Importance-Performance Analysis of Tourism Destination Attractiveness: Technology and Other Influencing Factors

Importance-Performance Analysis of Tourism Destination Attractiveness: Technology and Other Influencing Factors

João Ferreira do Rosário, Maria de Lurdes Calisto, Ana Teresa Machado, Nuno Gustavo
Copyright: © 2022 |Pages: 24
DOI: 10.4018/978-1-7998-8306-7.ch012
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Abstract

This chapter presents an importance-performance analysis to evaluate the ability of a destination's attributes to attract tourists through tourism stakeholder perceptions. In this case, one of Europe's larger destination cities, Lisbon, was considered. It departs from the proposition that tourists are not the most knowledgeable about a destination while the evaluation of a destination's competitiveness from the supply side perspective is scarce. This stakeholder feedback approach to identifying a destination's attributes to attract tourists showed that only 7 of the 40 attributes (five of them related to accessibility and technological infrastructures as municipality responsibility) fall in the IPA grid Concentrate Here quadrant, results that are consistent with the recently received Best City Destination and Best City Break World Travel Awards. This research shows the relevance of multiple stakeholders' feedback to evaluate a city's attributes, including the feedback about the city's need to improve its technological offer through an integrated digital strategy.
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Importance-Performance Analysis Model

The importance-performance analysis (IPA) model was created by Martilla & James (1977) as a business research methodology to help management decisions, based on the concepts of choice models of multiple attributes. It helps investment allocation decisions to maintain and improve consumer satisfaction, with a first application in the vehicle industry marketing. The objective of the IPA model is to make data interpretation accessible and to suggest relevant measures to improve competitiveness, based on the optimization of the allocation of resources among the various attributes analyzed (Abalo, Varela, & Manzano, 2007).

In the traditional IPA technique, data from customer satisfaction surveys or service quality surveys (Cronin & Taylor, 1992) are utilized to construct a two-dimensional grid. In this grid, the x-axis depicts the attribute importance, and the y-axis the attribute performance (satisfaction or service quality). The mean of performance and importance divides the grid into four quadrants, identifying areas of high or low attribute performance combined with high or low attribute importance. The grid (Table 1) provides managers with information on the aspects that (I) require additional investment as they are underperforming; aspects that (II) are performing well but need continued investment, aspects that (III) are of low priority and require little investment, and aspects that (IV) are at risk of overinvestment as they are of small importance to customers (Coghlan, 2012).

Table 1.
IPA grid
LowHigh
ImportanceHighI
Concentrate here
(Increase resources)		II
Keep up the Good Work
(Sustain resources)
LowIII
Low Priority
(No change in resources)		IV
Potential Overkill
(Curtail Resources)
Performance

(Martilla & James, 1977)

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