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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 Bass model was created to analyse the product life cycle (PLC) in order to help sales and marketing departments in their business decision making. The purpose of this paper is to analyse the diferences between the clients assisted and sales variables, to discover which of the two variables is the more useful for the estimation of the PLC phases through the Bass model, thus aiding the managers of company sales and marketing departments.

In this research, the authors analysed the 223,577 clients assisted by a nationwide network of car dealerships, who acquired 36,819 vehicles, during a 24-month period. In the analysis, the Bass model was applied to define the PLC phases; and nonlinear regression models were used to carry out the estimations.

The results show that more consistent estimates of the PLC phases are obtained from the clients assisted variable. This work has theoretical and practical implications that can help business management.

The most remarkable thing about this research is that we have shown that the functionality of the clients assisted variable is greater than the sales variable for the Bass model and, therefore, for PLC estimation.

The results of this research are very useful, since they allow marketing decision makers to obtain more consistent estimations of the PLC phases using the Bass model and the clients assisted variable. This is based on the fact that the use of this variable helps to detect if there is any deficiency in the design of the marketing strategy when the client does not make the purchase.

The data on clients assisted are as easily available to companies as sales data. However, the use of this variable improves PLC analysis and this allows an improvement in company forecasting. Thus, making the clients assisted variable a tool to strategically plan investments in innovation and marketing would reduce uncertainty in business management.

The purpose of this paper is to analyse the diferences between the clients assisted and sales variables, to discover which of the two variables is the more useful for the estimation of the PLC phases through the Bass model, thus aiding the managers of company sales and marketing departments.

The purpose of this paper is to use bibliometric analysis to forecast RFID technology and uses the adoption of barcode scanner to model the RFID scanner adoption in the US retail market.

Forecasting emerging technologies and identifying the rate of diffusion of products based on these technologies is difficult because of lack of data. This paper uses techniques such as bibliometric analysis and Bass model based on analogous products.

The authors were able to come up with a good Bass model for the adoption of RFID scanners in the retail sector. And according to the Bass model it appears that it will take at least seven or eight years for the retailers to adopt to this new technology at their point‐of‐sale.

This paper attempts to use bibliometric analysis and Bass model for forecasting technologies and provides a new research discussion as results from these two are compared.

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 solve the problem of low accuracy in new product demand forecasting caused by the absence of historical data and inadequate consideration of influencing factors.

A hybrid new product demand forecasting model combining clustering analysis and deep learning is proposed. Based on the product similarity measurement, the weight of product similarity attributes is realized by using the method of fuzzy clustering-rough set, which provides a basis for the acquisition and collation of historical sales data of similar products and the determination of product similarity. Then the prediction error of Bass model is adjusted based on similarity through a long short-term memory neural network model, where the influencing factors such as product differentiation, seasonality and sales time on demand forecasting are embedded. An empirical example is given to verify the validity and feasibility of the model.

The results emphasize the importance of considering short-term impacts when forecasting new product demand. The authors show that useful information can be mined from similar products in demand forecasting, where the seasonality, product selling cycles and sales dependencies have significant impacts on the new product demand. In addition, they find that even in the peak season of demand, if the selling period has nearly passed the growth cycle, the Bass model may overestimate the product demand, which may mislead the operational decisions if it is ignored.

This study is valuable for showing that with the incorporation of the evaluation method on product similarity, the forecasting model proposed in this paper achieves a higher accuracy in forecasting new product sales.

Asserts that innovation, which plays a key role in product and process improvement in many companies, is the very lifeblood of high technology firms. Considers that because technological change is a function of the economic growth model then technological substitution must be a sub‐function of this model. The ability to forecast technological substitution in the long‐term macro view enables strategic planners to develop trends for their specific technological application. Begins with a brief statement of the problem, followed by a discussion of the theoretical framework, review of related literature, methodology, findings, discussion of findings and their implications and, finally, recommendations to practitioners.

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.

This paper aims to propose a novel method to predict and alleviate feature fatigue. Many products now are loaded with an extensive number of features. Adding more features to one product generally makes the product more attractive on the one hand but, on the other hand, may result in increasing difficulty to use the product. This phenomenon is called “feature fatigue”, which will lead to dissatisfaction and negative word-of-mouth (WOM). Feature fatigue will damage the brand’s long-term profit, and ultimately decrease the manufacturer’s customer equity. Thus, a problem of balancing the benefit of increasing “attractiveness” with the cost of decreasing “usability” exists.

A novel method based on the Bass model is proposed to predict and alleviate feature fatigue. Product capability, usability and WOM effects are integrated into the Bass model to predict the impacts of adding features on customer equity in product development, thus helping designers alleviate feature fatigue. A case study of mobile phone development based on survey data is presented to illustrate and validate the proposed method.

The results of the case study demonstrate that adding more features indeed increases initial sales; however, adding too many features ultimately decreases customer equity due to usability problems. There is an optimal feature combination a product should include to balance product capability with usability. The proposed method makes a trade-off between initial sales and long-term profits to maximize customer equity.

The proposed method can help designers predict the impacts of adding features on customer equity in the early stages of product development. It can provide decision supports for designers to decide what features should be added to maximize customer equity, thus alleviating feature fatigue.