Biomedical Engineering Reference
In-Depth Information
Additionally, we obtain some new insights with respect to the determinants of
detailing elasticity. In particular, unlike AMS (
2010
), we fi nd a signifi cant interac-
tion effect between geographic region and product life cycle (PLC) stage on detail-
ing elasticity. Specifi cally, detailing elasticities are highest for products which were
in their
early
life cycle stage in Europe, and lowest for products in their late life
cycle stage in the USA.
2
After adjusting for the signifi cant methodology-induced
biases found in our meta-analysis, we determine that the mean estimate of current-
period detailing elasticity is about 0.178. This value can be used as a benchmark to
guide researchers and practitioners assessing detailing effectiveness spending levels
in various circumstances (geographic and life cycle stage).
The rest of this chapter is organized as follows: First, we describe the scope of
our database and meta-analysis; and the selected determinants or independent vari-
ables that could affect detailing elasticities that we investigate. Then we present our
meta-analytic model and methodology; describe our results and ensuing empirical
generalizations; and discuss their managerial implications.
The determinants or drivers of detailing elasticity that we investigate include
variables falling in three classes: (a)
market type characteristics
, (b)
dataset charac-
teristics
, and (c)
researcher
'
s choices with respect to model specifi cations
.
Additionally, we investigate the interactive effects of these covariates and two
market-type factors, geographic region and PLC stage. Subsequently, we obtain the
methodology bias-corrected distribution of detailing elasticities. We then discuss
the implications of our results for pharma sales force managers and researchers. We
conclude with some suggestions for further research.
18.2
Database Scope
We restrict detailing elasticity measurements from past studies included in this meta-
analysis to those that are (1) based on
ratio
-
scaled objective
(e.g., Rich et al.
1999
)
measures of selling
output
(e.g., sales volume in units or dollars, number of prescrip-
tions), and input
effort
, e.g., “size” measures such as the total number of salespeople
or dollar expenditures on detailing, “frequency” measures such as the number of sales
calls or details, and “time” measures such as number of selling hours; (2) derived
from objective, econometric data and not subjective decision calculus data (e.g.,
Lodish et al.
1988
); (3) fi rm-level rather than industry-level response function param-
eter estimates; (4) current-period measures, either directly provided or derivable
using author-reported lagged effects; and (5) unambiguously reported or derivable
from the estimated coeffi cients and/or other relevant data reported in the study.
2
According to Grabowski (
2002
), the representative new drug introduction in the United States
during the mid-1990s, had an average effective (post-launch) patent life (EPL) of approximately
12 years, similar to reported average EPL of drugs launched in Europe (e.g., Andersson and
Hertzman
1993
). Based on these average EPLs, we consider “early” lifecycle stage as 5 years or
less since launch of a product on the market.
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