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Travel counseling and recommender systems on the Internet have not yet become smart enough to fulfill the elementary functions a fastidious consumer may expect. The EU‐funded project named DieToRecs (http://dietorecs.itc.it/) aims at improving recommender system functionality by incorporating relevant findings from tourist behavior research. The computational intelligence needed to optimize the user‐system encounter greatly depends on how far the user has advanced in his travel decision process. This report elaborates the levels of counseling intelligence, explores the basic marketing paradigm of matching the products/services desired and offered, and ponders on the consequences for devising a recommender or counseling system capable of learning.

Discusses consumer lifestyle attributes and psychographic data with respect to market segmentation and as systematized by “Eurostyles”. Describes a sample application of a neural network model to assist in the transfer of the Eurostyle typology to the USA.

Image analysis faces data reduction problems when deriving low‐dimensional image spaces (‘perceptual maps’) from multidimensional profile data. The neurocomputing methodology of Self‐Organizing Maps may contribute to finding a radically parsimonious representation. The principles of SOM methodology are shown in a case study on the company images of nine Austrian tour operators.

The Tourism Knowledge Map is part of a larger project that develops a web portal entitled The Tourism Knowledge Base. On this web site the users will be offered comprehensive information about the organizations in Austria providing tourism education, research, and consulting services. The Knowledge Map assists the users in finding and optimizing a set of keywords for launching an efficient search operation in tourism‐centred databases accessible on the internet. The underlying method is the Self‐Organizing Map, one of the most widely accepted techniques of unsupervised learning. Three real‐world examples illustrate how a Knowledge Map may be constructed from the frequencies of keywords and their co‐occurrence in the abstracts of tourism‐related research papers, articles, and reports.

This article seeks to provide an empirical identification of groups of marketing scholars who share common beliefs about the role of science and the logic of scientific discovery.

Topology is used representing network quantization to identify empirically classes of marketing researchers within a representative sample of marketing professors.

Six distinct classes of marketing scholars were found. They differ with regard to popularity (size) and productivity (levels of publication output). Comparing the sub‐samples of German‐speaking and US respondents shows cross‐cultural differences.

The study enhances understanding of the current scientific orientation(s) of marketing. It may help to motivate marketing scholars to ponder on their own positions and assist them in judging where they may belong. Future comparisons over time would give an indication about the future of the academic discipline of marketing.

Over the last decade various parametric approaches toward modelling segmented perception‐preference structures such as combined MDS and latent class procedures have been introduced. These methods, however, are not tailored for qualitative data describing consumers’ redundant and fuzzy perceptions of brand images. A completely different method is based on topology‐sensitive vector quantization (VQ) for consumers‐by‐brands‐by‐attributes data. It maps the segment‐specific perceptual structures into bubble‐pie‐bar charts with multiple brand positions demonstrating perceptual distinctiveness or similarity. Though the analysis proceeds without any distributional assumptions, it allows for significance testing. The application of exploratory and inferential data processing steps to the same database is statistically sound and particularly attractive for market structure analysts. A brief outline of the VQ method is followed by a sample study with travel market data, which proved to be particularly troublesome for conventional processing tools.

Academic researchers love multi‐category answer formats, especially five‐ and seven‐point formats. More than a decade ago Josef Mazanec concluded that these formats may not the best choice, and that simple binary‐answer options are preferable in some empirical survey contexts. The purpose of the present study is to investigate empirically Mazanec's hypothesis in the context of the measurement of evaluative beliefs relating to fast‐food restaurants.

The authors conducted an online experiment that asked respondents to assess evaluative beliefs relating to fast‐food brands using either a forced binary (n=100) or a seven‐point answer format (n=100). The authors also measured preferences for each of the fast‐food restaurants, user friendliness, and recorded the actual completion times for the survey.

The results indicate that the full binary answer format outperforms the popular seven‐point multi‐category format with respect to stability, concurrent validity, and speed of completion.

Given the demonstrated strengths of full binary measures, they should be used more by both practitioners and academics when measuring evaluative beliefs.

This study provides empirical evidence of the strong performance of the forced binary‐answer format for the measurement of evaluative beliefs, and thus challenges current measurement practice among academics and practitioners.

Online reviews have been gaining relevance in hospitality and tourism management and represent an important research avenue for academia. This study aims to illustrate the discrimination between positive and negative reviews based on single word items and the sector-specific relevance of hidden topics.

By probing two parallel approaches of entirely unrelated analytical methods (penalized support vector machines and Latent Dirichlet Allocation), the analysts explore differences in language between favorable and unfavorable reviews in three service settings (hotels, restaurants and attractions).

The percentage of correctly predicted positive and negative review reports by means of individual word items does not decrease if reports from the three tourism businesses are analyzed together.

However, there is limited generalizability of the discriminant words across the three businesses. Also, the latent topics relevant for generating customers’ review reports differ significantly between the three sectors of tourism businesses.

Tourism benefits from increasing leisure — a reliable mechanism? Several scholars in tourism have been inspired by the end of the decade to engage in forecasting projects. Especially, the Delphimethod has become popular among tourism experts in Germany, Switzerland and Austria. One of the results almost unanimously accepted is confidence in future growth of leisure time and paid holidays, in a rising number of families travelling twice or three times a year and in an overall increase of all tourism/travel categories. Though there is widespread understanding that the growth rate will be diminishing, optimism prevails with respect to the tourism/leisure ratio (i.e. “the proportion of leisure time spent in tourism”. The “lemming” paradigm is still dominating the minds of policy makers and managers: “We do not know why they move, but we know that, at certain times of the year, they all start moving — and we have a fair idea of the destinations.”