Takeoff

Abstract: A consistent pattern observed for really new household consumer durables is a takeoff or dramatic increase in sales early in their history. The takeoff tends to appear as an elbow-shaped discontinuity in the sales curve showing an average sales increase of over 400%. In contrast, most marketing textbooks as well as diffusion models generally depict the growth of new consumer durables as a smooth sales curve. Our discussions with managers indicate that they have little idea about the takeoff and its associated characteristics. Many managers did not even know that most successful new consumer durables had a distinct takeoff. Their sales forecasts tend to show linear growth. Yet, knowledge about the takeoff is crucial for managers to decide whether to maintain, increase, or withdraw support of new products. It is equally important for industry analysts who advise investors and manufacturers of complementary and substitute products. Although previous studies have urged researchers to examine the takeoff, no research has addressed this critical event. While diffusion models are commonly used to study new product sales growth, they do not explicitly consider a new product's takeoff in sales. Indeed, diffusion researchers frequently use data only from the point of takeoff. Therefore, nothing is known about the takeoff or models appropriate for this event. Our study provides the first analysis of the takeoff. In particular, we address three key questions: i How much time does a newly introduced product need to takeoff? ii Does the takeoff have any systematic patterns? iii Can we predict the takeoff? We begin our study by developing an operational measure to determine when the takeoff occurs. We found that when the base level of sales is small, a relatively large percentage increase could occur without signaling the takeoff. Conversely, when the base level of sales is large, the takeoff sometimes occurs with a relatively small percentage increase in sales. Therefore, we developed a “threshold for takeoff.” This is a plot of percentage sales growth relative to a base level of sales, common across all categories. We define the takeoff as the first year in which an individual category's growth rate relative to base sales crosses this threshold. The threshold measure correctly identifies the takeoff in over 90% of our categories. We model the takeoff with a hazard model because of its advantages for analyzing time-based events. We consider three primary independent variables: price, year of introduction, and market penetration, as well as several control variables. The hazard model fits the pattern of takeoffs very well, with price and market penetration being strong correlates of takeoff. Our results provide potential generalizations about the time to takeoff and the price reduction, nominal price, and penetration at takeoff. In particular, we found that: • On average for 16 post-World War II categories: ---the price at takeoff is 63% of the introductory price; ---the time to takeoff from introduction is six years; ---the penetration at takeoff is 1.7%. • The time to takeoff is decreasing for more recent categories. For example, the time to takeoff is 18 years for categories introduced before World War II, but only six years for those introduced after World War II. • Many of the products in our sample had a takeoff near three specific price points in nominal dollars: $1000, $500 and $100. In addition, we show how the hazard model can be used to predict the takeoff. The model predicts takeoff one year ahead with an expected average error of 1.2 years. It predicts takeoff at a product's introduction with an expected average error of 1.9 years. Even against the simple mean time to takeoff of six years for recent categories, the model's performance represents a tremendous improvement in prediction. It represents an immeasurable improvement in prediction for managers who currently have no idea about how long it takes for a new product to takeoff. The threshold rule for determining takeoff can be used to distinguish between a large increase in sales and a real takeoff. Some limitations of this study could provide fruitful areas for future research. Our independent variables suffer from endogeneity bias, so alternative variables or methods could address this limitation. Also, the takeoff may be related to additional variables such as relative advantage over substitutes and the presence of complementary products. Finally, examination of sales from takeoff to their leveling off could be done with an integrated model of takeoff and sales growth or with the hazard model we propose. Generalizations about this period of sales growth could also be of tremendous importance to managers of new products.

Abstract: Aircraft emissions impact human health though degradation of air quality. The majority of previous analyses of air quality impacts from aviation have considered only landing and takeoff emissions. We show that aircraft cruise emissions impact human health over a hemispheric scale and provide the first estimate of premature mortalities attributable to aircraft emissions globally. We estimate ∼8000 premature mortalities per year are attributable to aircraft cruise emissions. This represents ∼80% of the total impact of aviation (where the total includes the effects of landing and takeoff emissions), and ∼1% of air quality-related premature mortalities from all sources. However, we note that the impact of landing and takeoff emissions is likely to be under-resolved. Secondary H2SO4−HNO3−NH3 aerosols are found to dominate mortality impacts. Due to the altitude and region of the atmosphere at which aircraft emissions are deposited, the extent of transboundary air pollution is particularly strong. For example, w...

Abstract: The applicability of the dual-task event-related (brain) potential (ERP) paradigm to the assessment of an operator's mental workload and residual capacity in a complex situation of a flight mission was demonstrated using ERP measurements and subjective workload ratings of student pilots flying a fixed-based single-engine simulator. Data were collected during two separate 45-min flights differing in difficulty; flight demands were examined by dividing each flight into four segments: takeoff, straight and level flight, holding patterns, and landings. The P300 ERP component in particular was found to discriminate among the levels of task difficulty in a systematic manner, decreasing in amplitude with an increase in task demands. The P300 amplitude is shown to be negatively correlated with deviations from command headings across the four flight segments.