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…
Abstract
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 withrespect to market segmentation and as systematized by“Eurostyles”. Describes a sample application of a…
Abstract
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.
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Sara Dolnicar and Friedrich Leisch
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Practical implications
Given the demonstrated strengths of full binary measures, they should be used more by both practitioners and academics when measuring evaluative beliefs.
Originality/value
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.
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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…
Abstract
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.
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Astrid Dickinger, Lidija Lalicic and Josef Mazanec
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…
Abstract
Purpose
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.
Design/methodology/approach
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).
Findings
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.
Originality/value
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.
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Ulrike Gretzel, Yeong‐Hyeon Hwang and Daniel R. Fesenmaier
Destination recommender systems need to become truly human‐centric in their design and functionality. This requires a profound understanding of human interactions with technology…
Abstract
Purpose
Destination recommender systems need to become truly human‐centric in their design and functionality. This requires a profound understanding of human interactions with technology as well as human behavior related to information search and decision‐making in the context of travel and tourism. This paper seeks to review relevant theories that can support the development and evaluation of destination recommender systems and to discuss how quantitative research can inform such theory building and testing.
Design/methodology/approach
Based on a review of information search and decision‐making literatures, a framework for the development of destination recommender systems is proposed and the implications for the design and evaluation of human‐centric recommender systems are discussed.
Findings
A variety of factors that influence the information search and processing strategies that influence interactions with a destination recommender system are identified. This reveals a great need for data‐driven models to inform recommender system processes.
Originality/value
The proposed framework provides a basis for future research and development in the area of destination recommender systems. The paper concludes that the success of a specific destination recommender system will depend largely on its ability to anticipate and respond creatively to transformations in the personal and situational needs of its users. Such system intelligence needs to be based on empirical data analyzed with sophisticated quantitative methods. The importance of recommender systems in tourism marketing is also discussed.
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To evaluate the comparative effectiveness of perceptions‐based market segmentation strategies: to what extent do consumers' choice rules and the distinctness and variability of…
Abstract
Purpose
To evaluate the comparative effectiveness of perceptions‐based market segmentation strategies: to what extent do consumers' choice rules and the distinctness and variability of consumer preferences determine the success or failure of PBMS strategies?
Design/methodology/approach
The computer simulation is run on an artificial consumer market. Its firm and consumer agents enjoy a certain extent of autonomy and a limited capability of learning. Strategies for incorporating the choice information into the firms' segmentation schemes, consumers' brand choice rules, initial preference patterns and their variability over time are factors in the experimental design.
Findings
The market factors “brand choice rule” and “distinctness” and “adaptivity” of preferences significantly influence the profit performance of the segmentation and positioning strategies. The distinctness of the initial pattern of consumer preferences turns out to be least influential while the choice rule is most important.
Research limitations/implications
Computer simulation cannot replace analyses of real‐world data. When, however, advanced explanatory models are made to fit to empirical data the results sometimes are disappointing (and then do not get published). Computer simulation on artificial markets assists in exploring the reasons for success or failure.
Practical implications
Boundedly rational consumers; product classes which are technologically homogeneous and subject to communications‐driven differentiation; consumer preferences that are directly inaccessible and must be inferred from actual brand choice; consumers' perceptions and preferences evolving over time are realistic settings.
Originality/value
Controlling for conditions such as the consumers' choice rules and the distribution and variability of preferences in real markets demands a prohibitive research effort. No empirical study so far has tried to systematically relate the profit performance of marketing strategies to choice rules and preference distinctness and variability.
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Tourism is one of the most important and rapidly growing sectors for economic, cultural and global development of a country. Competition between tourist destinations is…
Abstract
Tourism is one of the most important and rapidly growing sectors for economic, cultural and global development of a country. Competition between tourist destinations is increasingly intense and is played internationally in a globalized scenario, where each destination competes with new and different competing destinations. In view of this, the tourism sector has had to equip itself with appropriate decision-making tools for studying and analyzing the competitiveness of the destinations. This chapter focuses on its analysis on the countries bordering on the Mediterranean Sea, one of the areas with the greatest worldwide attractiveness to tourists, characterized by different levels and models of tourism development. There are areas traditionally dedicated to hospitality, considered world leaders (such as France, Spain and Italy) and countries that have grown rapidly in their wake (Croatia and Greece), and on the other, more recently emerged destinations (Egypt, Morocco and Tunisia) that compete very well, focussing on an ‘exotic’ seaside offer accessible to all. The aim of this chapter is to carry out a research on the tourism competitiveness of Mediterranean countries. The analysis is based on the 14 pillars described in the Travel & Tourism Competitiveness Report 2019. In order to see how the 14 pillars of the competitiveness index are grouping on the countries, we applied the principal component analysis and hierarchical cluster analysis.
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J.A. Mazanec, G.I. Crouch, J.R. Brent Ritchie and A.G. Woodside