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It has often been proposed, or assumed, that improvisation is a useful metaphor to provide insight into managing and organizing. However, improvisation is more than a metaphor. It is an orientation and a technique to enhance the strategic renewal of an organization. The bridge between theory and practice is made through exercises used to develop the capacity to improvise, borrowed from theatre improvisation. This paper describes a typical improvisation workshop in developing six key areas that link improvisation exercises to the practice of management: interpreting the environment; crafting strategy; cultivating leadership; fostering teamwork; developing individual skills; and assessing organizational culture.

In spite of the high financial stakes involved in marketing new motion pictures, marketing science models have not been applied to the market evaluation of motion pictures. The motion picture industry poses some unique challenges. For example, the consumer adoption process for movies is very sensitive to word-of-mouth interactions, which are difficult to measure and predict the movie has been released. In this article, we undertake the challenge to develop and implement MOVIEMOD—a prerelease market evaluation model for the motion picture industry. MOVIEMOD is designed to generate box-office forecasts and to support marketing decisions for a new movie after the movie has been produced (or when it is available in a rough cut) but before it has been released. Unlike other forecasting models for motion pictures, the calibration of MOVIEMOD does not require any actual sales data. Also, the data collection time for a product with a limited lifetime such as a movie should not take too long. For MOVIEMOD it takes only three hours in a “consumer clinic” to collect the data needed for the prediction of box-office sales and the evaluation of alternative marketing plans. The model is based on a behavioral representation of the consumer adoption process for movies as a macroflow process. The heart of MOVIEMOD is an interactive Markov chain model describing the macro-flow process. According to this model, at any point in time with respect to the movie under study, a consumer can be found in one of the following behavioral states: undecided, considerer, rejecter, positive spreader, negative spreader, and inactive. The progression of consumers through the behavioral states depends on a set of factors that are related to the marketing mix, as well as on a set of more general factors that characterize the movie-going behavior in the population of interest. This interactive Markov chain model allows us to account for word-of-mouth interactions among potential adopters and several types of word-of-mouth spreaders in the population. Marketing variables that influence the transitions among the states are movie theme acceptability, promotion strategy, distribution strategy, and the movie experience. The model is calibrated in a consumer clinic experiment. Respondents fill out a questionnaire with general items related to their movie-going and movie communication behavior, they are exposed to different sets of information stimuli, they are actually shown the movie, and finally, they fill outpostmovie evaluations, including word-of-mouth intentions.These measures are used to estimate the word-of-mouth parameters and other behavioral factors, as well as the movie-specific parameters of the model. MOVIEMOD produces forecasts of the awareness, adoption intention, and cumulative penetration for a new movie within the population of interest for a given base marketing plan. It also provides diagnostic information on the likely impact of alternative marketing plans on the commercial performance of a new movie. We describe two applications of MO...

In a viral marketing campaign, an organization develops a marketing message and encourages customers to forward this message to their contacts. Despite its increasing popularity, there are no models yet that help marketers to predict how many customers a viral marketing campaign will reach and how marketers can influence this process through marketing activities. This paper develops such a model using the theory of branching processes. The proposed viral branching model allows customers to participate in a viral marketing campaign by (1) opening a seeding e-mail from the organization, (2) opening a viral e-mail from a friend, and (3) responding to other marketing activities such as banners and offline advertising. The model parameters are estimated using individual-level data that become available in large quantities in the early stages of viral marketing campaigns. The viral branching model is applied to an actual viral marketing campaign in which over 200,000 customers participated during a six-week period. The results show that the model quickly predicts the actual reach of the campaign. In addition, the model proves to be a valuable tool to evaluate alternative what-if scenarios.branching processes, forecasting, Markov processes, online marketing, viral marketing, word of mouth

The integration of marketing and R&D is a major concern for companies that want to improve their new product performance (NPP). For this integration, companies are using mechanisms such as physical proximity, cross‐functional teams, and job rotation. This study examines the effectiveness of these mechanisms by developing a model that distinguishes between indirect effects of mechanisms on NPP (i.e., through a higher level of integration) and direct effects. The model is tested with data collected from 148 pharmaceutical companies. We were able to measure and compare the effectiveness of seven different integration mechanisms, which produced insights that are interesting and relevant for theory as well as practice. We found that housing marketing and R&D closer to each other and using an influential cross‐functional phase review board are the most effective mechanisms to foster integration. Equal remuneration and career opportunities for marketing and R&D and cross‐functional teams are somewhat less effective, whereas personnel movement and informal social group events contribute little. ICT appears to be a very effective tool for enhancing NPP. ICT not only fosters integration between marketing and R&D, but it also has an independent direct positive effect on NPP. Through ICT the day‐to‐day communication between the different parties in the companies becomes much easier, and we think that this fosters the knowledge creation process within marketing and R&D. For cross‐functional phase review boards we found a negative direct effect on NPP. Notwithstanding its strong positive effect on integration, a price is paid in terms of NPP. This may be related to the amount of formalization and complexity accompanying this mechanism.

This paper provides an introduction to this Special Issue by a) providing a framework for evaluating the potential and actual success of marketing management support systems (MMSS), and b) briefly discussing how each paper in this Special Issue addresses the general topic of managerial decision making. The paper concludes by outlining some key questions that still need to be addressed.Measures of Success, Decision Aids, Managerial Decision Making

Marketing decision makers are confronted with an increasing amount of information. This leads to a complex decision environment that may cause decision makers to lapse into using mental-effort-reducing heuristics such as anchoring and adjustment. In an experimental study, we find that the use of a marketing decision support system (MDSS) increases the effectiveness of marketing decision makers. An MDSS is effective because it assists its users in identifying the important decision variables and, subsequently, making better decisions based on those variables. Decision makers using an MDSS are also less susceptible to applying the anchoring and adjustment heuristic and, therefore, show more variation in their decisions in a dynamic environment. Low-analytical decision makers and decision makers operating under low time pressure especially benefit from using an MDSS.Marketing, Decision Support Systems, Managerial Decision Making

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