RANGE AND NUMBER-OF-LEVELS EFFECTS IN DERIVED AND STATED MEASURES OF ATTRIBUTE IMPORTANCE

We study how the range of variation and the number of attribute levels affect five measures of attribute importance: full profile conjoint estimates, ranges in attribute level attractiveness ratings, regression coefficients, graded paired comparisons, and self-reported ratings. We find that all importance measures are affected by the range manipulation. The number of attribute levels affects only two measures. The results allow us to benchmark the magnitude of the number-of-levels effect against the range effect: conjoint importance estimates were approximately equally affected by a threefold increase in the range of attribute variation and by the insertion of two intermediate attribute levels. Our findings show that the number-of-levels effect is most likely due to respondents’ tendencies to distribute their mental stimulus representations and their responses uniformly over the corresponding continua.attribute importance, context effects, conjoint analysis, Research Methods/ Statistical Methods,

Semiparametric analysis to estimate the deal effect curve

The marketing literature suggests several phenomena that may contribute to the shape of the relationship between sales and price discounts. These phenomena can produce severe nonlinearities and interactions in the curves, and we argue that those are best captured with a flexible approach. Since a fully nonparametric regression model suffers from the curse of dimensionality, we propose a semiparametric regression model. Store-level sales over time is modeled as a nonparametric function of own-and cross-item price discounts, and a parametric function of other predictors (all indicator variables). We compare the predictive validity of the semiparametric model with that of two parametric benchmark models and obtain better performance on average. The results for three product categories indicate a.o. threshold- and saturation effects for both own- and cross-item temporary price cuts. We also show how the own-item curve depends on other items’ price discounts (flexible interaction effects). In a separate analysis, we show how the shape of the deal effect curve depends on own-item promotion signals. Our results indicate that prevailing methods for the estimation of deal effects on sales are inadequate.

Are persons with rheumatoid arthritis deconditioned? A review of physical activity and aerobic capacity

BACKGROUND: Although the general assumption is that patients with rheumatoid arthritis (RA) have decreased levels of physical activity, no review has addressed whether this assumption is correct. METHODS: Our objective was to systematically review the literature for physical activity levels and aerobic capacity (VO(2max)). in patients with (RA), compared to healthy controls and a reference population. Studies investigating physical activity, energy expenditure or aerobic capacity in patients with RA were included. Twelve studies met our inclusion criteria. RESULTS: In one study that used doubly labeled water, the gold standard measure, physical activity energy expenditure of patients with RA was significantly decreased. Five studies examined aerobic capacity. Contradictory evidence was found that patients with RA have lower VO(2max) than controls, but when compared to normative values, patients scored below the 10(th) percentile. In general, it appears that patients with RA spend more time in light and moderate activities and less in vigorous activities than controls. CONCLUSION: Patients with RA appear to have significantly decreased energy expenditure, very low aerobic capacity compared to normative values and spend less time in vigorous activities than controls

Is 3/4 of the Sales Promotion Bump Due to Brand Switching? No it is 1/3

Several researchers have decomposed sales promotion elasticities based on household scanner panel data.A key result is that the majority of the sales promotion elasticity, about 74 percent on average, is attributed to secondary demand effects (brand switching) and the remainder to primary demand effects (timing acceleration and quantity increases).We demonstrate that this result does not imply that if a brand gains 100 units in sales during a promotion the other brands in the category lose 74 units (74 percent).We offer a complementary decomposition measure based on unit sales.This measure shows the ratio of the current cross-brand unit sales loss to the current own-brand unit sales gain during promotion, and we report empirical results for this measure.We also derive analytical expressions that transform the elasticity decomposition into a decomposition of unit sales effects.These expressions show the nature of the difference between the two decompositions.To gain insight into the magnitude of the difference, we apply these expressions to previously reported elasticity decomposition results.We find that on average about one third of the unit sales increase is attributable to losses incurred by other brands in the same category (i.e., they lose 33 units).Thus, secondary demand effects account for a far smaller percent of the unit sales promotion effect than has been inferred from elasticity decomposition results.We find that the difference is due to the manner in which the two decomposition measures deal with the category expansion that occurs during a promotion.One interpretation is that the elasticity decomposition yields a gross measure of brand switching, in the sense that category sales are held constant.The unit sales decomposition yields a net measure of brand switching: it accommodates the category expansion effect that applies to both promoted and nonpromoted brands in the models

Is 3/4 of the Sales Promotion Bump Due to Brand Switching? No it is 1/3

Several researchers have decomposed sales promotion elasticities based on household scanner panel data.A key result is that the majority of the sales promotion elasticity, about 74 percent on average, is attributed to secondary demand effects (brand switching) and the remainder to primary demand effects (timing acceleration and quantity increases).We demonstrate that this result does not imply that if a brand gains 100 units in sales during a promotion the other brands in the category lose 74 units (74 percent).We offer a complementary decomposition measure based on unit sales.This measure shows the ratio of the current cross-brand unit sales loss to the current own-brand unit sales gain during promotion, and we report empirical results for this measure.We also derive analytical expressions that transform the elasticity decomposition into a decomposition of unit sales effects.These expressions show the nature of the difference between the two decompositions.To gain insight into the magnitude of the difference, we apply these expressions to previously reported elasticity decomposition results.We find that on average about one third of the unit sales increase is attributable to losses incurred by other brands in the same category (i.e., they lose 33 units).Thus, secondary demand effects account for a far smaller percent of the unit sales promotion effect than has been inferred from elasticity decomposition results.We find that the difference is due to the manner in which the two decomposition measures deal with the category expansion that occurs during a promotion.One interpretation is that the elasticity decomposition yields a gross measure of brand switching, in the sense that category sales are held constant.The unit sales decomposition yields a net measure of brand switching: it accommodates the category expansion effect that applies to both promoted and nonpromoted brands in the models.

The estimation of pre- and postpromotion dips with store-level scanner data

One of the mysteries of store-level scanner data modeling is the lack of a dip in sales in the week(s) following a promotion. Researchers expect to find a postpromotion dip because analyses of household scanner panel data indicate that consumers tend to accelerate their purchases in response to a promotion that is, they buy earlier and/or purchase larger quantities than they would in the absence of a promotion. Thus, one should also find a pronounced dip in store-level sales in the week(s) following a promotion. However, researchers find such dips usually neither at the category nor at the brand level. Several arguments have been proposed for the lack of a postpromotion dip in store-level sales data. These arguments explain why dips may be hidden. Given that dips are difficult to detect by traditional models (and by a visual inspection of the data), we propose models that can account for a multitude of factors which together cause complex pre- and postpromotion dips. We use three alternative distributed lead- and lag structures: an Almon model, an Unrestricted dynamic effects model, and an Exponential decay model. In each model, we include four types of price discounts: without any support, with display-only support, with feature-only support, and with feature and display support. The models are calibrated on store-level scanner data for two product categories: tuna and toilet tissue. We estimate the dip to be between 4 and 25 percent of the current sales effect, which is consistent with household-level studies

How Promotions Work: SCAN*PRO-Based Evolutionary Model Building

We provide a rationale for evolutionary model building. The basic idea is that to enhance user acceptance it is important that one begins with a relatively simple model. Simplicity is desired so that managers understand models. As a manager uses the model and builds up experience with this decision aid, she will realize its shortcomings. The model will then be expanded and will lead to the increase of complexity. Evolutionary model building also stimulates the generalization of marketing knowledge. We illustrate this by discussing different extensions of the SCAN*PRO model. The purpose of published model extensions is to increase the knowledge about "how promotions work" and to provide support for more complex decisions. We summarize the generated knowledge about how promotions work, based on this process.We provide a rationale for evolutionary model building. The basic idea is that to enhance user acceptance it is important that one begins with a relatively simple model. Simplicity is desired so that managers understand models. As a manager uses the model and builds up experience with this decision aid, she will realize its shortcomings. The model will then be expanded and will lead to the increase of complexity. Evolutionary model building also stimulates the generalization of marketing knowledge. We illustrate this by discussing different extensions of the SCAN*PRO model. The purpose of published model extensions is to increase the knowledge about "how promotions work" and to provide support for more complex decisions. We summarize the generated knowledge about how promotions work, based on this process.Articles published in or submitted to a Journal without I