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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.

The Bass model is widely used in the literature to capture the diffusion of innovations and shows excellent predictive power in the context of durable goods. However, the model's efficacy fades when services are the target of analysis. Services that users adopt and subsequently utilize regularly are regarded as a continuous process that entails the possibility of dis-adoption and re-adoption. These aspects are not accounted for in the traditional Bass model. Thus, this study extends the Bass model to information system (IS)-based services by taking into account the unique nature of service adoption: the possibility of dis-adoption and re-adoption.

The proposed hypotheses were empirically tested using a longitudinal study of mobile service usage over 18 months. The longitudinal design provides a stronger position than the typical cross-sectional survey to understand the dynamics and infer causality.

Results show that the inclusion of the dis-adoption and re-adoption rates in the Bass model significantly improves the explanatory power over the traditional Bass model.

Consumption of services delivered through IS has exponentially increased. However, understanding on the diffusion pattern of IS-based services is limited. Our study is the first to examine the effect of dis-adoption and re-adoption together in the innovation diffusion process. The study offers significant implications for researchers and practitioners. The extended Bass model can help service firms develop an accurate prediction about the number of adopters at different periods of time.

The purpose of this paper is to present a modeling approach for aggregate and disaggregate level models for cluster‐based diffusion of a new technology. The aggregate approach refers to the diffusion modeling of a product at the overall population level, while the disaggregate approach refers to the diffusion process at the individual entity level.

The pattern of diffusion of a new technology in a representative two‐cluster situation is studied. In the aggregate level modeling, a diffusion model is developed in which potential adopters of both clusters learn about the new technology from each other. This is done by a Lotka‐Volterra type of dynamical system of equations. Then, to focus on relatively micro‐level phenomena, such as different propensities of imitation and innovation of firms within a cluster, an agent‐based disaggregate model for cluster‐based diffusion of technology is proposed. In these disaggregate models, the effects of heterogeneity and the inter‐cluster and intra‐cluster distances between the agents are captured.

The results highlight two major points: first, both aggregate and disaggregate models are in agreement with each other, and second, both of the models exhibit a form similar to the Bass model. Thus, consistent with the general theme of why the Bass model fits without decision variables, it is found that the Bass model, when extended appropriately, can be expected to work well also in the cluster‐based technology diffusion situation.

This modeling approach can be applied to the modeling of those situations in which heterogeneous industrial units are present in geographical clusters. It can also be applied in the related contexts such as diffusion of practices (e.g. quality certifications) within a multi‐divisional organization or across various networked clusters.

For a homogenous population, the Bass model has been used extensively to predict the sales of newly introduced consumer durables. In comparison, little attention has been given to the modeling of the technology adoption by the industrial units present in disparate groups, called clusters. The major contribution of this paper is to propose a framework for cluster‐based diffusion of technological products, and then to present an analysis of that framework using two different methodologies.

The purpose of this paper is to propose a model that predicts information communication technology (ICT) adoption in saturated markets, to analyze the shifting behaviors of the subscribers and to present the results.

By developing an expanded Bass model, the authors analyzed post‐adopters' switching behaviors in the mobile phone market, using the officially verified time‐series data for the number of mobile phone adopters in South Korea.

The results show that: the expanded Bass diffusion model can delineate the stream of post‐adopter's switching behaviors in saturated ICT markets; based on innovation value p and imitation value q, the authors conclude that customer immigrations during market saturated period are mainly caused by innovation effect, which is closely related to launching innovative products; and fast mover still has its imitation effect, which is closely related to first mover advantage.

The paper provides novel insights for the frame of practical strategies. To survive in a saturated market, late movers should continuously develop new and innovative products. In addition, fast movers should also develop innovative products to prevent customer immigrations, and they also should utilize their first mover advantage more efficiently, even in matured market.

The aim of this paper is to explore the benefit of forecasting emerging biomedical therapy technologies as well as the rate of diffusion of resultant biomedical products in the context of management of technology.

The research method is exploratory using a case study approach. Techniques such as bibliometric analysis and the Bass diffusion model are utilized to assess the growth rate and market penetration of pulsed electromagnetic field therapy (PEMF) as a technology.

The penetration and growth rate of user acceptance of the technology in a global context are simulated across a 15‐year period. The technology forecasting model is also used in a case study to simulate the penetration of a product using ten years' medical application data of a patented pulsed electromagnetic field for biomedical therapy application in the global context. Useful correlation between bibliometric data for PEMF and real data for the case study is illustrated.

The research is limited to the case of PEMF data presented. Further research may be done on other PEMF technology applications.

Aspects of a holistic management model that was developed for high technology companies are invoked in the practical realisation of the professional paradigm shift required when an emerging biomedical therapy technology is in the process of becoming mainstream.

It has been shown that technology diffusion traces exist for PEMF therapy technology as is evident from the bibliometric PEMF global data presented. Furthermore it is possible to simulate the PEMF therapy technology diffusion process with a Bass diffusion model incorporating innovation imitation and market size coefficients.

The purpose of this study is to measure the Arab States? innovation and imitation levels to understand the factors affecting their diffusion processes. The authors argue that sampling Arab States provides the literature on international diffusion with the ability to contrast between developing and developed countries regarding the diffusion process and to represent a different region with different characteristics both economic and cultural. As such, the authors investigated the diffusion patterns of seven Arab States, namely, Kuwait, the Kingdom of Saudi Arabia (KSA), the United Arab Emirates, Lebanon, Iraq, Libya and Egypt.

The present study mapped the respective Arab States’ diffusion patterns by applying the Bass model on their mobile cellular subscriptions data.

The paper’s contributions include estimating the Arab States diffusion patterns and distinguishing them according to their innovation and imitation coefficients. Findings indicate Kuwait and Libya as the most innovative countries, whereas Egypt and Lebanon ranked as laggards. The present study also reviewed each Arab State’s telecommunication sector which provided a theoretical interpretation for the differences found in their diffusion patterns.

The paper extends diffusion theory to encompass a region otherwise excluded from the literature’s generalizable findings. The present study’s sampling of countries from the Middle East and North African region and subsequent findings provide a stronger basis to draw empirical generalizations about international product diffusion process than previously suggested by the literature.

The novel coronavirus disease 2019 (COVID-19) pandemic has presented unique challenges in terms of understanding its unique characteristics of transmission and predicting its spread. The purpose of this study is to present a simple, parsimonious and accurate model for forecasting mortality caused by COVID-19.

The presented Bass Model is compared it with several alternative existing models for forecasting the spread of COVID-19. This study calibrates the model for deaths for the period, March 21 to April 30 for the USA as a whole and as the US States of New York, California and West Virginia. The daily data from the COVID-19 Tracking Project has been used, which is a volunteer organization launched from The Atlantic. Every day, data is collected on testing and patient outcomes from all the 50 states, 5 territories and the District of Columbia. This data set is widely used by policymakers and scholars. The fit of the model (F-value and its significance, R-squared value) and the statistical significance of the variables (t-values) for each one of the four estimates are examined. This study also examines the forecast of deaths for a three-day period, May 1 to 3 for each one of the four estimates – US, and States of New York, California and West Virginia. Based on these metrics, the viability of the Bass Model is assessed. The dependent variable is the number of deaths, and the two independent variables are cumulative number of deaths and its squared value.

The findings of this paper show that compared to other forecasting methods, the Bass Model performs remarkably well. In fact, it may even be argued that the Bass Model does better with its forecast. The calibration of models for deaths in the USA, and States of New York, California and West Virginia are all found to be significant. The F values are large and the significance of the F values is low, that is, the probability that the model is wrong is very miniscule. The fit as measured by R-squared is also robust. Further, each of the two independent variables is highly significant in each of the four model calibrations. These forecasts also approximate the actual numbers reasonably well.

This study illustrates the applicability of the Bass Model to estimate the diffusion of COVID-19 with some preliminary but important empirical analyses. This study argues that while the more sophisticated models may produce slightly better estimates, the Bass model produces robust and reasonably accurate estimates given the extreme parsimony of the model. Future research may investigate applications of the Bass Model for pandemic management using additional variables and other theoretical lenses.

The Bass Model offers effective forecasting of mortality resulting from COVID-19 to help understand how the curve can be flattened, how hospital capacity could be overwhelmed and how fatality rates might climb based on time and geography in the upcoming weeks and months.

This paper demonstrates the efficacy of the Bass Model as a parsimonious, accessible and theory-based approach that can predict the mortality rates of COVID-19 with minimal data requirements, simple calibration and accessible decision calculus. For all these reasons, this paper recommends further and continued examination of the Bass Model as an instrument for forecasting COVID-19 (and other epidemic/pandemic) mortality and health resource requirements. As this paper has demonstrated, there is much promise in this model.

The purpose of this paper is to analyze the relationship between new product diffusion and consumer internet search patterns using big data and to investigate whether such data can be used in forecasting new product diffusion.

This research proposes a new product diffusion model based on the Bass diffusion model by incorporating consumer internet search behavior. Actual data from search engine queries and new vehicle sales for each vehicle class and region are used to estimate the proposed model. Statistical analyses are used to interpret the estimated results, and the prediction performance of the proposed method is compared with other methods to validate the usefulness of data for internet search engine queries in forecasting new product diffusion.

The estimated coefficients of the proposed model provide a clear interpretation of the relationship between new product diffusion and internet search volume. In 83.62 percent of 218 cases, analyzing the internet search pattern data are significant to explain new product diffusion and that internet search volume helps to predict new product diffusion. Therefore, marketing that seeks to increase internet search volume could positively affect vehicle sales. In addition, the demand forecasting performance of the proposed diffusion model is superior to those of other models for both long-term and short-term predictions.

As search queries have only been available since 2004, comparisons with data from earlier years are not possible. The proposed model can be extended using other big data from additional sources.

This research directly demonstrates the relationship between new product diffusion and consumer internet search pattern and investigates whether internet search queries can be used to forecast new product diffusion by product type and region. Based on the estimated results, increasing internet search volume could positively affect vehicle sales across product types and regions. Because the proposed model had the best prediction power compared with the other considered models for all cases with large margins, it can be successfully utilized in forecasting demand for new products.