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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.

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.

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.

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.

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.

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.

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 Switchers – less than 10 percent, and The In‐Betweens. According to the findings, bidders can induce other bidders to switch to a concurrent auction by responding quickly to the current high bid. Moreover, the paper finds a surprisingly high degree of inertia and reluctance to switch towards the end of the auction when bidding is most critical.

To the authors' knowledge, this study is the first to model elapsed time to switch from one auction to a simultaneous auction for an identical product, and to investigate determinants of the time required to switch, with special attention to unobserved heterogeneity across bidders.

This study aims to explore the roles and relations of dynamic capabilities (DCs) and marketing capabilities (MCs) to generate firm performance through new empirical data from the automotive industry in an emerging market, Turkey, where volatile market conditions may compel firms to use both their DCs and MCs. The automotive industry dynamic character, which is shaped by fierce competition among car manufacturers, fluctuating customer demands and strong effect of environmental forces, provides an ideal context for examining the performance outcomes of MC and DC in non-static environments. This study aims to show whether better financial performance can be achieved through an integrated MC and DC framework; if the level of environmental dynamism (ED) influences the utilization of MCs; and the impact of MC and DC convergence on firm performance by using emerging market data, which is rare in the extant literature.

This paper empirically investigates the role of MCs on the relationship between DCs and firm performance and the effect of ED in marketing capability development through a study of 162 top level managers from the automotive industry in Turkey using multiple regression methods. A self-administered questionnaire was used to collect data. A maximum concern was given to obtain at least three questionnaires from each firm to minimize the risk of getting biased answers from only one manager.

The data were analyzed by the regression method, and the mediation and moderation tests were conducted to test the established hypotheses. The direct relationship between MCs and firm performance was examined through linear regression, and a significant relationship was found (ß = 0.408; t = 5.656; p < 0.001). Pricing (ß = 0.404; p < 0.001), marketing research (ß = 0.367; p < 0.001) and marketing strategy and implementation (ß = 0.336; p < 0.001) had the strongest association with firm performance. The mediating role of MC on the relationship between DC and firm performance was assessed, and the analysis result yielded a significant result (ß = 0.439; t = 6.174; p < 0.001). Finally, the moderating effect of ED on the direct relationship between MC and firm performance was assessed. Yet, the interaction term was insignificant (ß = 0.013; t = 0.103; p = 0.918) in predicting firm performance.

Although the data set covers a broad range of firms operating in the Turkish automotive industry, the generalization of findings should only be possible through obtaining fresh evidence from other emerging markets that possess the similar market characteristics of Turkey. The cross-sectional nature of the study may offer insights only for a certain period of time; thus, additional longitudinal studies are recommended to see the dynamic changes on the constructs and relationships between them. Future studies may also include qualitative methods, i.e. interviews with top managers to have a deeper understanding on how DC–MC interaction creates better performance.

This study empirically shows the importance of MCs for firm performance; thus, managers should allocate significant efforts and resources for improving MCs. The demand for the electric and even autonomous vehicles is likely to increase in the following years, and this new era in the automotive industry requires more R&D and innovation-based products, i.e. green vehicles with low carbon footprint, the use of robotics and long-life batteries for electric vehicles. The cost-related pricing may no longer be a competitive advantage for the firms in emerging markets such as Turkish automotive industry; thus, more investment for disruptive technologies should be considered.

The results show that MCs of firms mediated the relationship between DCs and economic performance. Yet, ED did not play a moderating role on the relationships between MCs and performance. It is concluded that DCs were associated with improved firm performance via MCs. Furthermore, the insignificant impact of ED on the development of MCs leading to better performance was explained by firms’ given over-performing efforts in the context of emerging markets.