The design of product packaging significantly impacts consumer behavior by attracting attention and influencing purchasing choices within a competitive market environment. This research explores the effects of visual packaging elements, such as branding, imagery, and descriptive text, on consumer preferences and decisions. By examining regular, organic, and character-themed milk packaging, the study seeks to determine which features effectively capture attention and drive engagement. Employing eye-tracking tools (Tobii Pro Spectrum and Tobii Pro Lab) alongside survey analysis, the study evaluates consumer focus through fixation durations and heatmap visualizations. The findings indicate that character-themed packaging draws the most attention, with colorful illustrations and prominently displayed brand names playing a central role. Fixation data highlight brand names as the most consistently engaging feature across all packaging types. Regression analysis further reveals that cultural familiarity influences preferences, as international participants favored designs resembling those from their home regions or familiar global brands. These insights underscore the importance of visually appealing and culturally relevant designs in building consumer engagement and brand loyalty. The study offers valuable guidance for marketers and designers aiming to create packaging that resonates with diverse audiences and market demands.

Singapore relies heavily on the tourism industry, which was severely affected by COVID-19. During the early phase of the pandemic, the Singapore government created a campaign reassuring locals and encouraging them to “travel” within Singapore. During the pandemic, travelers’ focus shifted to pandemic-related topics. This study examined 8441 customer textual reviews from seven luxury hotels in the Marina Bay area through Google Travel derived from SCTM 3.0. In order to determine the new attributes affecting customer satisfaction, this study used UCINET 6.0 and Text2Data as part of text mining. Subsequently, SPSS was used for descriptive analysis and regression analysis to identify the relationship between the attributes in the customer textual reviews and the overall satisfaction of the customers. The results showed that all the attributes were significant in terms of overall customer satisfaction, with three attributes, sentiment polarity, readability, and word length, positively affecting overall customer satisfaction. Through social media and online platforms, consumers express their thoughts and feelings about online reviews of many products and services. With the adopted methodology, the industry may be able to benefit from this abundance of information in order to adjust strategies and increase financial benefits post-COVID-19.

Bali is known as one of the region’s most popular and long-established mass tourism destinations. However, the tourism sector in Indonesia saw a drastic decrease in the number of local and foreign tourists due to COVID-19. The objective of this study is to analyze the factors that are related to customer satisfaction post-COVID-19 in Bali’s resorts. The data consist of a total of 7370 hotel reviews collected from Google Travel. Text mining was used to conduct a frequency analysis to determine which attributes were frequently mentioned. Additionally, semantic network analysis was used to analyze customer experiences and satisfaction in Bali resorts. As a result, the top 88 keywords were divided into five clusters such as “Location”, “Health Protocol”, “Destination Resort”, “Value”, and “F&B”. The first quantitative analysis, factor analysis, shows there are 18 words out of 88 words related to six different clusters. Furthermore, the absolute value result of the linear regression analysis indicated that intangible service is affecting customer satisfaction negatively. As a result of the factor analysis, the two aspects that are related to the intangible service, “hospitality” and “staff”, are considered to be the most important aspects of resorts and should be improved in order to increase customer satisfaction.

The purpose of this study is to evaluate green hotels in Sentosa Island, Singapore, with big data analysis utilizing online reviews regarding environmental sustainability practices. Tourism, while providing significant economic benefits, also contributes to environmental degradation, particularly through the hotel industry, which accounts for a substantial share of global greenhouse gas emissions. Sustainable tourism practices are becoming increasingly popular as the public becomes more aware of the environment. As a result, green hotels emerged as a solution and hotels are taking steps to become eco-friendly. Based on the 3579 online reviews, the findings indicate that green practices, including water and energy conservation, play a crucial role in enhancing customer satisfaction, alongside traditional hospitality elements such as service quality and amenities. The integration underscores the importance of incorporating sustainability into core operations without compromising the high standards of service that guests expect. This research contributes to the understanding of sustainable hospitality practices, offering actionable recommendations for policymakers and hotel managers to foster environmentally friendly practices while maintaining customer satisfaction.

This research aims to explore the relationship between customer satisfaction and various extracted factors at dive resorts in the Red Sea, Egypt, utilizing a hybrid methodology of descriptive and diagnostic analytics applied to online review data. Employing techniques such as KH coder for text analysis, exploratory factor analysis (EFA), and linear regression, this study systematically identifies key elements that influence customer satisfaction. Findings reveal that activities related to diving and marine life markedly enhance guest satisfaction, underscoring the critical role these aspects play in the overall appeal of Egyptian coastal tourism. Conversely, areas such as dining and amenities were identified as needing improvement. The originality of this study lies in its application of big data analytics to dissect and understand customer feedback in a sector-specific context, providing strategic insights for the sustainable advancement of coastal tourism in Egypt. By focusing on dive resorts, this research highlights their integral role in coastal tourism and offers a model for leveraging online customer reviews to enhance service quality and promote sustainable practices within the tourism industry, contributing to the overall growth and sustainability of coastal tourism.

The MICE (Meetings, Incentives, Conventions, and Exhibitions) industry plays a crucial role in driving the rapid growth of the local economy, and Busan is emerging as a significant MICE destination in Asia. This study uses online customer reviews to acquire a more in-depth insight into BEXCO (Busan Exhibition & Convention Center)’s customer experiences and satisfaction levels. A meticulously curated dataset of 2339 Google Maps reviews was assembled using the Outscraper tool. KH Coder 3 software was employed for a comprehensive analysis, including word frequency and co-occurrence analysis. The co-occurrence analysis revealed three prominent clusters of keywords: ‘MICE’ (Meetings, Incentives, Conferences, and Exhibitions), ‘Facility’, and ‘Environment’. This study also conducted exploratory factor analysis (EFA) and identified six factors that play a significant role in shaping customer experiences at BEXCO. Using these six factors, this study performed regression analysis to determine their impact on customer satisfaction. The results show that Food and Beverage, Venue, Event Space, and Parking Facilities significantly affect customer satisfaction. This research aims to bridge existing knowledge gaps, offering valuable insights that can contribute to the strategic development and enhancement of BEXCO’s offerings, thereby influencing the overall impact on the MICE sector in Busan.