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PostprintThis is the accepted version of a paper published in Journal of Travel Research. This paper has been peer-reviewed but does not include the final publisher proof-corrections or journal pagination. Citation for the original published paper (version of record):Chekalina, T., Fuchs, M., Lexhagen, M. (2017) Customer-based destination brand equity modelling: The role of destination resources, value-for money and value-in-use. Journal of Travel ResearchAccess to the published version may require subscription. N.B. When citing this work, cite the original published paper. Permanent link to this version:http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-29223 This study contributes to the development of knowledge on transferring the concept of customer-based brand equity to a tourism destination context. Keller's (2009) brand equity pyramid is utilized as the comparison framework to reveal similarities but also overlaps, differences and gaps on both the conceptual and measurement level of existing brand equity models for destinations. Particularly, the inner core of the model depicts the complex mechanisms of how destination resources transform into benefits for tourists overlooked by prior research. This study proposes a customer-based brand equity model for destinations, which consists of five dependent constructs, including awareness, loyalty, and three destination brand promise constructs constituting the inner core of the model, namely, destination resources, value-in-use and value-for-money. The model was repeatedly tested for the leading Swedish mountain destination Åre, by using a linear structural equation modelling approach. Findings confirm the path structure of the proposed model. Customer-based destination brand equity modelling -The role of destination resources, value-for-money and value-in-useAbstract: This study contributes to the development of knowledge on transferring the concept of customer-based brand equity to a tourism destination context. Keller's (2009) brand equity pyramid is utilized as the comparison framework to reveal similarities but also overlaps, differences and gaps on both the conceptual and measurement level of existing brand equity models for destinations. Particularly, the inner core of the model depicts the complex mechanisms of how destination resources transform into benefits for tourists overlooked by prior research. This study proposes a customer-based brand equity model for destinations, which consists of five dependent constructs, including awareness, loyalty, and three destination brand promise constructs constituting the inner core of the model, namely, destination resources, value-in-use and value-for-money. The model was repeatedly tested for the leading Swedish mountain destination Åre, by using a linear structural equation modelling approach. Findings confirm the path structure of the proposed model.Key words: destination branding, customer-based brand equity, destination resources, valuefor-money, value-in-use, destination loyalty To answer these quest...

Because of high fluctuations of tourism demand, accurate predictions of tourist arrivals are of high importance for tourism organizations. The study at hand presents an approach to enhance autoregressive prediction models by including travelers’ web search traffic as external input attribute for tourist arrival prediction. The study proposes a novel method to identify relevant search terms and to aggregate them into a compound web-search index, used as additional input of an autoregressive prediction approach. As methods to predict tourism arrivals, the study compares autoregressive integrated moving average (ARIMA) models with the machine learning–based technique artificial neural network (ANN). Study results show that (1) Google Trends data, mirroring traveler’s online search behavior (i.e., big data information source), significantly increase the performance of tourist arrival prediction compared to autoregressive approaches using past arrivals alone, and (2) the machine learning technique ANN has the capacity to outperform ARIMA models.

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Decision-relevant data stemming from various business processes within tourism destinations (e.g. booking or customer feedback) are usually extensively available in electronic form. However, these data are not typically utilized for product optimization and decision support by tourism managers. Although methods of business intelligence and knowledge extraction are employed in many travel and tourism domains, current applications usually deal with different business processes separately, which lacks a cross-process analysis approach. This study proposes a novel approach for business intelligence-based cross-process knowledge extraction and decision support for tourism destinations. The approach consists of (a) a homogeneous and comprehensive data model that serves as the basis of a central data warehouse, (b) mechanisms for extracting data from heterogeneous sources and integrating these data into the homogeneous data structures of the data warehouse, and (c) analysis methods for identifying important relationships and patterns across different business processes, thereby bringing to light new knowledge. A prototype of the proposed concepts was implemented for the leading Swedish mountain destination Å re, which demonstrates the effectiveness of the proposed business intelligence architecture and the gained business benefits for a tourism destination.