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een trained it is ready for use in fields such as
forecasting, classification and optimisation.
NNWs today predict financial time series, rate the
creditworthiness of customers, classify consumers,
screen satellite data, recognise speech and images,
help accelerating the transmission of bulky data, and
extract rules from observing sample cases for an
expert system.
NNWs consist of data processing units �neurones),
arranged in layers and largely interconnected.
Each unit performs very simple and
fundamental computational functions. However,
the connectivity and the parallelism make the
network a powerful classifier and pattern recogniser.
In conventional computing systems the
information is stored in data files which are
separated from the programs retrieving old data
and generating new data. NNWs have a distributed
memory that is implicitly contained in the
network's weight structure.
Many neural network operations may also be
implemented with well-established mathematical
and statistical methods. NNWs, however, are still
operative where conventional methodology fails,
because a problem becomes analytically intractable
or runs out of computer resources. NNWs handle
nonlinearities and interactions which often plague
the classical methods. Neurocomputing helps analysts
to keep up with the explosion of data. One may
think of scanner data, the information generated by
automated checkin/checkout systems, or by computerised
reservation and travel counselling systems
�see computer reservation systems; decision
support system; reservation). The model offers
promising applications, including classification of
tourists into market segments.
Further reading
Andersen, J.A. and Rosenfeld, E. �eds) �1988)
Neurocomputing:Foundations of Research, Cambridge,
MA: MIT Press. �A collection of the seminal
articles from a variety of disciplines.)
Caudill, M. �1993) Neural Networks Primer, 3rd edn,
San Francisco: AI Expert. �An introduction for
readers lacking a background in mathematics.)
Haykin, S. �1994) Neural Networks. A Comprehensive
Foundation, New York: Macmillan. �An in-depth
introduction for readers seeking a rigorous
treatment.)
Mazanec, J.A. �1992) `Classifying tourists into
market segments: a neural network approach',
Journal of Travel & Tourism Marketing 1: 39±59. �An
example of an NNW application in tourism
marketing research.)
JOSEF A. MAZANEC, AUSTRIA
new product development
New product development involves the formation
and/or modification of tourism products. Itisa
research-driven, sequential and disciplined process
which recognises that tourism markets and
destinations constantly evolve �see life cycle). It
can either be achieved by acquisition of products or
by planning new ones, but both mechanisms
ensure the constant adjustment of tourism products
to meet changes in market demand.
New Zealand
New Zealand 413
CHRIS COOPER, AUSTRALIA
New Zealand lies in the South Pacific Ocean, 1,600
km east of Australia. It comprises a North and
South Island and numerous smaller ones, with a
total land area of 270,500 square km. Although
highly urbanised, with a resident population of 3.6
million, the population density is generally low. Its
touristic potential has been recognised since the first
European arrivals �early to mid-1850s), and this has
led to the development of the first governmentsponsored
national tourism office in 1901. From its
early beginnings with explorers and mountaineers,
and some package tours, New Zealand tourism
entered the modern era with the arrival of jet
engined aircraft in the late 1950s and the
subsequent breaking of distance barriers to major
markets. Growth in international tourist demand
has averaged 9 per cent over much of the
past two decades, ahead of both Pacific regional
and world tourism trends. Australians, Japanese,
North Americans and United Kingdom residents
comprise about 60 per cent of all arrivals; one-half
of these originate from Australia. There is strong