Journal of Marketing Research (JMR)
Выпуск N3 за 2001 год
Опубликовано на портале: 29-09-2003Peter J. Danaher Journal of Marketing Research (JMR). 2001. Vol. 38. No. 3. P. 298-313.
The authors examine the rescheduling of television programs to maximize the total ratings for one network across a week. The key idea is to design a choice experiment in which television programs are rescheduled and presented to respondents. Respondents read these program schedules (much like the regular TV Guide listings) and give their preferences, including not watching any of the listed programs. Because there are potentially billions of possible schedules, the authors give a procedure for designing a fractional factorial experiment that can accommodate both programs of varying length and constraints on eligible program times. The authors also develop a latent class multinomial logit model for modeling program preferences and present a validation of our experimental procedure and the model. They also present an empirical test of the procedure in which they use the model to predict ratings for all the possible program schedules, not just those constituting the choice sets. In this example, the optimum schedule increases the predicted total weekly ratings during prime time by 18% for a network. The projected increase in total weekly ratings is achieved without the network needing to purchase any new programs; all it needs to do is reschedule eight programs in its existing prime-time lineup.
Опубликовано на портале: 29-09-2003David H. Henard Journal of Marketing Research (JMR). 2001. Vol. 38. No. 3. P. 362-375.
Product innovation is increasingly valued as a key component of the sustainable success of a business's operations. As a result, there has been a noticeable increase in the number of studies directed at explicating the drivers of new product success. To help managers and researchers synthesize this growing body of evidence, the authors conduct a meta-analysis of the new product performance literature. Of the 24 predictors of new product performance investigated, product advantage, market potential, meeting customer needs, predevelopment task proficiencies, and dedicated resources, on average, have the most significant impact on new product performance. The authors also find that the predictor-performance relationships can vary by measurement factor (e.g., the use of multi-item scales, subjective versus objective measures of performance, senior versus project management reporting, time elapsed since product introduction) or contextual factor (e.g., services versus goods, Asian versus North American markets, competition in high-technology versus low-technology markets). They discuss the implications of these findings and offer directions for further research.