• Title/Summary/Keyword: Bass model

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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.

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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Analyzing and Forecating of Event Visitation :Applicaton of Bass'Model of Diffusion Process (배스의 확산모형을 이용한 이벤트 방문수요 상측에 관한 연구)

  • 엄서호
    • Journal of the Korean Institute of Landscape Architecture
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    • v.26 no.1
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    • pp.51-58
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    • 1998
  • The opening of an event in a given geographical area may be defined as an innovation. Visitors to the event adopt the innovation; therefore, their visitation patterns since the opening can be regarded as a diffusion process. Bass' model of diffusion process was applied to analyzing weekly visitation of Kwang-ju Viennale. Parameters of the Bass' model were estimated by regression analysis, and then reviewed in terms of applicability. Actual estimation of event visitation was implemented by calculation of the three parameters of the model based on the actual data. After comparing estimated value with actual value, it was concluded that Bass' model is applicable to estimating event visitation as far as it is the only prediction method available at this point.

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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.

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.

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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  • 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.

    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.

    Forecasting Market Demand of u-Transportation Vehicle Sensor OBU (u-Transportation UVS 단말기 시장수요예측)

    • Jeong, Eon-Su;Kim, Won-Kyu;Kim, Min-Heon;Kim, Byung-Jong;Kim, Song-Ju
      • Journal of The Institute of Information and Telecommunication Facilities Engineering
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      • v.8 no.4
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      • pp.157-162
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      • 2009
    • This study's purpose is to forecast the market demand of UVS (u-Transportation Vehicle Sensor) OBU (On-board Unit) of the ubiquitous Transportation. Bass model, Logistic model, and Gompertz model were used for the forecasting market demand. Firstly, this research focused on the market size for the u-T OBU. All three models were used for the market size prediction and the average values were used. The Bass model were calibrated and the market demand for the UVS OBU of the u-Transportation system were estimated using this model.

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    Analysis of Diffusion Pattern in New Product and Services Based on Two-pieces Bass Model (신제품 및 서비스에 있어 이분조각 Bass모형에 의한 확산 패턴 분석)

    • Hong, Seok-Kee;Hong, Jung-Sik
      • IE interfaces
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      • v.23 no.4
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      • pp.337-348
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      • 2010
    • The Bass model is the most widely used model in research of new product diffusion because it presents a nice explanation on the diffusion process of new products. However, it has a limitation that its performance of fitness is lower as the available data become less and also, the diffusion curve is bell-shape and so, it can not represent the various diffusion patterns. Recently, a two-pieces Bass model is developed and applied to analyze diffusion of 10 products. The results are encouraging in terms of fitness. However, diffusion pattern is not dealt with in the paper. In this paper, analysis of diffusion pattern is in depth addressed in two-pieces Bass model. It is shown that the diffusion curves are divided into 3 types with respect to the peak adoption rate and each type is divided into 2 types further. Takeoff time of a diffusion process is analyzed by using the inflection point and regime-change time where it represents the point that imitation and innovation parameters change. Empirical studies for 68 products(28 domestic products and 40 USA products) are performed to analyze the diffusion pattern. Findings are that diffusion patterns of all products except 1 USA product show type I and regime-change time becomes shorter as the introduction time of the product is later in domestic products and regime-change time can be regarded as a takeoff time in 47% of total 68 products.