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.

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

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

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

How to democratize Internet of Things devices. A participatory design research

The global introduction of affordable Internet of Things (IoT) devices offers an opportunity to empower a large variety of users with different needs. However, many off-the-shelf digital products are still not widely adopted by people who are hesitant technology users or by older adults, notwithstanding that the design and user-interaction of these devices is recognized to be user-friendly. In view of the potential of IoT-based devices, how can we reduce the obstacles of a cohort with low digital literacy and technology anxiety and enable them to be equal participants in the digitalized world? This article shows the method and results achieved in a community-stakeholder workshop, developed through the participatory design methodology, aiming at brainstorming problems and scenarios through a focus group and a structured survey. The research activity focused on understanding factors to increase the usability of off-the-shelf IoT devices for hesitant users and identify strategies for improving digital literacy and reducing technology anxiety. A notable result was a series of feedback items pointing to the importance of creating learning resources to support individuals with different abilities, age, gender expression, to better adopt off-the-shelf IoT-based solutions.Comment: 8 pages, 5 figure

Why don’t patients take their analgesics? A meta-ethnography assessing the perceptions of medication adherence in patients with osteoarthritis

Introduction/objectives: Whilst analgesics and medications have demonstrated efficacy for people with osteoarthritis, their effectiveness is dependent on adherence. This has previously been reported as particularly low in this population. The purpose of this meta-ethnography was to explore possible perceptions for this. Method: A systematic review of published and unpublished literature was undertaken. All qualitative studies assessing the attitudes or perceptions of people with osteoarthritis towards medication adherence were eligible. Study quality was assessed using the Critical Appraisal Skills Programme Qualitative tool. Analysis was undertaken using a meta-ethnography approach, distilling to a third order construct and developing a line of argument. Results: From 881 citations, five studies met the eligibility criteria. The meta-ethnography generated a model where medication adherence for people with osteoarthritis is perceived as a balance between the willingness or preference to take medications with the alterative being toleration of symptoms. Motivators to influence this ‘balance’ may fluctuate and change over time but include: severity of symptoms, education and understanding of osteoarthritis and current medications, or general health which may raise issues for poly-pharmacy as other medications are added or substituted into the patient’s formulary. Conclusions: Medicine adherence in people with osteoarthritis is complex, involving motivators which will fluctuate in impact on individuals at different points along the disease progression. Awareness of each motivator may better inform clinicians as to what education, support or change in prescription practice should be adopted to ensure that medicine adherence is individualised to better promote long-term behaviour change