Füsun F. Gönül

Promotion of Prescription Drugs and Its Impact on Physicians' Choice Behavior

The authors investigate whether and how pricing and promotional activities influence prescription choice behavior using a comprehensive panel of physicians and data on competitive price and promotional activities. The authors find that physicians are characterized by fairly limited price sensitivity, detailing and samples have a mostly informative effect on physicians, and physicians with a relatively large number of Medicare or health maintenance organization patients are less influenced by promotion than other physicians are.

On the Use of Expectations Data in Micro Surveys: The Case of Retirement

The object of this paper is to ascertain whether data on retirement expectations is consistent with data on actual labor supply in the sense that both are derived the same optimizing model.

Multiple Ways of Measuring Brand Loyalty

Recently, DuWors and Haines (1990) developed a new measure of brand loyalty based on event history analysis. They measure brand loyalty as the time until a consumer switches to another brand for the first time. This measure is an improvement since it can be applied for either static or timedependent loyalty. However, there are also problems. The measure does not consider competing-risks although their data contain multiple brands. More importantly, heterogeneity is not allowed for, which may lead to biased results and spurious state dependence (Heckman and Singer 1984). What appears as time-dependent loyalty may be an artifact due to aggregating consumers with heterogeneous purchase probabilities. In addition, they only consider the time until the first switch. This may provide a deceptive picture of loyalty if brand switching is frequent in a product category.

Estimation of Promotional Strategies for Newer vs. Older Drugs Based On Physician Prescribing Data

Purpose – The purpose of this paper is to use results from a comprehensive analysis of a physician-prescribing model to draw guidelines on how to promote a new drug in the presence of competing older drugs, in a chronic therapeutic state. Design/methodology/approach – The authors use an extensive database from SDI Health, LLC, and a second data set from IMS Health. They calibrate their model using logarithmic regression methodology. The dependent variable in the model is number of new prescriptions and the explanatory variables are physician and patient characteristics, and marketing variables. Findings – The authors’ estimates imply that heavy prescribers are likely to be specialists, be in solo practice, have more experience, receive more sales rep traffic, have more HMO affiliations, have a higher proportion of patients in HMOs, write more prescriptions across all therapeutic categories, see a higher number of patients, receive more free samples from the sales reps, have more rep intensity in their offices, and allow longer visits by sales reps. Originality/value – This model has novel implications for drug manufacturers on the effect of time-in-the-market. Accordingly, new drug makers are well-advised to wait until a drug gets established in the community for it to be prescribed more heavily by specialists and target physicians in solo practice and newer physicians to speed up the adoption process. Furthermore, for newer drugs traditional forms of detailing via a live sales rep are not as effective as for older more established drugs – the new-drug manufacturer can try other means such as e-detailing, social media, direct-to-consumer advertising, and word-of-mouth/mouse to initiate market share.

Joint Estimates of Purchase Timing and Brand Switch Tendency: Results from a Scanner Panel Data Set of Frequently Purchased Products

Our purpose in this paper is to measure the correlation between the purchase timing and brand switching behaviour of households. We want to study (i) whether households switch brands more often for the products they purchase more frequently, and (ii) to identify the demographic variables that influence purchase timing and the marketing variables that influence the brand switching. These research issues are important to brand managers who want to encourage purchase acceleration and loyalty to particular brands. Managers can use our model for identifying market segments that buy more often but switch less often, the heavy users of a brand, or segments with growth potential. Our data are from the scanner panel purchase data collected in two cities by the A.C. Nielsen Company over a period of approximately one year. We aggregate the purchases to weekly units and select two categories. We test our model and find that while for purchases of diapers there is no evidence of a significant correlation between purchase timing and brand switching, for ketchup purchases, there is a significant positive relationship.

Snipe bidding behaviour in eBay auctions

Our research investigates what factors make snipe bidding more likely, whether sniping pays, and how an auction can be designed to minimise sniping. We estimate sniping probability in eBay auctions using multivariate models to examine novel datasets compiled from auctions of the Norelco electric razor and the Sony PlayStation2. Our main results reveal that the winner of an auction is more likely to be a snipe bidder and that a lower ending price increases the likelihood that the winner of an auction is a snipe bidder; everything else held constant. We find that experienced bidders are more likely to engage in sniping behaviour. In addition, we find that an auction designer or a seller can discourage snipe bidding by setting a longer duration and/or a lower (or no) reserve price.

Determinants of elapsed time to switch between auctions

Purpose – Online auctions, which have become an important aspect of online sales, are generally regarded as stand‐alone events. However, in contrast to offline auctions, online auctions can be subject to the presence of simultaneous competing auctions. The purpose of this study is to model and estimate determinants of elapsed time to switch across concurrent auctions, with special attention to unobserved heterogeneity among bidders.Design/methodology/approach – Since auctions are dynamic and since the current winning bid progresses over time, the authors study time dependency over the course of an auction with hazard function models. To account for unobserved heterogeneity, the paper uses a latent class approach, which identifies bidder segments based on both observed and unobserved factors.Findings – The findings show significant heterogeneity across bidders, revealed by their varying degrees of propensity to switch across auctions. The three segments of bidders are The Inerts – about 30 percent, The Swi...