UM  > Faculty of Business Administration
Residential Collegefalse
Status已發表Published
Big data use in determining competitive position: The case of theme parks in Hong Kong
Albayrak, Tahir1; Cengizci, Aslıhan Dursun2; Caber, Meltem3; Nang Fong, Lawrence Hoc4
2021-12-01
Source PublicationJournal of Destination Marketing and Management
ABS Journal Level1
ISSN2212-571X
Volume22Pages:100668
Abstract

Theme park operators need to understand their competitiveness in a destination to increase their market share. This study adopted the big data approach by analysing online reviews to assess the competitiveness of a theme park called Ocean Park (HKOP) against its competitor Disneyland (HKDL) in Hong Kong. Firstly, the strengths and weaknesses of HKOP were identified through importance performance analysis (IPA) and asymmetric impact performance analysis (AIPA). Results revealed that urgent action is required for the ‘Staff’, ‘Fast pass’, and ‘F&B and prices’ attributes, since they are the basic attributes that perform poorly. Secondly, to determine HKOP's competitive position against its rival HKDL, importance performance competitor analysis (IPCA) and asymmetric impact competitor analysis (AICA) were performed. On the one hand, the IPCA results indicated that the ‘Shows’, ‘Spend time’, and ‘Time & weather’ attributes are the strengths of HKOP when compared with HKDL. On the other hand, the AICA findings suggested urgent action for the ‘Child friendly’, ‘Waiting time’, ‘F&B and prices’, ‘Staff’, and ‘Accessibility’ attributes of HKOP. This research is one of the scarce studies that follow a holistic approach to understand competitiveness by examining each attribute's company-based and competitor-comparative performance.

KeywordBusiness-to-business Competitiveness Hong Kong Ocean Park Latent Dirichlet Allocation Online Reviews Theme Parks
DOI10.1016/j.jdmm.2021.100668
URLView the original
Indexed BySSCI
Language英語English
WOS Research AreaSocial Sciences - Other Topics ; Business & Economics
WOS SubjectHospitality, Leisure, Sport & Tourism ; Management
WOS IDWOS:000719274100002
PublisherELSEVIERRADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
Scopus ID2-s2.0-85118535681
Fulltext Access
Citation statistics
Document TypeJournal article
CollectionFaculty of Business Administration
DEPARTMENT OF INTEGRATED RESORT AND TOURISM MANAGEMENT
Corresponding AuthorAlbayrak, Tahir
Affiliation1.Akdeniz University, Tourism Faculty, Tourism Management Department, Antalya, Campus, Turkey
2.Antalya Bilim University, Tourism Faculty, Antalya, Turkey
3.Akdeniz University, Tourism Faculty, Tourism Guidance Department, Antalya, Campus, Turkey
4.Faculty of Business Administration, Centre for Cognitive and Brain Sciences, University of Macau, Taipa, Avenida da Universidade, Macau SAR, E22-3037, China
Recommended Citation
GB/T 7714
Albayrak, Tahir,Cengizci, Aslıhan Dursun,Caber, Meltem,et al. Big data use in determining competitive position: The case of theme parks in Hong Kong[J]. Journal of Destination Marketing and Management, 2021, 22, 100668.
APA Albayrak, Tahir., Cengizci, Aslıhan Dursun., Caber, Meltem., & Nang Fong, Lawrence Hoc (2021). Big data use in determining competitive position: The case of theme parks in Hong Kong. Journal of Destination Marketing and Management, 22, 100668.
MLA Albayrak, Tahir,et al."Big data use in determining competitive position: The case of theme parks in Hong Kong".Journal of Destination Marketing and Management 22(2021):100668.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Albayrak, Tahir]'s Articles
[]'s Articles
[Caber, Meltem]'s Articles
Baidu academic
Similar articles in Baidu academic
[Albayrak, Tahir]'s Articles
[Cengizci, Aslıh...]'s Articles
[Caber, Meltem]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Albayrak, Tahir]'s Articles
[Cengizci, Aslıh...]'s Articles
[Caber, Meltem]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.