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This chapter aims to assess how effective loyalty programs are in contributing to retaining guests for hotels. The effectiveness is measured by means of a Bass model which allows the measurement of the diffusion patterns of adopters within potential adopters. The data used to perform this model allow the depiction of the effect of geographical localization over a time frame of three years. Results suggest that the loyalty card’s acceptance was measured from the internal and external parameters, based on the concept of diffusion theory. The results indicated a need for innovation of the loyalty program from 2019. Due to the existence of several hotels with different typologies in different countries, a segmentation of clients by nationalities is suggested with a “waterfall” strategy being placed in the hotel chain loyalty program.

We develop a model to analyze the growth of social organizations as a spatially nested mixed-influence diffusion process. Drawing on gravity models and threshold models, we split the social system into social units and model the diffusion process as a system of differential equations. The diffusion of a new organizational form in a social unit is a function of an internal process within the unit and external processes in the surrounding units. The model is confronted with data on the growth of trade unions in Stockholm, Sweden, between 1890 and 1940.

This paper gives a selective review on some recent developments of nonparametric methods in both continuous and discrete time finance, particularly in the areas of nonparametric estimation and testing of diffusion processes, nonparametric testing of parametric diffusion models, nonparametric pricing of derivatives, nonparametric estimation and hypothesis testing for nonlinear pricing kernel, and nonparametric predictability of asset returns. For each financial context, the paper discusses the suitable statistical concepts, models, and modeling procedures, as well as some of their applications to financial data. Their relative strengths and weaknesses are discussed. Much theoretical and empirical research is needed in this area, and more importantly, the paper points to several aspects that deserve further investigation.

Much attention has been given to adoption and diffusion, defined as the degree of market penetration, of Information and Communications Technologies (ICT) in recent years (Carter, Jambulingam, Gupta, & Melone, 2001; Kiiski & Pohjola, 2002; Milner, 2003; Benhabib & Spiegel, 2005). The theory of diffusion of innovations considers how a new idea spreads throughout the market over time. The ability to accurately predict new product diffusion is of concern to designers, marketers, managers, and researchers alike. However, although the diffusion process of new products is generally accepted as following an s-curve pattern, where diffusion starts slowly, grows exponentially, peaks, and then declines (as shown in Fig. 1), there is considerable disagreement about what factors affect diffusion and how to measure diffusion rates (Bagchi, Kirs, & Lopez, 2008).

The time series of the federal funds rate has recently been extended back to 1928, now including several episodes during which interest rates remained near the lower bound of zero. This series is analyzed, using the method of indirect inference, by applying recent research on bounded time series to estimate a set of bounded parametric diffusion models. This combination uncouples the specification of the bounds from the law of motion. Although Louis Bachelier was the first to use arithmetic Brownian motion to model financial time series, he has often been criticized for this proposal, since the process can take on negative values. Most researchers favor processes such as geometric Brownian motion (GBM), which remains positive. Under this framework, Bachelier's proposal remains valid when specified with bounds and is shown to compare favorably when modeling the federal funds rate.

Production operations managers have long been concerned about new product development and the life cycle of these products. Because many products do not sell at constant levels throughout their lives, product life cycles (PLCs) must be considered when developing sales forecasts. Innovation diffusion models have successfully been employed to investigate the rate at which goods and/or services pass through the PLC. This research investigates innovation diffusion models and their relation to the PLC. The model is developed and then tested using modem sales from June 1994 to May 2006.

Online social networks are increasingly important venues for businesses to promote their products and image. However, information propagation in online social networks is significantly more complicated compared to traditional transmission media such as newspaper, radio, and television. In this chapter, we will discuss research on modeling and forecasting diffusion of virally marketed content in social networks. Important aspects include the content and its presentation, the network topology, and transmission dynamics. Theoretical models, algorithms, and case studies of viral marketing will be explored.