hoon-Young
      • Journal of the Korean Operations Research and Management Science Society
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      • v.37 no.2
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      • pp.113-125
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      • 2012
    • The logistic model and the Bass model have diverse names and formulae in diffusion theory. This diversity makes users or readers confused while it also contributes to the flexibility of modeling. The method of handling the integration constant, which is generated in process of deriving the closed form solution of the differential equation for a diffusion model, results in two different 'actual' models. We rename the actual four models and propose the usage of the models with respect to the purpose of model applications. The application purpose would be the explanation of historical diffusion pattern or the forecasting of future demand. Empirical validation with 86 historical diffusion data shows that misuse of the models can draw improper conclusions for the explanation of historical diffusion pattern.

    Estimation of Semiconductor Market, Using NLS Diffusion Model (비선형회귀 확산모형을 이용한 반도체 시장수요 추정)

    • Kim, Gene;Khoe, Kyung-Il
      • Journal of Digital Convergence
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      • v.12 no.3
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      • pp.141-147
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      • 2014
    • Diffusion model is popular research topic in marketing and economy particularly for the areas of model specification and market size forecasting. In particular, Bass model can explain Roger's innovation diffusion and product life cycle through easy mathematical representation and hence the model has been widely used for the explanation of adopting innovative new products and technologies. Nonetheless, there're only a couple of pioneering researches about semiconductor market, using diffusion models. Consequently, we'd utilise NLS approach diffusion model to estimate the market potential of MOSFET, major switching device for power management of system, and explain the process to industry stakeholders and policy makers for delivery of managerial implication with pragmatic purpose.

    Network Based Diffusion Model (네트워크 기반 확산모형)

    • Joo, Young-Jin
      • Korean Management Science Review
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      • v.32 no.3
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      • pp.29-36
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      • 2015
    • In this research, we analyze the sensitivity of the network density to the estimates for the Bass model parameters with both theoretical model and a simulation. Bass model describes the process that the non-adopters in the market potential adopt a new product or an innovation by the innovation effect and imitation effect. The imitation effect shows the word of mouth effect from the previous adopters to non-adopters. But it does not divide the underlying network structure from the strength of the influence over the network. With a network based Bass model, we found that the estimate for the imitation coefficient is highly sensitive to the network density and it is decreasing while the network density is decreasing. This finding implies that the interpersonal influence can be under-looked when the network density is low. It also implies that both of the network density and the interpersonal influence are important to facilitate the diffusion of an innovation.