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Kronenberg, K., Fuchs, M. & Lexhagen, M. (2018). A multi-period perspective on tourism’s economic contribution : A regional input-output analysis for Sweden. Tourism Review, 73(1), 94-110
Open this publication in new window or tab >>A multi-period perspective on tourism’s economic contribution : A regional input-output analysis for Sweden
2018 (English)In: Tourism Review, ISSN 1660-5373, E-ISSN 1759-8451, Vol. 73, no 1, p. 94-110Article in journal (Refereed) Published
Abstract [en]

Purpose: Previous studies on tourism input-output (IO) primarily focus on a single year’s snapshot or utilize outdated IO coefficients. The purpose of this paper is to analyze the multi-period development of regional tourism capacities and its influence on the magnitude of the industry’s regional economic contribution. The paper highlights the importance of applying up-to-date IO coefficients to avoid estimation bias typically found in previous studies on tourism’s economic contribution.

Design/methodology/approach: For the period 2008-2014, national IO tables are regionalized to estimate direct and indirect economic effects for output, employment, income and other value-added effects. A comparison of Leontief inverse matrices is conducted to quantify estimation bias when using outdated models for analyzing tourism’s economic contribution.

Findings: On the one hand, economic linkages strengthened, especially for labour-intensive sectors. On the other hand, sectoral recessions in 2012 and 2014 led to an economy-wide decline of indirect effects, although tourists’ consumption was still increasing. Finally, estimation bias observed after applying an outdated IO model is quantified by approximately US$4.1m output, 986 jobs full-time equivalents, US$24.8m income and US$14.8m other value-added effects.

Research limitations/implications: Prevailing assumptions on IO modelling and regionalization techniques aim for more precise survey-based approaches and computable general equilibrium models to incorporate net changes in economic output. Results should be cross-validated by means of qualitative interviews with industry representatives.

Practical implications: Additional costs for generating IO tables on an annual base clearly pay off when considering the improved accuracy of estimates on tourism’s economic contribution.

Originality/value: This study shows that tourism IO studies should apply up-to-date IO models when estimating the industry’s economic contribution. It provides evidence that applying outdated models involve the risk of estimation biases, because annual changes of multipliers substantially influence the magnitude of effects.

Keyword
Tourism economic contribution, Estimation bias, Flegg location quotient, Multiplier analysis, Regional input-output model
National Category
Other Social Sciences not elsewhere specified
Identifiers
urn:nbn:se:miun:diva-32480 (URN)10.1108/TR-03-2017-0044 (DOI)000425284000007 ()
Available from: 2017-12-18 Created: 2017-12-18 Last updated: 2018-03-19Bibliographically approved
Hoepken, W., Fuchs, M. & Lexhagen, M. (2018). Big data analytics for tourism destinations. (4ed.). In: M. Khosrow-Pour (Ed.), Encyclopedia of Information Science and Technology : (pp. 349-363). IGI Global
Open this publication in new window or tab >>Big data analytics for tourism destinations.
2018 (English)In: Encyclopedia of Information Science and Technology  / [ed] M. Khosrow-Pour, IGI Global, 2018, 4, p. 349-363Chapter in book (Refereed)
Place, publisher, year, edition, pages
IGI Global, 2018 Edition: 4
National Category
Other Social Sciences not elsewhere specified
Identifiers
urn:nbn:se:miun:diva-32482 (URN)10.4018/978-1-5225-2255-3.ch031 (DOI)
Available from: 2017-12-18 Created: 2017-12-18 Last updated: 2018-01-15Bibliographically approved
Chekalina, T., Fuchs, M. & Lexhagen, M. (2018). Customer-based destination brand equity modelling: The role of destination resources, value-for money and value-in-use. Journal of Travel Research, 57(1), 31-51
Open this publication in new window or tab >>Customer-based destination brand equity modelling: The role of destination resources, value-for money and value-in-use
2018 (English)In: Journal of Travel Research, ISSN 0047-2875, E-ISSN 1552-6763, Vol. 57, no 1, p. 31-51Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Sage Publications, 2018
Keyword
destination branding, customer-based brand equity, destination resources, value-for-money, value-in-use, destination loyalty
National Category
Economic Geography
Identifiers
urn:nbn:se:miun:diva-29223 (URN)10.1177/0047287516680774 (DOI)000418175100003 ()2-s2.0-85038234083 (Scopus ID)ETOUR (Local ID)ETOUR (Archive number)ETOUR (OAI)
Available from: 2016-11-08 Created: 2016-11-08 Last updated: 2018-02-22Bibliographically approved
Chekalina, T., Fuchs, M. & Lexhagen, M. (2018). Destination brand promise: The core of customer-based brand equity modelling for tourism destinations. Tourism Analysis, 23(1), 93-107
Open this publication in new window or tab >>Destination brand promise: The core of customer-based brand equity modelling for tourism destinations
2018 (English)In: Tourism Analysis, ISSN 1083-5423, E-ISSN 1943-3999, Vol. 23, no 1, p. 93-107Article in journal (Refereed) Published
Abstract [en]

The present study contributes to the discussion on transferring the concept of customer-based brand equity (CBBE) to a tourism destination context. The core component of the proposed CBBE model for tourism destinations (CBDBE) considers customers’ evaluation of the destination promise in terms of the transformation of destination resources into value-in-use for tourists. The introduced CBDBE model consists of six interdependent constructs, including awareness, tourists’ perception of functional, tangible and social destination resources, value-in-use disclosing the purpose and benefits of consumption, value-for-money, satisfaction and loyalty. The model was tested for the leading Swedish mountain destination Åre for the summer season by using customer-based survey data and a linear structural equation modelling (SEM) approach. Findings confirm the hypothesized relationships and the hierarchical structure of the proposed model. Managerial implications are discussed and the agenda for future CBDBE research is outlined.

Place, publisher, year, edition, pages
Cognizant Communication Corporation, 2018
Keyword
destination branding, customer-based brand equity model, brand promise, value-in-use, value co-creation, SEM
National Category
Economic Geography
Identifiers
urn:nbn:se:miun:diva-29230 (URN)10.3727/108354218X15143857349800 (DOI)
Available from: 2016-11-08 Created: 2016-11-08 Last updated: 2018-04-12Bibliographically approved
Höpken, W., Eberle, T., Fuchs, M. & Lexhagen, M. (2018). Search engine traffic as input for predicting tourist arrivals.. In: Stangl Brigitte & Pesonen Juho (Ed.), Information and Communication Technologies in Tourism 2018: Proceedings of the International Conference in Jönköping, Sweden, January 24-26, 2018. Paper presented at Information and Communication Technologies in Tourism 2018 (pp. 381-393). New York: Springer
Open this publication in new window or tab >>Search engine traffic as input for predicting tourist arrivals.
2018 (English)In: Information and Communication Technologies in Tourism 2018: Proceedings of the International Conference in Jönköping, Sweden, January 24-26, 2018 / [ed] Stangl Brigitte & Pesonen Juho, New York: Springer, 2018, p. 381-393Conference paper, Published paper (Refereed)
Abstract [en]

Due to the perishable nature of tourism services and the limited capacity of tourism firms in serving customers, accurate forecasts of tourism demand are of utmost relevance for the success of tourism businesses. Nowadays, travellers extensively search the web to form expectations and to base their travel decision before visiting a destination. This study presents a novel approach that extends autoregressive forecasting models by considering travellers’ web search behaviour as additional input for predicting tourist arrivals. More precisely, the study presents a method with the capacity to identify relevant search terms and time lags (i.e. time difference between web search activities and corresponding tourist arrivals), and to aggregate these time series into an overall web search index with maximal effect on tourism arrivals. The study is conducted at the leading Swedish mountain destination, Åre, using arrival data and Google web search data for the period 2005-2012. Findings demonstrate the ability of the proposed approach to outperform traditional autoregressive approaches, thus, to increase the predictive power in forecasting tourism demand.

Place, publisher, year, edition, pages
New York: Springer, 2018
Keyword
Tourist arrival prediction, Web search traffic, Google Trends, Data Mining
National Category
Social Sciences
Identifiers
urn:nbn:se:miun:diva-33173 (URN)10.1007/978-3-319-72923-7_29 (DOI)978-3-319-72922-0 (ISBN)978-3-319-72923-7 (ISBN)
Conference
Information and Communication Technologies in Tourism 2018
Note

Awarded by the 1st place of Best Conference Paper

https://www.miun.se/en/ETOUR/nyheter/nyhetsarkiv/2018-2/enter-best-research-paper-award/

Available from: 2018-03-06 Created: 2018-03-06 Last updated: 2018-03-14Bibliographically approved
Höpken, W., Ernesti, D., Fuchs, M., Kronenberg, K. & Lexhagen, M. (2017). Big data as input for predicting tourist arrivals. In: Roland Schegg, Brigitte Stangl (Ed.), Information and Communication Technologies in Tourism 2017: Proceedings of the International Conference in Rome, Italy, January 24-26, 2017. Paper presented at ENTER Conference, Rome, Italy, 24-27 January, 2017. Springer Berlin/Heidelberg
Open this publication in new window or tab >>Big data as input for predicting tourist arrivals
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2017 (English)In: Information and Communication Technologies in Tourism 2017: Proceedings of the International Conference in Rome, Italy, January 24-26, 2017 / [ed] Roland Schegg, Brigitte Stangl, Springer Berlin/Heidelberg, 2017Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2017
Keyword
big data
National Category
Economic Geography
Identifiers
urn:nbn:se:miun:diva-29210 (URN)ETOUR (Local ID)ETOUR (Archive number)ETOUR (OAI)
Conference
ENTER Conference, Rome, Italy, 24-27 January, 2017
Projects
Kunskapsdestinationen II
Available from: 2016-11-08 Created: 2016-11-08 Last updated: 2018-01-11Bibliographically approved
Fuchs, M., Höpken, W. & Lexhagen, M. (2017). Business intelligence for destinations: Creating knowledge from social media. In: M. Sigala,U. Gretzel and E. Christou (Ed.), Advances in social media for travel, tourism and hospitality: new perspectives, practice and cases. Routledge
Open this publication in new window or tab >>Business intelligence for destinations: Creating knowledge from social media
2017 (English)In: Advances in social media for travel, tourism and hospitality: new perspectives, practice and cases / [ed] M. Sigala,U. Gretzel and E. Christou, Routledge, 2017Chapter in book (Refereed)
Abstract [en]

Due to an increasing number of online reviews in social media sites it has become vital for destinations, and businesses alike, to capture and regularly analyse these reviews to gain valuable new knowledge as input to managerial decision support. However, in practice, the possibility to manually extract and analyse the vast amount of available online reviews is fairly limited. Thus, the focus of this chapter is to highlight how knowledge from social media is utilized in the prototypically implemented destination management information system, DMIS-Åre. In order to extract and analyse user generated content from the social media platforms TripAdvisor.comand Booking.com a recently validated framework applying machine learning methods and a dictionary-based approach is presented

Place, publisher, year, edition, pages
Routledge, 2017
Keyword
Business intelligence;tourism;destination development, destination management
National Category
Economic Geography
Identifiers
urn:nbn:se:miun:diva-29238 (URN)ETOUR (Local ID)9781472469205 (ISBN)ETOUR (Archive number)ETOUR (OAI)
Projects
Kunskapsdestinationen II
Available from: 2016-11-08 Created: 2016-11-08 Last updated: 2018-03-14Bibliographically approved
Fuchs, M., Höpken, W. & Lexhagen, M. (2016). Applying Business Intelligence for Knowledge Generation in Tourism Destinations. In: Matthias Fuchs & Wolfram Höpken (Ed.), Big Data & Business Intelligence in the Travel & Tourism Industry: . Paper presented at IFITTtalk@Östersund on Big Data & Business Intelligence in Travel & Tourism Domain, April 11-12, 2016, Östersund, Sweden (pp. 19-26). Mid Sweden University
Open this publication in new window or tab >>Applying Business Intelligence for Knowledge Generation in Tourism Destinations
2016 (English)In: Big Data & Business Intelligence in the Travel & Tourism Industry / [ed] Matthias Fuchs & Wolfram Höpken, Mid Sweden University , 2016, p. 19-26Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Mid Sweden University, 2016
National Category
Economic Geography
Identifiers
urn:nbn:se:miun:diva-29246 (URN)ETOUR (Local ID)978-91-88025-62-3 (ISBN)ETOUR (Archive number)ETOUR (OAI)
Conference
IFITTtalk@Östersund on Big Data & Business Intelligence in Travel & Tourism Domain, April 11-12, 2016, Östersund, Sweden
Available from: 2016-11-08 Created: 2016-11-08 Last updated: 2016-12-20Bibliographically approved
Fredman, P., Wolf-Watz, D., Sandell, K., Wall-Reinius, S., Lexhagen, M., Lundberg, C. & Ankre, R. (2016). Dagens miljömål och framtidens fjällupplevelser: Iakttagelser av aktivitetsmönster, landskapsrelationer och kommunikationsformer. Östersund
Open this publication in new window or tab >>Dagens miljömål och framtidens fjällupplevelser: Iakttagelser av aktivitetsmönster, landskapsrelationer och kommunikationsformer
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2016 (Swedish)Report (Other academic)
Abstract [sv]

Rapporten "Dagens miljömål och framtidens fjällupplevelser – Iakttagelser av aktivitetsmönster, landskapsrelationer och kommunikationsformer" ger inledningsvis en bakgrund till fjällupplevelser och en genomgång av de metoder som använts i projektet. Därefter följer en beskrivning av de förändringar forskarna anser kunna se i de olika datamaterial som samlats in, följt av en diskussion av dagens miljökvalitetsmål i förhållande till framtidens fjällupplevelser.

Place, publisher, year, edition, pages
Östersund: , 2016
Series
Rapportserien / European Tourism Research Institute, ISSN 1403-4220 ; 2016:3
Keyword
miljökvalitetsmål, upplevelser, fjäll
National Category
Other Social Sciences
Identifiers
urn:nbn:se:miun:diva-29206 (URN)ETOUR (Local ID)ETOUR (Archive number)ETOUR (OAI)
Projects
Storslagen fjällmiljö Naturvårdsverket
Funder
Swedish Environmental Protection Agency
Available from: 2016-11-08 Created: 2016-11-08 Last updated: 2016-12-16Bibliographically approved
Fuchs, M., Höpken, W. & Lexhagen, M. (2016). Dynamic Need Fulfillment in a Collaborative Destination Environment. In: Matthias Fuchs & Wolfram Höpken (Ed.), Big Data & Business Intelligence in the Travel & Tourism Industry: . Paper presented at Big Data & Business Intelligence in the Travel & Tourism Industry (pp. 97-100). Mid-Sweden University
Open this publication in new window or tab >>Dynamic Need Fulfillment in a Collaborative Destination Environment
2016 (English)In: Big Data & Business Intelligence in the Travel & Tourism Industry / [ed] Matthias Fuchs & Wolfram Höpken, Mid-Sweden University , 2016, p. 97-100Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Mid-Sweden University, 2016
National Category
Economic Geography
Identifiers
urn:nbn:se:miun:diva-29243 (URN)ETOUR (Local ID)978-91-88025-62-3 (ISBN)ETOUR (Archive number)ETOUR (OAI)
Conference
Big Data & Business Intelligence in the Travel & Tourism Industry
Available from: 2016-11-08 Created: 2016-11-08 Last updated: 2016-11-23Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-6610-9303

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