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when the API is used. This made the production and
the communication of a study to update and deepen
the potential biases of search engines API within
webometrics studies: see Viseur, 2012a.
The fact that media share of premium brands
seems to be greater than their market share could be
explained by the additional communication about
premium brands, which are able to generate passion
and elicit comments in the official press or
participatory media (forums, blogs, etc.). Albert et
al. (2012) indeed confirm that the brand passion, i.e.
“the strong positive feeling towards a brand”, is
linked to unique and prestigious brands, and in turn
leads to greater positive word-of-mouth.
Several points could be studied further.
Firstly, the study could be deepened for the
automotive sector. The method could be applied to
models of cars, in order to assess if it could also be
used to estimate the market share of products and
services (and not just brands).
Secondly, although this first experiment was
conducted on a mature market, our aim is to obtain a
method applicable to more turbulent markets. The
smartphone market is one of those markets. The
application of the method presents practical
difficulties related to the bias caused by the
accumulation of historical data in commercial search
engines. Several approaches could improve this
method. They include the use of news search
engines, the use of Twitter timeline content, the use
of the Google “daterange” operator or the use of a
custom search engine with a specific index (see e.g.
Viseur, 2012b).
Thirdly, the impact of negative opinions on the
market shares of brands could be further explored.
The impact of negative opinions on market share is
not easy to quantify. See the astonishing example of
the DecorMyEyes online store. Uncles et al. (2010)'s
works provide an initial insight into the effects of the
total volume of word-of-mouth (WOM), the volume
of positive word-of-mouth (PWOM) and the volume
of negative word-of-mouth (NWOM).
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