Individuals charts and additional tests for changes in spread

Some authors recommend the use of an additional test for detecting increases in the spread, when using a control chart for individual observations. We examine this recommendation both in a practical situation and theoretically. Both studies show that the additional test gives somewhat more power for detecting a 25% increase of the process variation. For nearly all other deviations from the in-control state the test is more likely to cause confusion. From a practical viewpoint we therefore advise against its use.

Combining time series and cross sectional data for the analysis of dynamic marketing systems

Vector AutoRegressive (VAR) models have become popular in analyzing the behavior of competitive marketing systems. However, an important drawback of VAR models is that the number of parameters to be estimated can become very large. This may cause estimation problems, due to a lack of degrees of freedom. In this paper, we consider a solution to these problems. Instead of using a single time series, we develop pooled models that combine time series data for multiple units (e.g. stores). These approaches increase the number of available observations to a great extent and thereby the efciency of the parameter estimates. We present a small simulation study that demonstrates this gain in efficiency. An important issue in estimating pooled dynamic models is the heterogeneity among cross sections, since the mean parameter estimates that are obtained by pooling heterogenous cross sections may be biased. In order to avoid these biases, the model should accommodate a sufficient degree of heterogeneity. At the same time, a model that needlessly allows for heterogeneity requires the estimation of extra parameters and hence, reduces efciency of the parameter estimates. So, a thorough investigation of heterogeneity should precede the choice of the nal model. We discuss pooling approaches that accommodate for parameter heterogeneity in different ways and we introduce several tests for investigating cross-sectional heterogeneity that may facilitate this choice. We provide an empirical application using data of the Chicago market of the three largest national brands in the U.S. in the 6.5 oz. tuna sh product category. We determine the appropriate level of pooling and calibrate the pooled VAR model using these data.

Why do firms invest in consumer advertising with limited sales response? A shareholder perspective

Marketing managers increasingly recognize the need to measure and communicate the impact of their actions on shareholder returns. This study focuses on the shareholder value effects of pharmaceutical direct-to-consumer advertising (DTCA) and direct-to-physician (DTP) marketing efforts. Although DTCA has moderate effects on brand sales and market share, companies invest vast amounts of money in it. Relying on Kalman filtering, the authors develop a methodology to assess the effects from DTCA and DTP on three components of shareholder value: stock return, systematic risk, and idiosyncratic risk. Investors value DTCA positively because it leads to higher stock returns and lower systematic risk. Furthermore, DTCA increases idiosyncratic risk, which does not affect investors who maintain well-diversified portfolios. In contrast, DTP marketing has modest positive effects on stock returns and idiosyncratic risk. The outcomes indicate that evaluations of marketing expenditures should include a consideration of the effects of marketing on multiple stakeholders, not just the sales effects on consumers

The effect of financial and educational incentives on rational prescribing:A state-space approach

In 2005, a Dutch health insurer introduced a financial incentive directed to general practitioners to promote rational prescribing of statins and proton pump inhibitors (PPIs). Concomitantly, a regional institution that develops pharmacotherapeutic guidelines implemented two educational interventions also aiming at promoting rational statin and PPI prescribing. Utilizing a prescription database, we estimated the effect of the interventions on drug utilization and cost of statins and PPIs over time. We measured the effect of the interventions within an implementation and a control region. The implementation region included prescriptions from the province of Groningen where the educational intervention was implemented and where the health insurer is most active. The control region comprised all other provinces covered by the database. We modelled the effect of the intervention using a state-space approach. Significant differences in prescribing and cost patterns between regions were observed for statins and PPIs. These differences however were mostly related to the concurrent interventions of Proeftuin Farmacie Groningen. We found no evidence indicating a significant effect of the rational prescribing intervention on the prescription patterns of statins and PPIs. Our estimates on the economic impact of the Proeftuin Farmacie Groningen interventions indicate that educational activities as such can achieve significant cost savings. Copyright © 2014 John Wiley & Sons, Ltd

Consumers’ privacy calculus:The PRICAL index development and validation

Although collecting personal information about consumers is crucial for firms and marketers, understanding of when and why consumers accept or reject information collection remains limited. The authors conceptualize a privacy calculus that represents a consumer's trade–off of the valence and uncertainty of the consequences of the collection, storage, and use of personal information. For example, usage-based car insurance requires drivers to share data on their driving behavior in exchange for a discount (certain benefit) but at the risk of third parties intercepting location data for malicious use (uncertain disadvantage). Building on this conceptualization, the authors develop the privacy calculus (PRICAL) index. They empirically confirm the validity of the items (Study 1) and the index as a whole (Study 2). The PRICAL index is generally applicable and improves the explanation of behavioral intentions (Study 2) and actual behavior (Study 3), compared with currently used constructs (e.g., privacy concern, trust). Overall, the PRICAL index allows managers to understand consumers’ acceptance of information collection regarding financial, performance, psychological, security, social, and time-related consequences, which the authors demonstrate using the top five most valuable digital brands (Study 4)