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Pricing is always a fundamental marketing element. In the digital marketing/e-commerce context, there are two universal phenomena: desire to micro-segment and customize, and the adverse reaction upon unfair perception of price. A third related question is how should firms consider price increases and decreases? Specifically, this paper aims to address the following three research and practice questions: What are the theoretical underpinnings of perception of fairness/unfairness in pricing, and what are the findings? What are the theoretical underpinnings of response to price increases and decreases? What should be online pricing strategy, consistent with the findings on (un)fairness perception of pricing and response to price increases and decreases?

The present approach is integrative review and critical analyses, and synthesis. The review dates back to 1960s, and is inter-disciplinary, including apposite findings in behavioral science, economics, marketing and operations management/research. The authors search for insights with significant empirical support to address these questions.

Perception of unfair price impacts consumer choice, probability of purchase, intent to buy and attitude to product/service/firm adversely. Consumers react differently to perceived unfair and fair prices. Consumers react more strongly and negatively to perceived unfair prices (compared to prices perceived to be fair) in their intent to buy and other related metrics. Consumers react differently to price increases and price decreases relative to the reference price. Consumers react more strongly to price increases than to price decreases. There is substantial heterogeneity in the magnitude of loss-aversion effect, depending on the product/service category and estimation methods.

The authors review and discuss potential viable pricing strategies. Based on the generalizable findings, this study provides actionable insights to managers for pricing in digital marketing context. Also, the authors provide useful directions for future research.

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.

This paper aims to examine three important questions: What would be the effects of pricing at the lower end of a wide vs narrow latitude of price acceptance (LPA) on consumer choice of the bundle? How would the nature of a bundle frame (i.e. discount on bundle vs discount on components) and discount frame (i.e. discount as absolute off vs discount as percentage off) influence the preference given to a price level that is at the wide or narrow end of the LPA? Would the effect be significantly different if the bundle components were complementary vs if they were non-complementary?

The authors carried out two studies using between-subject experimental design. In Study 1, the authors used 2 (LPA: wide/narrow) × 2 (complementarity: yes/no) × 2 (bundle frame: together/separate) design, and in Study 2, the authors replaced bundle frame with discount frame (i.e. absolute off/percentage off).

The authors find that the LPA effect is likely to outweigh the complementarity effect; however, a combined effect of complementarity and bundle frame is stronger than the LPA effect. Also, for a wide (narrow) LPA product bundle, absolute off (percentage off) discount frame is more attractive.

Managers should use bundling strategy with complementary products having wider LPA. In case of wide LPA and complementary products, both together and separate frame could be the best bundling strategy while in case of narrow LPA and complementary products, together frame could be the best bundling strategy.

The main contribution relates to the role LPA plays in consumer evaluation of a bundle offer and its interaction with complementarity and discount frame. The authors apply the range hypothesis principles (i.e. price-attractiveness judgments are based on a comparison of market prices to the endpoints of a range of evoked prices) in the bundling context and extend the earlier work in the area of complementarity and discount frame.

This paper aims to study the effects of order of market entry on market share in prescription (Rx) and over‐the‐counter (OTC) pharmaceutical drugs market.

Data on sales, price, direct‐to‐physicians (DTP) advertising, and direct‐to‐consumers (DTC) advertising for three Rx drugs categories and two OTC drugs categories were obtained for the period, January 1998 to December 1999. A log‐log statistical model was estimated using OLS methodology.

There is a significant order of entry effect on market share in both Rx and OTC drugs categories. This effect is higher in magnitude in the OTC category than in the Rx category. The effects of price, and DTP and DTC advertising are also significant. The differential effects of DTP and DTC advertising in the Rx and OTC categories are intuitive.

This study is unique in studying the differential effects of order‐of‐entry, and DTP and DTC advertising on market share in Rx and OTC drugs product categories.