Sample attrition in the RLMS, 2001–10

The data of the Russian Longitudinal Monitoring Survey (RLMS) – Higher School of Economics represents one of the few nationally representative sources of household and individual data for Russia. These data have been collected since 1992 and in recent years, thanks to more secure financial and logistical support, have become a resource increasingly drawn upon by scholars and students for national and cross-national studies. In this paper, we examine the extent of non-random attrition in the RLMS and discuss the circumstances under which this might give rise to biases in econometric analysis. We illustrate this with an example drawn from the health sphere.     

Incorporating Prior Information into A GMM Objective For Mixed Logit Demand Systems

Random parameters demand system estimates can generate upward sloping demands and imply margins outside of the theoretical bounds for profit maximization. If such violations are numerous enough, they can confound merger simulation exercises. Using Lerner indices for multiproduct firms playing static Bertrand games, we find that up to 35 per cent of implied margins for beer are outside the bounds. We characterize downward sloping demand and the theoretical bounds for profit maximization as prior information and extend the GMM objective function, incorporating inequality moments for product‐level own‐elasticities and brand level or product level Lerner indices. Very few violations remain when an inequality constrained estimator is used.     

Contextual Innovation Management Using a Stage‐Gate Platform: The Case of Philips Shaving and Beauty

To improve its innovation process, Philips Shaving and Beauty (S&B) designed a blueprint for its innovation process. Although it has proved to be quite effective, it has experienced a lack of efficiency, in terms of frequent cost and time overruns, in the fuzzy front end of this process. We suggest a contextual innovation management approach to set up a stage‐gate‐based innovation process platform and thus improve the efficiency in the fuzzy front end, which means that, for different contexts, stage‐gate process variants will be designed from which unnecessary activities are removed and important activities are emphasized. The design is based on the identification of relevant contextual factors to develop variations of the common innovation process within Philips S&B. We distinguished different variants of the innovation processes within Philips S&B that can increase the efficiency in the fuzzy front end. Based on interviews within and outside Philips S&B, we identified problems and potential solutions with regard to efficiency in eight recently finished innovation processes. The results indicate that the most important contextual factors are the distinction between incremental and radical innovations, and between market and technology‐based innovations. We used these factors to design three variants on the basic platform of the stage‐gate process.     

Up in smoke: demarketing and consumer smoking cessation

The concept of demarketing refers to the use of marketing techniques to reduce or eliminate demand for a product or service. A review of the demarketing literature relating to health and specifically antismoking initiatives indicated that, while research on this topic exists, much of it is not grounded in an acceptable attitudinal or behavioural theory. After determining the importance placed by a sample of 18–24‐year olds on nine demarketing initiatives, two dimensions were identified that best explained this construct. Items within these dimensions were summed and averaged to form single variables, which were then used to form the attitudinal component of the Model of Goal Directed Behaviour. The findings showed that two of these variables – one that captured product packaging aspects and another that consisted of place and price items – significantly influenced the desire to quit and indirectly influenced the intention to quit. Anticipated positive emotions, frequency of quitting attempts and perceived control over quitting also positively influenced the desire and/or intention to quit. The article concludes with a discussion that interprets these findings from a theoretical and practical perspective and suggests directions for future research.     

Investigating Adoption of Free Beta Applications in a Platform‐Based Business Ecosystem

Planning new product development (NPD) activities is becoming increasingly difficult, as contemporary businesses compete at the level of business ecosystems in addition to the firm‐level product‐market competition. These business ecosystems are built around platforms interlinking suppliers, complementors, distributors, developers, etc. together. The competitiveness of these ecosystems relies on members utilizing the shared platform for their own performance improvement, especially in terms of developing new valuable offerings for end users. Therefore, managing the development of the platform‐based applications and gaining timely end‐user input for NPD are of vital importance both to the ecosystem as a whole and to the developers. Subsequently, to succeed in NPD planning developers utilizing beta testing need a thorough understanding of the adoption dynamics of beta products. Developers need to plan for example resource allocation; development costs; and timing of commercial, end‐product launches. Therefore, the anticipation of the adoption dynamics of beta products emerges as an important antecedent in planning NPD activities when beta testing is used for gaining end‐user input to the NPD process. Consequently, we investigate how free beta software products that are built upon software platforms diffuse among their end users in a cocreation community. We specifically study whether the adoption of these beta products follows Bass or Gompertz model dynamics used in the previous literature when modeling the adoption of stand‐alone products. Further, we also investigate the forecasting abilities of these two models. Our results show that the adoption dynamics of free beta products in a cocreation community follow Gompertz's model rather than the Bass model. Additionally, we find that the Gompertz model performs better than the Bass model in forecasting both short and long out‐of‐sample time periods. We further discuss the managerial and research implications of our study.