The Successful Launch and Diffusion of New Therapies - Innovation and Marketing in the Pharmaceutical Industry - page 213

7.4 Future Research on Launch and Diffusion Excellence

While the above overview shows that much work has been done in marketing sci-
ence towards assessing the potential of new treatments, extracting value from a new
treatment, and leveraging the value of a new treatment across countries, much work
still remains. Below, we review some of the themes we consider important.

7.4.1 Future Research on Assessing the Potential

of a New Treatment

Over the past 2 decades, marketing scholars have pointed out the need for a more
elaborate framework for the study of diffusion processes that takes into account the
usage of an introduced innovation (Anderson and Ortinau 1988 ; Hahn et al. 1994 ;
Lewis and Seibold 1993 ). Several models in the life sciences, marketing, and eco-
nomic literature have considered the process of post-adoption learning about a new
drug (e.g., Camacho et al. 2011 ; Coscelli and Shum 2004 ; Hahn et al. 1994 ;
Narayanan et al. 2005 ). These models, however, do not specifically account for
physicians' initial adoption decisions and the factors that influence them.
Furthermore, the models focus on the development of market shares rather than on
drug sales and do not fully integrate patient behavior into the modeling framework.
A promising avenue for future research is therefore to develop an individual-level
model that integrates both the role of time dynamics in physicians' adoption deci-
sion processes and the role of patient compliance in the sales patterns of new drugs.
This can be done by integrating information on refill prescriptions for previously
diagnosed patients, corresponding to patients' compliance with therapeutic regi-
mens, into an individual-physician-patient adoption model.
In addition, more work is needed that integrates the richness of primary data with
the behavioral regularity identified in secondary data (such as from physician
prescription panels). Integrating or fusing such data sources can yield great value,
particularly for pharmaceutical companies have demonstrated the usefulness of pri-
mary and secondary data fusion in examining policy shifts in detailing by pharmaceu-
tical firms. They found that when the market leader in a drug category dramatically
reduces detailing, all firms in the category make more money, and the category shrinks
only to a minor extent. Similar models can be developed for pharmaceutical forecast-
ing, integrating information from conjoint analysis and information on past physician
behavior from physician tracking panels.
Finally, there is a need for more work that examines the adoption of marketing
science models by pharmaceutical managers. While marketing scientists have
developed “heavy artillery” to assess the commercial (future) potential of new
drugs, little of that artillery is used in practice. Rather, managers typically use linear
or nonlinear extrapolation as well as traditional conjoint models. Examining the
reasons that underlie the limited usage of sophisticated models in practice can yield
important insights that can lead to better model development in the future.