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The Effect of Brand Image and Price on Tourist Purchasing Decisions Choosing The Westin Resort Nusa Dua Bali as a Mice Venue Wedari, Ni Kadek Maedha; Pitanatri, Putu Diah Sastri; Wiartha, Nyoman Gede Mas
Indonesian Journal of Interdisciplinary Research in Science and Technology Vol. 2 No. 11 (2024): November 2024
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/marcopolo.v2i11.11293

Abstract

The tourism industry has great potential in generating foreign exchange and improving the welfare of the community. MICE (Meeting, Incentive, Convention, and Exhibition) is a rapidly growing sector in this industry. The MICE industry in Indonesia has been severely impacted by the COVID-19 pandemic, with many events postponed or canceled, affecting related economic sectors. Recovery of the sector requires strategies to adapt to the "new normal", event innovation, implementation of health protocols, and utilization of technology. Understanding brand image and personality is important for employees in creating a positive company image, such as at The Westin Resort Nusa Dua Bali. This study uses quantitative methods to evaluate the effect of brand image and price on purchasing decisions. The results showed that brand image and price have a positive but significant influence on purchasing decisions.
Analyzing TripAdvisor reviews to improve service quality at Courtyard Marriott Bali Nusa Dua Resort Vijaya, Bagaskara; Pitanatri, Putu Diah Sastri; Pratiwi, Kadek Andita Dwi
Annals of Management and Organization Research Vol. 7 No. 1 (2025): August
Publisher : goodwood publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/amor.v7i1.2857

Abstract

Purpose: This study aims to explore trends in guest satisfaction at the Courtyard by Marriott Bali Nusa Dua Resort by analyzing online reviews from TripAdvisor, with the objective of enhancing service quality. Methods: The research follows a systematic methodology that begins with scraping guest reviews from TripAdvisor, ensuring the collection of relevant data. Subsequently, a thorough data cleaning and preprocessing process is undertaken to guarantee high-quality data. The study then utilizes time series analysis, specifically the ARIMA model, to analyze the evolving patterns of guest satisfaction over time. Results/findings: The findings show that the majority of guest feedback is positive, indicating general satisfaction with the hotel. The ARIMA model reveals that guest satisfaction is highly influenced by previous satisfaction levels, suggesting a trend where past experiences strongly impact future perceptions. Conclusions: These results provide valuable insights into the key drivers of guest satisfaction, offering actionable recommendations for hotel management. By understanding the dynamic factors that influence guest experiences, management can improve service quality, respond more effectively to unexpected situations, and remain competitive in the market. Limitations: The ARIMA model does not account for external factors, such as holiday seasons or marketing changes, nor does it analyze the specific content of reviews or differentiate between guest segments. Additionally, comparisons with competitors can provide a broader strategic context for a more comprehensive understanding. Contribution: The combination of sentiment analysis and time series forecasting in this study offers a unique contribution, enabling data-driven decisions that support continuous service improvement and customer satisfaction.
The role of seasonal trends in shaping tourist preferences for luxury resort: Big data approach Pamungkas, Luh Made Gunapria Hindu Rajeswari; Pitanatri, Putu Diah Sastri; Adinda, Clearesta
Journal of Sustainable Tourism and Entrepreneurship Vol. 7 No. 1 (2025): September
Publisher : Goodwood Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/joste.v7i1.2927

Abstract

Purpose: This study aims to examine seasonal patterns in tourist preferences for luxury resort stays in Bali, with a focus on how cultural backgrounds influence accommodation choices. The goal is to help resorts better understand guest behavior and optimize occupancy strategies. Methodology/approach: The research analyzes monthly online review data from Tripadvisor for Bvlgari Resort Bali, a prominent luxury hotel. A time-series analysis using the ARIMA (Autoregressive Integrated Moving Average) model is applied to forecast occupancy trends. Prior to modeling, the data is tested for stationarity. In addition to forecasting, the study explores guest preferences by analyzing cultural characteristics inferred from reviews, categorizing them into collectivist and individualist orientations. Results/findings: Findings reveal that occupancy trends do not strictly align with the hotel’s predefined seasonal categories. Instead, they are shaped by global travel trends and cultural factors. Guests from collectivist cultures tend to prefer facilities that support group interaction and shared experiences, while those from individualist cultures prioritize privacy, exclusivity, and personalized services. The ARIMA model delivers accurate forecasting results, helping to predict future occupancy rates effectively. Conclusion: IoT integration enhances the reliability of hospital-based PV systems. Tourist behavior is not solely dictated by conventional seasons but also by cultural expectations and travel motivations. Leveraging these insights allows hotels to better align operations, marketing, and pricing strategies with actual guest preferences. Limitations: The study is limited to a single resort and uses data from one online review platform, which may not fully capture the diversity of all guests. Contribution: This study contributes to tourism analytics, cross-cultural marketing, and hotel management by offering data-driven strategies to enhance occupancy performance.