Proceedings of the 3rd European Conference on Intellectual Capital

Klaus Bruno Schebesch
We argue in favour <strong>of</strong> using adapted recommender systems with trust generation and also for
learning <strong>of</strong> how to use complementarities in products and technologies within highly collaborative
eCommerce procedures (Salakhutdinov et al. (2007), Abernethy et. al. (2009)). Trust formation and
recommender systems (Xiao and Benbasat (2007), Schebesch et al. (2010)) are explicit mechanisms,
which contribute to opinion formation in contexts like marketing and technology campaigns, in that
<strong>the</strong>y use more details about individuals. Recommender systems use matrices containing client and
product feature entries. A product is recommended to a client, if
The client is not aware <strong>of</strong> <strong>the</strong> product or did never buy / use <strong>the</strong> product,
There are scores from o<strong>the</strong>r clients concerning <strong>the</strong> very <strong>the</strong> product,
Meaningful recommendations stem from a cluster which contains <strong>the</strong> client, and
Only persons / organizations trustworthy to <strong>the</strong> client do participate in <strong>the</strong> recommendation.
Figure 5: Real-life feedback loops in social learning connected to aspects <strong>of</strong> innovation, branding,
and behavioural imitation, <strong>the</strong> boxes named by encircled letters A,B,C and D are are
modelling issues in <strong>the</strong>ir own right, a high resolution postscript version can be obtained
from <strong>the</strong> author
The cost <strong>of</strong> designing and operating a recommender system can be substantial. Finding good client
clusterings and empirically valid trust matrices can be expensive. Product score matrices may be
extremely sparse, indicating that most clients do not use most products. If one intends to recommend
new products based on smart textiles and if <strong>the</strong>se products did not produce as yet any scores from
clients, you may include wearing habits and electronic devices as auxiliary entries in order to start
with some surrogate data for <strong>the</strong> needed clusterization <strong>of</strong> clients. Such prelaunch forecasting <strong>of</strong>
products which are new to <strong>the</strong> market is a recurring topic in Marketing (Urban et al. (1996), Hoeffler
(2003), Natter et al. (2003)). Starting with new products based on smart textiles thus requires <strong>the</strong> use
<strong>of</strong> client similarities from o<strong>the</strong>r domains or markets and will have to rely more strongly on trust
between clients. Discovering <strong>the</strong> amount <strong>of</strong> trust new technologies and product designs are capable
to produce in a client population can be at least in principle enabled by means <strong>of</strong> providing incentives
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