Big data use in determining competitive position: The case of theme parks in Hong Kong

ALBAYRAK T., Cengizci A. D., CABER M., Fong L. H. N.

JOURNAL OF DESTINATION MARKETING & MANAGEMENT, vol.22, 2021 (SSCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 22
  • Publication Date: 2021
  • Doi Number: 10.1016/j.jdmm.2021.100668
  • Journal Indexes: Social Sciences Citation Index (SSCI), Scopus, CAB Abstracts
  • Keywords: Theme parks, Business-to-business competitiveness, Online reviews, Latent dirichlet allocation, Hong Kong Ocean park, CUSTOMER SATISFACTION, GUEST EXPERIENCE, SERVICE QUALITY, ONLINE REVIEWS, HOSPITALITY, ATTRIBUTES, RATINGS, ANALYTICS
  • Akdeniz University Affiliated: Yes


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 competitorcomparative performance.