A choice-based multi-product diffusion model incorporating replacement demand

This study proposes an integrated forecasting model that incorporates both first purchase diffusion and replacement component of sales in predicting multi-product diffusion patterns. The model consists of a two-stage procedure that customers undertake during purchase occasions according to a diffusion process or replacement process, and at each occasion, they make purchase incidence decisions and product choices according to a choice model. By incorporating various exogenous factors in the choice model, the model can identify the impact of each factor on customers' purchase incidence decisions and choice decisions among various product sub-categories. This approach enables us to understand the overall process of customers' purchasing behavior and to separate total sales amount into sales to three consumer segments: first-time buyers, customers who repurchase the same product category and customers who change their previous product category. We applied the proposed model in the forecasting multi-product framework; forecasting sub-category level automobile sales and brand level mobile terminal sales in the South Korean market. The results of the empirical studies showed that incorporating the replacement components into the multi-product diffusion framework is useful not only in understanding the demand interaction among automobile categories and among mobile handset brands, but also in producing more accurate long-term sales forecasts compared to previous multi-product diffusion models.     

Analysis of product efficiency in the Korean automobile market from a consumer’s perspective

In this study we develop and describe a conceptual and methodological framework to measure technical and allocative efficiency at the product level considering consumer choice, which encompasses overall efficiency. Empirically, we combined data envelopment analysis and a discrete choice model in order to measure efficiency levels. The suggested framework is applied to the Korean automobile market. The relationship between the level of efficiency and market performance is discussed in terms of market share.     

Forecasting future demand for large-screen television sets using conjoint analysis with diffusion model

The methodological framework proposed in this paper addresses two limitations of the basic Bass diffusion model: that it does not reflect competition among products nor does it forecast demand for products that do not exist in the marketplace. The model consists of four steps. First, to investigate consumer preferences for product attributes, we use conjoint analysis to estimate the utility function of consumers. Next we estimate the dynamic price function of each competing product to reflect technological changes and the evolving market environment. Then we derive dynamic utility function by combining the static utility function and the price function. Finally, we forecast the sales of each product using estimated market share and sales data for each period, which are derived from the dynamic utility function and from the Bass diffusion model, respectively.We apply this model to South Korea's market for large-screen televisions. The results show that (1) consumers are sensitive to picture resolution and cost and (2) in the near future, should the market see the introduction of liquid crystal display (LCD) TVs with screens larger than 50 inches, the high resolution and steep price drop of LCD will lead LCD TVs to capture a larger market share than TVs with other display types. Finally, our results show that TVs with 40-inch screens are preferred over TVs with larger screens.     

Estimating the geographic diffusion of the videocassette recorder market

The majority of studies concerning diffusion or product growth of consumer durables have treated the U.S. market as a whole and have applied the diffusion model on the assumption that the market exhibits a homogeneous response in its diffusion process. If the market is heterogeneous, however, an aggregate model entails a misspecification problem which could adversely affect the applicability and efficiency of the model. A modeling framework is developed for analyzing the diffusion process in a possibly heterogeneous market. Empirical analysis using data on the videocassette recorder (VCR) market reveals that the modeling framework captures to a fair extent heterogeneous diffusion processes across different regions in the U.S. market. Managerial implications are derived and discussed.     

基于品牌经济学的名人代言机制分析

本文在品牌经济学的框架下,分析了名人代言提高销量和价格的机制以及名人代言策略成功的条件。认为名人代言实际上是一种品牌联合策略,通过名人品牌与原产品品牌形成联合品牌而改变了品牌信用度,从而改变了消费者的选择成本,改变了消费者的需求函数从而改变了销量或价格。通过引入品牌契合系数构建联合品牌函数,分析了名人代言提高品牌信用度的条件。通过联合品牌信用函数分析,本文证明,只有当产品品牌和名人品牌的品牌信用、二者品类契合系数均达到一定水平时,名人代言策略才会成功,还证明了品类契合对于代言成败影响超过了名人本身的品牌信用度,选择名人代言的第一要素是品类契合度,第二才是名人品牌信用度。     

Individual Choice Modelling with Discrete Commodities: Theory and Application to the Tasman Bridge Reopening*

Many choice situations facing individual consumers entail selection of one alternative out of a set of discrete commodities. The essential elements of a theory of individual choice behaviour are outlined for the situation where the alternatives are discrete. An operationally tractable model is proposed within the theoretical framework but with certain assumptions. The model, multinomial logit, is applied to a study of the prediction of mode and route market shares associated with the reopening of the Tasman Bridge. The empirical study is primarily designed to illustrate the potential of this relatively new microeconomic approach to demand modelling.     

Modeling technological change in energy demand forecasting: A generalized approach

Conventional economic modeling of energy demand has characterized technological choice as an investment decision driven primarily by the relationship between capital costs and operating costs. Yet the implementation of this approach has tended to yield unrealistically high estimates of the implicit discount rate governing investment decisions, particularly those involving energy efficient technologies. This result arises from incomplete specification of the process of technological choice and the diffusion of innovations. General models of diffusion include conventional costs as one set of factors among many others that influence the spread of new technologies. These more general models have been widely applied to the adoption of other new or improved products, and their use in energy demand forecasting would lead to more accurate and reliable projections. Modification of the forecasts would have policy implications. In particular, the cost of a strategy to reduce greenhouse gas emissions by encouraging more rapid diffusion of energy efficient technologies is likely to be considerably smaller than would be suggested by the conventional economic models.     

Labor migration in Europe: experiences from Germany after unification

"This paper examines the effects of diverging economic conditions on labor migration within reunified Germany. We employ a life-cycle model with consumers' sovereignty regarding choice of location to derive estimates of labor migration in future periods. Heterogeneity of individuals is explicitly taken into account by adopting a random utility approach....Within limits, our predictions can serve as rough indicators of potential future migration between Eastern and Western Europe."     

A simple model of a speculative housing market

We develop a simple model of a speculative housing market in which the demand for houses is influenced by expectations about future housing prices. Guided by empirical evidence, agents rely on extrapolative and regressive forecasting rules to form their expectations. The relative importance of these competing views evolves over time, subject to market circumstances. As it turns out, the dynamics of our model is driven by a two-dimensional nonlinear map which may display irregular boom and bust housing price cycles, as repeatedly observed in many actual markets. Complex interactions between real and speculative forces play a key role in such dynamic developments.     

A product diffusion model incorporating repeat purchases

A logistic-based model for forecasting the rate of product diffusion given aggregate time series data was constructed. The model differs from earlier models based on fitting the logistic to aggregate data in that it includes a submodel to separate replacement demand from first-time sales. We fit the theoretical model to data and show that forecasts will be significantly more accurate using this model instead of the logistic curve.     

Technology product evolution and the diffusion of new product features

The evolution of technology products can be analysed on multiple levels. Product categories go through continuous evolution determined by the cumulative changes in the features of new product models. This is manifested in the diffusion of new product features and in the increasingly vague boundaries between different product generations. This article develops an approach for planning and forecasting technology product evolution and the diffusion of new product features. This is achieved by isolating the phenomena underlying the evolution process, and formulating the process at the product category, product feature, and product model levels. The approach is derived from these formulations combining the primarily demand-driven product category diffusion and product unit replacement behaviour, and the more supply-driven product feature dissemination. The approach enables meaningful sensitivity analysis including the analysis of discontinuities. The developed approach is applied to characterise the evolution of an example product category of mobile handsets and to forecast the diffusion of mobile handset features using extensive longitudinal and cross-sectional data collected from Finland. In consequence, the process of technology product evolution and the phenomenon of product feature dissemination are suggested as extensions to research on product category diffusion and replacement.     

Cellular telephones in the Israeli market: the demand,the choice of provider and potential revenues

This study presents the methodology, the estimates and a scenario for forecasting the demand for cellular telephones and their use in Israel. The analysis was based on the integration of three sub-models. The estimate of the consumer's decision on whether to purchase a cellular telephone and what type was obtained by using a discrete choice model of the multinomial logit type. The total number of cellular telephone purchases to be made in Israel during the years 1998–2008 was estimated using a logistic growth model employing aggregate data over time. The anticipated demand for airtime was based on findings of a survey carried out on a national sample, and on actual usage data on the various consumers during the years prior to the survey. The research shows the substantial economic potential of Israel's cellular telephone market.     

Analysis on the growth of telecommunication services: a global comparison of diffusion patterns

With the development of the Internet, the telecommunication market has seen a rapid shift from voice-based services to data-based services. Relationships of substitutability and complementarity have emerged among communications technology services such as Internet service, mobile phone service and fixed telephone service. The article analyses the diffusion patterns of communication services in different continents. A modified logistic growth model is estimated using panel data for the years 1975–2004. The research forms a valuable framework for forecasting demand for new services based on the diffusion of incumbent services and can inform strategies for entering the communications network industry.     

The UK demand for steel

The paper suggests a short-run model of the demand for steel that may be used for forecasting future trends. The paper commences with consideration of a long-run model which is estimated using cointegration analysis. An error correction model is then developed to depict the short-run movements to equilibrium. This can be used for the purpose of ex-post forecasting.     

An evolutionary model of recycling and product lifetime extension

This paper presents a model-based analysis of the development and diffusion of so-called ‘green’ products, i.e. those which are easily recyclable and which have a long lifetime. We address this problem by developing an evolutionary simulation model to identify the impact of the environmental R&D strategies of business firms on the economy and the environment. The simulation results show that R&D investment both in product recyclability and in product lifetime extension can be positive for the firm. Adopting such a strategy means that the firm can then market green products, which provides it with a competitive advantage on firms investing mainly in product recyclability whatever consumers' preferences. From an environmental point of view, the diffusion of green products will reduce both waste flows in the economy and pressure on virgin resources. However, diversifying R&D investment to develop both product recyclability and lifetime can mean slowing down the flow of recycled materials and may lead to even greater quantities of unrecycled waste. Our results suggest introducing regulation policies aiming at encouraging firms to invest both in product recyclability and lifetime in order to benefit from their complementarities. Furthermore, simulations show that it should be better to direct environmental policies on firms' environmental innovation strategies than on demand attributes because significant changes in these strategies would provide much more radical environmental changes.     

Multiple generation product life cycle predictions using a novel two-stage fuzzy piecewise regression analysis method

Product life cycle (PLC) prediction plays a crucial role in strategic planning and policy definition for high-technology products. Forecast methodologies which can predict PLCs accurately can help to achieve successful strategic decision-making, forecasting, and foresight activities in high-technology firms, research institutes, governments, and universities. Over the past few decades, even though analytic framework strategies have been proposed for production, marketing, R&D (research and development), and finance, aiming at each stage of PLCs, forecast methodologies with which to predict PLCs are few. The purpose of this research is to develop a novel forecast methodology to allow for predictions of product life time (PLT) and the annual shipment of products during the entire PLC of multiple generation products. A novel two-stage fuzzy piecewise regression analysis method is proposed in this paper. In the first stage, the product life-time of the specific generation to be analyzed will be predicted by the fuzzy piecewise regression line that is derived based upon the product life-time of earlier generations. In the second stage of the forecast methodology, the annual shipment of products of the specified generation will be predicted by deriving annual fuzzy regression lines for each generation, based upon the historical data on the earlier generations' products. An empirical study predicting the life-time and the annual shipment of the 16 Mb (Mega bit) DRAM (Dynamic Random Access Memory) PLC is illustrated to validate the analytical process. The results demonstrate that two-stage fuzzy piecewise regression analysis can predict multiple generation PLT and PLC precisely, thereby serving as a foundation for future strategic planning, policy definitions and foresights.     

OWA-based ANFIS model for TAIEX forecasting

In stock market forecasting, high-order time-series models that use previous several periods of stock prices as forecast factors are more reasonable to provide a superior investment portfolio for investors than one-order time-series models using one previous period of stock prices. However, in forecasting processes, it is difficult to deal with high-order stock data, because it is hard to give a proper weight to each period of past stock price, reduce data dimensions without losing stock information, and produce a comprehensive forecasting result based on stock data with nonlinear relationships.Additionally, there are two more drawbacks to past time-series models: (1) some assumptions (Bollerslev, 1986; Engle, 1982) about stock variables are required for statistical methods, such as the autoregressive model (AR) and autoregressive moving average (ARMA); (2) numeric time-series models have been presented to deal with forecasting problems for stock markets, but they can not handle the nonlinear relationships within the stock prices.To address these shortcomings, this paper proposes a new time series model, which employs the ordered weighted averaging (OWA) operator to fuse high-order data into the aggregated values of single attributes, a fusion adaptive network-based fuzzy inference system (ANFIS) procedure, for forecasting stock price in Taiwanese stock markets.In verification, this paper employs a seven-year period of the TAIEX stock index, from 1997 to 2003, as experimental datasets and the root mean square error (RMSE) as evaluation criterion. The experimental results indicate that the proposed model is superior to the listing methods in terms of root mean squared error.     

Human capital and economic growth: A macroeconomic model for Pakistan

The paper presents a small macro model for Pakistan economy focusing the impact of investment in human capital on the key macroeconomic variables. The demand side is modeled along the Keynesian lines while the supply side is modeled as per neoclassical theory of production. This framework allows analyzing the effects of investment in human capital on supply side variables (like labor, physical and human capital) and demand side variables (like consumption and investment) at the same time.The model has small forecasting horizon in which three alternative scenarios regarding government spending on education are evaluated from 2012 to 2016. The model shows that the link between human capital and labor market is weak however a change in education spending affects output through enhancing productivity and through multiplier-accelerator principle. Though the model is small in size and forecasting horizon, it can help in evaluating the future paths of key macroeconomic variables associated with education spending.