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Self-catering accommodation is an important lodging alternative in some tourist destinations. Attributes such as the size, furniture and equipment; pool area; quietness; accessibility to beach; or Wi-Fi play an important role in the selection of this type of accommodation. Understanding tourists’ preferences is essential to improve services and gain competitiveness. In this article, a stated choice experiment between two hypothetical self-catering apartments is carried out in Maspalomas, a world renowned destination in the south of Gran Canaria, Spain. Consistent multinomial and mixed logit model specifications that incorporate systematic and random taste variation within tourists’ preferences are estimated. Willingness to pay for improving different service quality attributes is obtained. The findings are crucial and provide important insights to managers and policymakers in order to streamline the marketing and promotional strategies, as well as to make optimal investment decisions.

Importance–performance analysis (IPA) is a valuable tool for developing marketing strategies by prioritizing improvements to service quality attributes. Despite its simplicity and easy interpretability, IPA is not free from criticism. Moreover, IPA methodology does not consider the distribution of respondents when the importance–performance grid is displayed. By applying the analysis to a sample of four competing tourist apartment complexes located in the Canary Islands, this work aims to propose a new synthetic service quality indicator by importance–performance analysis (SSQI-IPA) to overcome the above-mentioned limitations. On the one hand, by comparing service performance with competitors, managers can establish adequate strategies to enhance their relative position in the market and achieve competitive advantages. On the other hand, taking into account the whole distribution of the importance–performance grid provides managers with more accurate results than traditional IPA.

Abstract. The aim of this paper is to analyse the regional tourist competitiveness performance in Spain. We use the seven pillars of tourism from a very detailed and complete database carried out by the Spanish Government -MoniTUR 2010 as primary data. Thus, we calculate several regional tourist competitiveness indices using data envelopment analysis (DEA) to analyse the robustness of the results obtained in the ranking of the tourist competitiveness for the 17 Spanish Autonomous Communities. Our results are robust to the use of two different modelling strategies: (1) input and output variables selection; and (2) virtual and super efficiency DEA models. Madrid and La Rioja are found to be the most competitive regions; meanwhile other inland regions of Spain like Extremadura and Aragón are the least competitive. The position of each of the laggard Autonomous Communities should be analysed by their respective destination management organizations (DMOs) in order to envisage adequate corrective measures.