The impact of user generated content (ugc) on tourists intention to choose a destination a factual survey in hcm city

BỘ GIÁO DỤC VÀ ĐÀO TẠOĐẠI HỌC KINH TỂ THÀNH PHỐ HỎ CHÍ MINH BÁO CÁO TÓNG KỂT ĐÈ TÀI NGHIÊN cứu KHOA HỌC THAM GIA XÉT GIẢI THƯỞNG ‘’ NHÀ NGHIÊN cứù TRẺ UEH” NÀM 2024 THE IMPACT OF USER-GE

INTRODUCTION

Background of the study

From October to December 2023, international tourism revenue surged by 90% compared to pre-pandemic levels, as reported by the UNWTO Looking ahead to 2024, the sector is projected to fully recover, with an expected growth of over 2% compared to 2019 figures Additionally, experts from the World Tourism Organization forecast continuous expansion of the tourism industry from 2024 to 2030.

As of February 2023, Vietnam boasts approximately 77 million internet users, with TikTok emerging as a significant player, attracting over 50 million users and ranking the country as the sixth-largest user base globally (Kemp, 2023) Furthermore, NapoleonCat's December 2023 data reveals that Vietnam has around 82 million Facebook users, representing 81.5% of its total population, alongside more than 11 million Instagram users These figures underscore the deep penetration of social media in Vietnam, particularly amid ongoing digital transformation efforts Globally, Statista reports that as of January 2024, there are 5.35 billion internet users, making up 66.2% of the world’s population (Ani Petrosyan, 2024), indicating that social media has become a vital aspect of daily life worldwide.

Thanks to this tendency, the online community is gaining consumer trust as a source of information for purchasing decisions or travel and tourism experiences (Hajli,

2018) When booking hotels or restaurants, consumers tend to rely on opinions, reviews, or online information provided by the online community (Moliner-Velazquez et al,

In the wake of the global Covid-19 pandemic, travel and tourism service companies are actively encouraging travelers to share their online reviews, fostering a sense of safety for specific destinations (Wut et al, 2022).

The traditional model of one-way communication from brands to consumers has evolved significantly with the rise of the internet, allowing consumers to share their opinions and engage with marketing messages in a more authentic manner This shift has fostered a mutually beneficial relationship, enabling consumers to access information more comprehensively while brands can expand their reach through User-generated content (UGC) UGC serves as an effective marketing tool that enhances brand-consumer connections; however, if not executed properly, it can lead to negative outcomes.

Reasons for choosing the topic

Tourism is a vital driver of economic growth, fostering the expansion of local economies by attracting foreign investments and enhancing the balance of payments It generates employment and income while stimulating domestic consumption, particularly in developing countries where research shows a positive link between tourism and economic progress The World Trade Organization highlights tourism's crucial role in job creation, GDP contribution, and foreign exchange generation, with tourism-related services being labor-intensive and interconnected with key sectors like transportation, entertainment, and financial services Furthermore, the tourism industry is projected to continue its robust growth from 2024 to 2030, underscoring its importance for global economic development.

Individual travelers increasingly depend on User-generated content (UGC) to inform their travel decisions throughout all stages of their journey—pre-travel, during travel, and post-travel UGC acts as a vital resource for reviewing destinations and products, shaping expectations, and aiding in travel planning While traveling, it allows for the evaluation of tourism services, and in the post-travel phase, it influences traveler satisfaction by bridging the gap between expectations and experiences Notably, research shows that 84% of leisure travelers utilize the internet for planning, highlighting the significant role of UGC in enhancing their overall travel experience.

User -generated content (UGC) significantly influences tourists' travel decisions, surpassing the impact of traditional travel agents As a reliable source of information, UGC reflects authentic experiences shared by users on various platforms Consequently, UGC on social media and booking sites plays a crucial role in raising awareness of travel destinations and guiding tourists in their destination choices (Dewi & Yuliati, 2018).

The benefits of tourism are clear, as consumers now encounter a wide range of products and are influenced more by the experiences and opinions of others than by traditional brand promotions.

2013) Therefore, this research aims to examine how User-generated content (UGC) influences tourists' decision-making processes when selecting a destination.

The purpose of the study

This study explores the factors that affect tourists' decisions when selecting a destination, focusing on User-generated content (UGC) We propose strategies to leverage UGC channels to attract more tourists, thereby enhancing the competitive advantage of localities rich in tourism resources and fostering the overall growth of the tourism industry.

• Identify the factors of UGC that influence the intention of tourists to choose destinations in Ho Chi Minh City.

• Investigate how User-generated content (UGC) channels impact the Intention to

Choose a Destination (ITC) in the era of 4.0, while also examining the reliability of these channels for consumers.

• Propose solutions to help local authorities, where there are tourist destinations, to effectively utilize User-generated content channels.

Research questions

• Which factors of Uscr-gcncratcd content (UGC) influence the choice of tourist destinations for residents of Ho Chi Minh City?

• What is the level of impact of each factor on the decision to choose a destination?

• What are the proposed solutions/policies to help local authorities in provinces access user-generated channels to develop local tourism?

The scope of the research

• Audience survey: Both males and females, individuals aged 18 and above.

• Space survey: Ho Chi Minh City, Vietnam.

• Time survey: The project implementation period is from December 2023 to

Significance and contribution of the study

Previous studies have often prioritized theoretical frameworks while neglecting their practical application and validation in real-world contexts, creating a disconnect between theory and practice This study aims to bridge that gap by not only developing theoretical frameworks but also emphasizing their relevance in the tourism industry It seeks to translate these theories into actionable strategies for stakeholders, ultimately contributing to academic understanding and providing practical benefits for destination marketers and local communities.

Businesses, Tour organizers, and Policymakers Therefore, this study seeks to bridge the gap between theory and reality, providing tangible value to both the research community and the tourism industry.

This study presents a novel approach to examining the impact of User-Generated Content (UGC) on tourists' destination choices, distinguishing itself from prior research by highlighting new factors and incorporating previously overlooked variables.

This research topic is crucial in understanding the dynamics of this influence and how it shapes the tourism landscape.

This research highlights the significance of user-generated content in tourism-rich areas, enabling local authorities and businesses to optimize their resources By examining the content created and shared by users, stakeholders can uncover insights into tourist attractions, preferences, and potential service improvements This understanding can foster more targeted marketing strategies, enhance tourist services, and ultimately boost tourist satisfaction.

This research topic plays a crucial role in socio-economic development, particularly within the tourism sector, which serves as a significant economic driver by creating jobs, enhancing infrastructure, and preserving culture By analyzing the impact of user-generated content on tourist behavior, stakeholders can refine their strategies to attract more visitors, thereby stimulating the local economy Furthermore, gaining insights into tourist preferences can promote sustainable tourism practices, aiding in the conservation of natural and cultural resources for future generations.

Structure of research

Chapter 1: Introduction - The short review of chapter 1 illustrates the overview of the background, the objective, the purpose, the scope and the relevance of study, the reasons to conduct the research.

Chapter 2: Literature review - Chapter 2 reviews the general concept of User generated content (UGC) and its application in tourism It establishes a theoretical background, highlights the study's relevance, and forms hypotheses for empirical investigation.

Chapter 3: Methodology - Chapter 3 outlines the approach for gathering and analyzing data to provide detailed explanations for the research questions.

Chapter 4: Findings - A deep data processing is performed via the use of Smart PLS 4.1.0.0

Chapter 5: Recommendation and conclusion- Based on the results, this chapter concludes with recommendations for improving the effectiveness of destination choices with User-generated content (UGC) for travelers Besides, there are also some recommendations for marketers and authorities to optimize the use of UGC in influencing tourists' destination preferences.

LITERATURE REVIEW

General concept

User -generated content (UGC) refers to creative material produced by individuals outside of traditional professional settings, as noted by Bruns (2016) This concept gained prominence with the rise of Web 2.0 in the early 2000s, leading to significant shifts in online design and interaction Currently, UGC is reshaping societal interactions on the internet, with modern consumers increasingly appreciating it as a reflection of authentic personal travel experiences (Cheung & Law 2011).

User -generated content (UGC) is characterized by its creation by users in various forms, including images, videos, reviews, star ratings, and comments This type of content is prevalent across numerous digital platforms and experiences significant growth as users produce content intended for others with similar interests.

Research indicates that user-generated content (UGC) is more trusted than official travel websites, agents, and advertisements (2012) Duguay et al (2015) found that opinions from friends and family remain the most influential factors in travel planning and booking Additionally, Chung et al (2015) highlighted that social networks serve as persuasive tools for accessing travel information These findings underscore the significant impact of UGC on tourists' behavioral intentions.

2.1.2 User-generated content in Tourism

User -generated content (UGC) in the tourism industry encompasses a range of materials such as blogs, reviews, personal experiences, and multimedia, all shared on social platforms This content serves to inform and inspire potential travelers about specific destinations and travel products, offering authentic insights and recommendations.

User -generated content (UGC) related to travel can be found on various platforms, including travel-specific websites and popular social media networks like Facebook, TikTok, TripAdvisor, and Google Maps This type of content plays a significant role in shaping travelers' experiences and decisions.

Planning a trip can be challenging due to the inherent risks of visiting unfamiliar places, prompting travelers to seek valuable information to ease their journey Many turn to social media as a trustworthy resource, where User-generated content (UGC) significantly impacts destination awareness and travel choices (Dewi & Yuliali, 2018; Xu et al., 2021) Online reviews from consumers not only share real user experiences but also serve as essential recommendations (Wilson et al., 2012; Pan et al., 2007; Cox et al., 2009) Consequently, UGC in tourism plays a vital role in shaping travelers' decision-making by offering authentic insights into destinations, accommodations, attractions, and activities.

I.I Pirogionic (1985) defines tourism as a leisure activity undertaken by individuals during their free time, involving travel and temporary stays away from their usual residences This form of activity is aimed at relaxation, health treatment, personal development, cultural enrichment, and sports, while also allowing for the appreciation of natural and cultural resources.

At the International Conference on Tourism held in Rome, Italy, from August 21 to September 5, 1963, researchers defined tourism as the totality of relationships, phenomena, and economic activities that arise from the travels and stays of individuals or groups away from their usual residence or country for peaceful purposes, with their destination serving as their temporary workplace.

Tourism, as defined by the World Tourism Organization (UNWTO), involves individuals traveling temporarily away from their usual residence to fulfill various needs such as sightseeing, exploration, and relaxation It encompasses activities that last less than a year and excludes travel for monetary gain, distinguishing itself as a form of active leisure rather than a routine lifestyle.

According to the International Association of Tour Operators (IUOTO):

Tourism involves traveling to destinations outside of one's regular residence for leisure and enjoyment, rather than for business purposes or employment.

Tourism can be defined as the activity of traveling away from one's usual residence to experience attractive destinations and their resources The primary goal of tourism is to engage with various products and services, including sightseeing, resorts, accommodations, cuisine, and entertainment, to fulfill the diverse needs and desires of individuals.

Theoretical Background

This chapter outlines the theoretical framework guiding the study, emphasizing two key theories: the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM) These theories form the foundation for developing the research model.

2.2.1 Theory of Planned Behavior (TPB)

The research applies the Theory of Planned Behavior (Ajzen, 1991), which evolved from the Theory of Reasoned Action (Ajzen and Fishbein, 1975).

Below is the representation of the TPB model:

According to TPB, three factors influence the intention to perform a behavior:

An individual's "attitude toward the behavior" reflects their positive or negative evaluation of engaging in that behavior, which is often influenced by their beliefs regarding its potential consequences and outcomes.

Subjective norms encompass the social pressures that influence an individual's behavior, stemming from the expectations of family, friends, and colleagues These norms drive individuals to conform to specific standards, motivated by a desire to meet the expectations of those in their social circle.

Perceived behavioral control refers to an individual's assessment of how easy or difficult it is to engage in a particular behavior, influenced by the resources and opportunities available to them.

Previous studies have traditionally applied the Theory of Planned Behavior (TPB) without adequately exploring the complex relationships between subjective norms and attitudes Notably, research by Chang (1998), Shepherd and O'Keefe (1984), Shimp and Kavas (1984), and Vallerand et al (1992) has revealed a significant causal link between these two elements, a connection that earlier studies often overlooked Chang's (1998) work particularly highlighted the importance of the pathway from subjective norms to attitudes Therefore, this research will investigate a modified TPB model, with a specific emphasis on the influence of subjective norms in the context of user-generated content and its implications for tourism.

Figure II.2: TAM model (Davis, 1986)

The model proposes that when users are introduced to a new technology, several factors influence their decision on how and when they will use it, specifically:

(1) Perceived usefulness (PU), meaning whether someone perceives the technology as useful for what they want to accomplish.

Perceived ease of use (PEOU) plays a crucial role in technology adoption; when technology is user-friendly and straightforward, it helps eliminate barriers to usage Conversely, a complex interface can lead to negative attitudes, discouraging users from engaging with the technology.

TAM has also demonstrated its utility in investigating factors influencing consumer utilization of various technologies, including wireless internet (Lu, Yu, Liu,

The Technology Acceptance Model (TAM) has been effectively applied across various technological contexts, including mobile phone adoption, internet banking, online shopping, e-government initiatives, and e-commerce This adaptability highlights its relevance in understanding technology use In this study, TAM is employed to analyze the impact of User-Generated Content (UGC) on travelers' decision-making processes.

Empirical review

Perceived usefulness (PU), as defined in Davis’s (1989) model, refers to an individual's belief that utilizing a specific technology can improve their job performance This concept encompasses the subjective views of potential users regarding the ability of an application system to enhance performance within an organizational setting Ultimately, this perception of usability signifies the expectation that the adoption of a particular technology will bring about beneficial outcomes and positive effects in its implementation.

Perceived ease of use (PEOU) refers to an individual's belief that using a system will require minimal effort, essentially reflecting their perception of the technology's usability According to Davis (1989), PEOU is crucial, as it is rooted in Bandura's (1982) research on self-efficacy, which evaluates a person's confidence in their ability to perform tasks in different scenarios This concept is instrumental in measuring users' confidence in effectively navigating technology with ease.

2.3.3 Subjective norms towards using ƯGC

Subjective norms are a measure of the extent to which an individual is influenced by the opinions of their family, friends, or colleagues (Conner & Armitage,

Subjective norms, as proposed by Ajzen (1991), reflect the social pressures individuals face when deciding whether to engage in a particular behavior These norms stem from the expectations of family, friends, and colleagues, influencing personal behavior and motivation to align with societal standards.

The advent of virtual communities, characterized by User-generated content

(UGC) and social media, has significantly influenced consumer behavior and online business tactics (Hajli, 2014).

Web 2.0 technology has shifted users from being mere spectators on the internet to active contributors of information, thereby becoming online information sources themselves (Hua & Wang, 2014) Furthermore, Hua and Wang (2014) highlight that User-generated content (UGC) is perceived to have a more profound impact on user or consumer behavior than professionally created content, as peers are seen as more unbiased and trustworthy.

Mir and Zaheer (2012) explored how the perceived credibility of user-generated content (UGC) affects consumer attitudes towards products and their subsequent purchase intentions Their research indicates that when social media users share credible product information, it enhances consumer attitudes towards UGC, ultimately leading to an increased likelihood of making a purchase.

This research investigates consumer attitudes towards User-generated Content (UGC) and its impact on destination selection decisions Attitudes serve as a crucial link to consumer behavior and influence behavioral intentions, as they reflect a tendency to evaluate entities with either a positive or negative bias, resulting in cognitive, emotional, or behavioral responses Before making significant purchases, consumers often seek information from diverse sources, which directly informs and shapes their attitudes.

The rise of social media has led to User-generated content (UGC) becoming a key information source for consumers during their decision-making processes, particularly in areas such as purchasing, travel planning, and hotel selection (Ye, Law, Gu, & Chen, 2011) Social networking sites play a crucial role in this trend.

(SNS) like Twitter and Facebook are becoming increasingly recognized as a burgeoning information resource about brands, products, or services (Mir & Zaheer, 2012).

Travel destinations offer visitors the chance to explore unique landscapes, connect with local communities, and immerse themselves in cultural experiences that fulfill their needs and provide value The decision to select a particular destination often reflects a desire to visit a specific location, influenced by careful consideration of its benefits and drawbacks, as well as impressions gathered from various external information sources, including online data.

& Heidari, 2017; Jalilvand & Samiei, 2012), but is also shaped by individuals’ personal traits, preferences, and emotions (Casalo, 2011; Jani et al., 2014; Jani, 2014; Leung & Law, 2010; Olga, 2015; Passafaro et al., 2015).

Hypothesis development

The research conducted by Ashaq Hussain Najar and Abdul Hamid Rather

A study conducted in 2020 examined how customer attitudes towards the benefits of User-generated Content (UGC) affect their purchasing intentions at restaurants By analyzing data from 330 customers who visited different restaurants and hotels, researchers utilized Structural Equation Modeling (SEM) alongside descriptive techniques The findings revealed that customer attitudes significantly influence the relationship between UGC benefits and their intention to make purchases at restaurants.

A recent study explored the motivations behind why internet users share User-Generated Content (UGC) and how these motivations affect their intention to share, both quantitatively and qualitatively By building on established online communication literature, the research identified key factors influencing UGC sharing It highlighted that attitudes and perceived behavioral control are significant predictors of sharing intentions, leading to the formulation of a specific hypothesis.

Hl: The perceived usefulness ofUGC (PƯ) positively affects the intention to use ƯGC to choose a destination

A study by Marta Liesa-Orús et al (2022) examined how older adults perceive the ease of use and usefulness of technology, revealing that perceived ease of use positively influences perceived usefulness Additionally, research by Ruoshi Geng and Jun Chen (2021) highlighted that the quality of user-generated content (UGC) interaction enhances its credibility and usefulness, which in turn boosts online purchase intentions, mediated by perceived usefulness and trust Further studies indicate strong positive correlations between perceived usefulness, flow experience, and user intention, while factors such as perceived enjoyment, trust, and attitude directly impact users' intentions to engage with technology Overall, perceived value, usefulness, and ease of use significantly enhance user attitudes towards technology adoption.

Based on these studies, it can be argued that "Perceived Usefulness" can have a significant influence on "Attitude towards UGC." Thus, this hypothesis is recommended:

H2: The perceived usefulness ofUGC (PU) positively affects attitudes towards the use of ƯGC

2.4.2 Perceived ease of use (PEOU)

Perceived ease of use (PEOU) refers to an individual's belief that adopting new technology will require minimal effort, as defined by Davis (1989) In our research, we specifically define PEOU as the perception that utilizing user-generated content (UGC) for travel planning is effortless Previous studies, such as those by Zhu and Chan (2014), indicate that ease of use significantly influences people's attitudes Therefore, we propose the following hypothesis:

H3: Perceived ease of use (PEOƯ) has a positive effect on attitudes towards UGC

Our study explores the concepts of perceived usefulness (PU) and perceived ease of use (PEOU) in the context of user-generated content (UGC) for travel decision-making We found that PEOU significantly influences how tourists evaluate the effectiveness of UGC, aligning with Ayeh's (2013) findings that highlight its predictive power Furthermore, research by Morosan (2012) reinforces the notion that systems perceived as easy to use are often regarded as more beneficial Consequently, we propose the following hypothesis:

H4: Perceived ease of use (PEOU) has a positive effect on perceived usefulness

2.4.3 Subjective norms towards using UGC (SNƯ)

Subjective norms are a measure of the extent to which an individual is influenced by the opinions of their family, friends, or colleagues (Conner & Armitage,

Subjective norms, defined as the perception of social pressure influencing an individual's decision to engage in a behavior, are a key component of the Theory of Planned Behavior (TPB) (Ajzen, 1991) Research consistently shows that the three pillars of TPB, including subjective norms, are strong predictors of behavioral intentions (Sparks & Shepherd, 1992; Kalafatis et al., 1999) However, in the context of organic food purchasing, Magnusson et al (2001) omitted this variable from their study, while Sparks and Shepherd (1992) found its impact on behavioral intention to be relatively weak This has led several scholars to suggest modifications to the TPB framework.

Research by Vallerand et al (1992) and others in 1998 highlights the substantial influence of subjective norms on attitudes This leads to the hypothesis that attitudes mediate the relationship between subjective norms and the intention to engage in specific behaviors, addressing a gap often neglected in prior studies.

H5: Subjective norms towards using UGC (SNU) have a positive impact on attitudes towards ƯGC

Numerous studies have explored consumer attitudes and purchase intentions (Gakobo & Jere, 2016; Larson, 2018; Wang et al., 2019), defining attitude as the degree to which an individual perceives a behavior as positive or negative (Conner & Armitage, 1998; Almajali, 2022) Research indicates that intention is influenced by one's attitude towards that behavior (Kraus, 1995; Shin et al., 2018) Ajzen's (1991) Theory of Planned Behavior (TPB) further supports that an individual's intention to engage in a behavior is closely linked to their attitude, suggesting that a more favorable attitude correlates with a stronger intention to act (Ajzen, 1991) In this context, the behavior pertains to a tourist's travel intention, while the attitude relates to perceptions of User-generated content (UGC), leading to the following hypothesis.

H6: The attitude towards ƯGC (AƯ) has a positive impact on the intention to choose a destination.

Proposed research model

With the combination of all five factors in term of the Literature Review, the

Hl proposed model is discovered as follow:

Hypothesis of research

The researchers propose a model for these following hypotheses:

HI: The perceived usefulness of UGC (PU) positively affects the intention to use UGC to choose a destination.

H2: The perceived usefulness ofUGC (PU) positively affects attitudes towards the use of UGC.

H3: Perceived ease of use (PEOU) has a positive effect on attitudes towards UGC.

H4: Perceived ease of use (PEOU) has a positive effect on perceived usefulness. H5: Subjective norms towards using UGC (SNU) have a positive impact on attitudes towards UGC.

H6: The attitude towards UGC (AU) has a positive impact on the intention to choose a destination.

RESEARCH METHODOLOGY

Research design

Quantitative formal research involves responding to online survey questionnaires and distributing a questionnaire via email to evaluate the scale and examine the theoretical model related to the subject.

This research involves a quantitative analysis of 287 participants to assess the significance of user-generated content (UGC) on tourists' destination choices The study aims to evaluate a theoretical model that highlights the influence of UGC on travelers' intentions to select specific destinations.

• Employing UGC to acknowledge which features affect tourists' intention.

• Formulating a scale for assessing the identified criteria.

• Establishing a regression model based on variable categorizations.

Types of research

This study utilized descriptive research to leverage the extensive data available on the statistical factors influencing the topic Through quantitative research methods, the researcher analyzed behavioral intentions across various variables, examining the correlations between them to identify positive or negative associations.

Research data

Data collection is crucial for statistical analysis in research, and it can be categorized into primary and secondary methods (Douglas, 2015) Accurate data is essential for analysis and validating results, significantly impacting study outcomes Therefore, ensuring precision in the data collection process is vital (Patil & Nageswara Yogi, 2011).

In simple terms, secondary data is any dataset that wasn't collected by the author

Secondary data refers to the analysis of information that was collected by another source, as defined by Boslaugh (2007) This pre-existing data is reassessed for its potential use in new research endeavors, even if it was not originally intended for those purposes, according to Vartanian (2010).

Secondary data provides a cost-effective and timely means of accessing essential information, as it requires less time to gather compared to primary data (Hox and Boeije, 2005) It acts as a shortcut for data collection in theory-driven research, facilitated by the availability of shared databases online With proper management and documentation, researchers can modify variables to extract new insights beyond the original data Consequently, many researchers initiate projects using secondary data, especially when examining students' responses to gamification in education, which can be obtained from various credible sources such as government agencies, private organizations, local institutions, and academic resources While this publicly available data is generally reliable, researchers must ensure they select an appropriate database aligned with their study's objectives.

This study utilized primary data sourced from publicly available research on Google Scholar, as well as articles from Vietnaminsider, McKinsey, and other relevant platforms These sources were chosen for their reliable and relevant insights into the factors influencing student engagement, performance, and satisfaction through gamification.

Primary data refers to information collected firsthand by researchers, as noted by Ajayi (2023) In this study, primary data was sourced directly from participants residing in Ho Chi Minh City through various methods, including surveys, individual interviews, and direct interactions The predominant methods for gathering this data were surveys and interviews, providing valuable insights into the research topic (Ajayi, 2023).

In this study, surveys were the primary method for collecting essential data due to their efficiency and broad reach This approach enables the distribution of questionnaires to a diverse demographic across various age groups via the internet, minimizing external limitations By utilizing surveys, researchers can effectively engage with a large number of participants through social networking platforms, making it the most suitable strategy for data collection.

Research method

The research employed a survey methodology to collect data, focusing on customer intent in destination selection through structured questionnaires distributed to potential respondents Influenced by prior studies (Bailey & Pearson, 1983; Coyle & Thorson, 2001; Erkan & Evans, 2016; Park et al., 2007; Prendergast et al., 2010), this approach allows for efficient data acquisition and the gathering of substantial information quickly The use of technology, particularly smartphones, enables respondents to complete surveys from various locations, enhancing convenience and accessibility Additionally, surveys are a cost-effective and time-efficient method for researchers However, it is crucial for the author to carefully analyze potential issues and implement mitigation strategies before presenting findings that accurately reflect attitudes and behaviors.

The research focused on social media users aged 18 to 35, requiring a sample size of at least 200 participants to account for a 5% error rate (Saunders et al., 2007) A total of 303 questionnaires were distributed, yielding 287 valid responses, surpassing the necessary threshold The study utilized 5-point Likert scales, with 1 representing strong disagreement and 5 indicating strong agreement Conducted over three months from December 2023 to February 2024, the survey primarily included university students living, studying, and working in Ho Chi Minh City.

Research tool

The study confirms the accuracy of scales used to measure the influence of user-generated content (UGC) factors on travelers' attitudes and intentions through the application of a questionnaire method.

The survey instrument is adapted from previous research and studies in the realm of User-generated content (UGC) and is modified to align with the context of this research.

Table III.l: Item scale for the all constructs

PUl UGC helps me plan trips more efficiently Ayeh

PU2 UGC makes my travel planning easier.

PU3 UGC makes it easier for me to make travel- related decisions.

PEOU1 I find UGC easy to understand.

PEOƯ2 I found it easy to find UGC for essential tra ve 1 in formation.

PEOƯ3 I found it easy to use UGC to plan my trip.

SNU1 My colleague thinks I should use UGC

(social networks, online reviews, websites, blogs, ) to decide whether to visit a tourist destination or not, for example: Da Lat.

SNƯ2 People who are important to me think I should use UGC when I’m thinking about choosing a travel destination.

SNU3 People who influence me think I should use

UGC for my intent to choose a travel destination.

SNU4 Among the people I communicate with regularly, many use UGC.

SNƯ5 Many people I communicate with use UGC to make decisions.

SNƯ6 The people I communicate with at work or school will continue to use UGC in the future to make decisions.

Attitudes toward user generated content

AU1 I have a positive attitude towards UGC Indrajit

Hafizullah Dar (2022) AU2 UGC comments or reviews can be reliable.

AU3 I have a favorable opinion to UGC to make a decision.

AƯ4 It is wise to use or refer to UGC for decision making.

Intention to choose destination rrci I will not hesitate to use UGC for travel information.

ITC2 I hope to use the UGC site's content to plan my future trips.

ITC3 I plan to use UGC content to inform decisions regarding my travel itinerary and vacation plans.

ITC4 I will most likely use UGC content for my travel plans.

The initial section of the questionnaire focuses on gathering background information from respondents, utilizing a nominal scale for non-numerical variables without any relative ranking among categories Additionally, the study employs a noncomparative scaling technique known as the Likert scale, which categorizes responses into distinct groups.

The questionnaire will prompt respondents to evaluate various statements using a Likert scale, which ranges from (1) Strongly disagree to (5) Strongly agree This method facilitates the analysis of ordinal regression in the Motivation scale, allowing for a nuanced understanding of participant attitudes.

This section examines how User-generated content (UGC) influences tourists' destination choices, focusing on perceived usefulness, perceived ease of use, and subjective norms regarding UGC Reliability is confirmed when Cronbach's Alpha is 0.6 or higher, and variables with low loading coefficients will be removed, while deviations will be assessed simultaneously Ultimately, regression analysis will be conducted to evaluate the suitability of the research model.

In research, the study population includes all individuals targeted for investigation, while the 'target population' consists of members meeting specific criteria for the study (Alvi, 2016) Sampling involves selecting a representative subset of this population, and it is crucial that the sample size is sufficiently large to support valid statistical analysis (Explorable, 2009).

Studying an entire population is often impractical, so researchers utilize a representative sample to reflect the broader group A sampling frame, as defined by Turner (2003), consists of source materials used to select this sample, which should accurately represent the population's characteristics (Taherdoost, 2016) This research targets Internet users in Ho Chi Minh City who rely on digital resources for travel planning Exclusion criteria will eliminate respondents who are not present in the city during the study or do not align with its objectives The sampling frame will be developed from potential respondents' lists sourced from local residency databases, tourism boards, and online platforms relevant to residents and tourists during the study period.

According to research by Hair et al (2006) and further supported by Hair et al (2014), it is recommended that the sample size for Exploratory Factor Analysis (EFA) be at least 50, with a preference for 100 or more In scientific research, a ratio of 5:1 or 10:1 between observations and analytical variables is standard, necessitating a minimum of 5 observations per measurement variable Given that this study's questionnaire includes 20 questions, applying the 5:1 ratio leads to a minimum sample size of 100 for EFA.

Regarding the minimum sample size required for regression analysis, Green

In 1991, it was suggested that when the primary goal of regression analysis is to evaluate the overall model fit—using metrics like R² and the F test—the minimum sample size required should be calculated as N = 8 times the number of variables plus one.

50, where 'N' represents the sample size and ’var' denotes the independent variable in the model Hence, the minimum sample size will be 8*3 + 50 = 74 for three independent variables.

The authors have determined that a minimum sample size of 200 is necessary based on the findings from Exploratory Factor Analysis (EFA) and Regression analysis To mitigate potential losses during the survey process, the actual sample size may be increased beyond this minimum requirement.

Generally, sampling methods can be categorized into two commonly employed types: probability sampling and non-probability sampling.

Probability sampling, also known as random sampling, gives each individual in a population an equal chance of selection, making it the ideal method for obtaining a representative sample (Sharma, 2017) However, researchers often face challenges in implementing this method due to difficulties in identifying a complete list of the population, which can lead to costly and time-consuming data collection Consequently, this study utilized a non-probability sampling method, enabling researchers to select elements in various forms.

Non-probability sampling techniques rely on non-random methods, including researcher judgment and convenience sampling, making the selection likelihood for participants unclear This method is ideal for researchers with limitations in time, budget, and access to samples Nevertheless, non-probability sampling can lead to systematic errors and biases in the sample selection (Alvi and Mohsin, 2016).

This study utilized convenience sampling, also known as accidental or opportunity sampling, which involves gathering samples from readily available populations (Alvi and Mohsin, 2016) While this method is favored for its speed, cost-effectiveness, and ease of use, it carries a significant risk of systematic errors and sampling biases Despite these drawbacks, the affordability and simplicity of convenience sampling make it a popular choice among researchers (Ackoff, 1953).

The data analysis was conducted using SmartPLS software version 4.1.0.0, focusing on two key components: the measurement model and the structural model The measurement model assessment ensures scale reliability by examining unidirectionality, reliability, convergent validity, and discriminant validity In contrast, the evaluation of the structural model consists of four distinct steps to thoroughly assess its effectiveness.

1 Evaluating the issue of multicollinearity within the structural model;

2 Evaluating the significance and relevance of relationships within the structural model;

3 Evaluating the extent of the impact measured by R2;

4 Evaluating the impact factor denoted as f2.

Sampling

In research, the study population includes all individuals targeted for investigation, known as the ‘target population,’ which consists of members meeting specific criteria (Alvi, 2016) To ensure accurate representation, population sampling involves selecting a subset of subjects, and the sample size must be sufficiently large to support valid statistical analysis (Explorable, 2009).

Studying an entire population is often impractical, leading researchers to examine a representative sample instead A sampling frame, as defined by Turner (2003), is a collection of source materials used to select this sample, which should accurately reflect the population's characteristics (Taherdoost, 2016) This research specifically targets Internet users in Ho Chi Minh City who use digital resources for travel planning Exclusion criteria will apply to individuals not present in the city during the study or those irrelevant to its objectives The sampling frame will be developed from local residency databases, tourism boards, and online platforms featuring residents and tourists in Ho Chi Minh City during the study period.

According to research by Hair et al (2006, 2014), a minimum sample size of 50 is recommended for conducting Exploratory Factor Analysis (EFA), with 100 or more being ideal In scientific studies, the optimal ratio of observations to analytical variables is typically 5:1 or 10:1, necessitating at least 5 observations for each measurement variable Given that this study's questionnaire includes 20 questions, applying the 5:1 ratio indicates that the minimum sample size required for EFA is 100 (20 questions multiplied by 5 observations each).

Regarding the minimum sample size required for regression analysis, Green

According to a 1991 study, when the primary goal of regression analysis is to evaluate the overall model fit—using metrics like R² and the F test—the recommended minimum sample size should be calculated as N = 8 times the variance plus additional factors.

50, where 'N' represents the sample size and ’var' denotes the independent variable in the model Hence, the minimum sample size will be 8*3 + 50 = 74 for three independent variables.

The authors have determined that a minimum sample size of 200 is necessary based on the findings from Exploratory Factor Analysis (EFA) and Regression analysis To mitigate potential losses during the survey process, the actual sample size may be increased beyond this minimum requirement.

Generally, sampling methods can be categorized into two commonly employed types: probability sampling and non-probability sampling.

Probability sampling, also known as random sampling, provides each individual in a population with an equal chance of selection, making it the preferred method for achieving a representative sample (Sharma, 2017) However, researchers often face difficulties in implementing this method due to the challenge of obtaining a comprehensive list of the entire population, which can lead to costly and time-consuming data collection Consequently, this study utilized a non-probability sampling method, enabling researchers to select elements in various forms.

Non-probability sampling techniques employ non-random methods, like researcher judgment or convenience sampling, making the chances of selection unknown This method is beneficial for researchers dealing with limitations in time, budget, and sample access Nevertheless, it is prone to systematic errors and sampling biases, as noted by Alvi and Mohsin (2016).

This study utilized convenience sampling, also known as accidental or opportunity sampling, which involves gathering samples from the readily available population (Alvi and Mohsin, 2016) While this method is popular for its speed, cost-effectiveness, and ease of implementation, it carries the risk of systematic errors and sampling biases Despite these drawbacks, the benefits of convenience sampling, such as its affordability and straightforward approach, make it a preferred choice over more complex sampling techniques (Ackoff, 1953).

Data analysis method

Data analysis was conducted using SmartPLS software version 4.1.0.0, focusing on two key components of the research model: the measurement model and the structural model The measurement model assessment ensures scale reliability by examining unidirectionality, reliability, convergent validity, and discriminant validity Evaluating the structural model involves a systematic four-step process.

1 Evaluating the issue of multicollinearity within the structural model;

2 Evaluating the significance and relevance of relationships within the structural model;

3 Evaluating the extent of the impact measured by R2;

4 Evaluating the impact factor denoted as f2.

FINDINGS

Introduction

This chapter aims to present the research findings by examining and testing the proposed research model Key sections include a detailed description of the survey sample and an analysis of the model We assess both the measurement model and the structural model through comprehensive analysis.

Descriptive Statistics

Table IV 1: Sample demographic characteristics report

Demographic characteristics Quantity Percent (%) Cumulative percent (%)

Average internet use per day

Source: Authors analysis survey data

Table IV.1 presents a comprehensive overview of the respondents involved in the study, detailing key demographic variables such as gender, age, occupation, income, travel frequency per year, and average daily internet usage.

The survey included 133 male participants (46.3%) and 154 female participants (53.7%), with women representing the majority Despite this slight female dominance, the gender ratio remains balanced, providing a comprehensive perspective from both genders.

The study's sample comprised individuals aged 18 and above, divided into four groups The largest segment, aged 18-22, included 149 respondents, representing 51.9% of the total Participants aged 23-27 made up 30.7% with 88 respondents, while those aged 28-32 accounted for 15% (43 respondents), and individuals over 32 years old comprised only 2.4% (7 respondents) The lower representation in the older age groups is attributed to the authors' limited outreach in these demographics.

The study categorizes participants into four occupational groups: students, employees, self-employed individuals, and the unemployed Notably, students make up the largest segment at 43.9%, while the unemployed group accounts for the smallest percentage at just 5.5%.

Understanding the monthly incomes of respondents is crucial for this study The largest segment consists of individuals earning under 5 million VND, comprising 42.2% of the total respondents Most participants are university students, with their income primarily derived from subsidies, allowances, or part-time jobs Consequently, the majority falls within the under 5 million VND income bracket, highlighting a significant distinction among income groups.

Travel frequency per year: There are 172 respondents, accounting for 59.9%, who choose an annual travel rate of less than 3 limes The group that travels more than

8 limes a year accounts for the least amount of people surveyed (only 2.1%).

A recent survey of 130 respondents revealed that the majority, 45.3%, spend between 1 to less than 3 hours online each day, indicating a significant trend in daily internet usage Conversely, only 7% of participants reported using the internet for less than 1 hour, highlighting that minimal internet use is relatively uncommon Overall, these findings underscore the prevalent role of the internet in daily life for most individuals.

Assessment of measurement model

We utilized SmartPLS software version 4.1.0.0 for data analysis, conducting a two-phase survey comprising a preliminary and an official survey Initially, we gathered data from 50 participants during the preliminary phase to assess the reliability of the measurement scale, which involved testing for omni-directionality, reliability, convergent validity, and discriminant validity Based on the results, necessary adjustments to the scale will be made before proceeding to the official survey.

The research team conducted an analysis on a sample of 50 individuals, concluding that the scales were effective and did not require modifications for future use The evaluation of the research model is divided into two key components: the assessment of the measurement model and the evaluation of the structural model.

To assess the measurement model, the research employs Cronbach's alpha (CA), Composite Reliability (CR), and Average Variance Extracted (AVE) It is essential that the Composite Reliability exceeds 0.7 and the Average Variance Extracted is greater than 0.5, as established by Fornell and Larcker (1981), to ensure acceptable measurement quality.

4.3.1 Internal consistency reliability - Cronbach's alpha

According to Nunnally and Bernstein (1994), the range of alpha from 0.7 to 0.8 was considered satisfactory in the measurement of internal consistency reliability -

J Internal consistency reliability' - Cronbach's alpha

Table IV.2: Cronbach’s Alpha, Composite reliability (rho_a), Composite reliability (rho_c), Average variance extracted (AVE)

Source: Authors analysis survey data

Subjective norms toward using UGC

Composite reliability assesses the internal consistency of scale indicators and serves as a superior alternative to Cronbach's Alpha It addresses several limitations of Cronbach’s Alpha, including its tendency to underestimate scale reliability, overlook intrinsic consistency, and sensitivity to the number of observed variables This makes composite reliability a more reliable measure for evaluating scale consistency (Netemeyer et al., 2003; Hair et al., 2016).

For a reliability coefficient similar to Cronbach's Alpha, a value between 0.7 and 0.8 is considered useful, while a score of 0.6 or higher may be acceptable if the research concept is innovative (Nunnally, 1978; Peterson, 1994; Slater, 1995) A reliability coefficient below 0.6 indicates a fundamental lack of consistency, warranting a reevaluation of the study's decisions (Hair et al., 2016) Once a sufficient reliability coefficient is established, variables with low variable-total correlations (below 0.4) should be removed, allowing for the adoption of a scale with a reliability greater than 0.7.

The test results, ranging from 0.857 to 0.915, indicate that all structures exhibit intrinsic confidence Consequently, all constructs demonstrated strong reliability, which was upheld and considered in subsequent studies.

4.3.3 Convergent validity - outer loadings and the average variance extracted (AVE)

The researcher evaluated the outer loadings and average variance extracted (AVE) to assess the authenticity of the study constructs and to ascertain the strength of the relationships between indicators within the same structure.

Hair et al (2016) state that for an observed variable to be considered of high quality, its outer loading coefficient should be at least 0.708 A coefficient of 0.5 indicates that the latent variable accounts for 50% of the variation in the observed variable.

In the study, variables with outer loadings coefficients between 0.4 and 0.7 may be removed from the scale if doing so enhances the composite reliability (CR) or if the average extracted variance (AVE) meets the established threshold.

AU ITC PEOU PU SNU

Source: Authors analysis survey data

In this convergent validity study, the average variance extracted (AVE) is a crucial metric alongside outer loading indices Defined as the sum of the mean squares of normalized load coefficients of observable variables within a latent variable, the AVE indicates convergence when it reaches 0.5 or higher, as per Falk & Miller (1992) At this threshold, the average latent variable explains at least 50% of the variation in each observable variable Notably, all constructs meet the criteria, with AVE values ranging from 0.701 (70.1%) for SNU to 0.786 (78.6%) for ITC, while variables with outer loadings below 0.7 are excluded from consideration.

4.3.4 Discriminant Validity - Fornell-IMeker Standard

Discriminant value, as defined by Cooper et al (2014), measures the lack of correlation between different sets of indicators for distinct ideas, ensuring that the variables assessed are consistent The Fornell-Larcker criterion is utilized to evaluate these values by comparing the square root of the Average Variance Extracted (AVE) with the correlation coefficients of two latent variables Specifically, the square root of a factor's AVE must exceed its highest correlation coefficient with other factors Additionally, the AVE must be greater than the square of the correlation coefficients among other parameters The results presented in the table below confirm that the constructs are indeed discriminant.

Table IV.4: Fornell - Larcker value

AU ITC PEOU PU SNU

Source: Authors analysis survey data

Assessment of structural mode

4.4.1 Multi-co Hine arity assessment

Multicollinearity evaluation is used to check the existence of multicollinearity in the structural model before testing the hypothesis model.

Source: Authors analysis survey data

Table IV.5 shows that all indicators in this study have VIF values below the cutoff threshold of 5, as established by Hair et al (2016), indicating the absence of multicollinearity in the research model.

4.4.2 Path coefficient and hypothesis testing

The research teams implemented the starter approach with 5,000 subsamples after validating the structural model to confirm the accuracy of the PLS estimates (Hair et al., 2016) This chapter will further elaborate on the influencing factors, with the path coefficient illustrated accordingly.

Source: Authors analysis survey data The estimates for the entire structural model, which contains all variables, arc shown in Table IV.6.

Table IV.6 The structural model assessment direct relationship

Source: Authors analysis survey data

Researchers commonly rely on p-values to assess statistical significance, interpreting them as the likelihood of error when rejecting a hypothesis A p-value threshold of less than 5% indicates that the hypotheses presented in Table IV.6 are statistically significant.

Specifically, PU had a positive effect on ITC (P = 0.517, t = 9.533, p < 0.05) The variable PU has a positive effect on AU, with the index of H2 being p = 0.199, t 2.864, p < 0.05 Hypothesis H3 and H4 also have similar results (p = 0.407, t = 6.743, p

The analysis indicates that hypothesis H5 is supported, with a p-value of 0.331 and a t-value of 4.068, demonstrating a positive influence of SNU on AU Additionally, hypothesis H6 confirms that AU also exhibits a positive effect, reinforcing the significance of these relationships in the study.

The evaluation of the independent variables' impact on the dependent variable reveals that hypothesis Hl, which posits that perceived usefulness positively influences the intention to choose a destination, is supported with a significance level of p = 0% This indicates that a one-unit increase in perceived usefulness leads to a 0.517 unit increase in the intention to choose a destination.

4.4.3 Assessment of the coefficient of determination (R2 value)

R Square and Adjusted R Square values range from 0 to 1, with higher values indicating greater predictive accuracy of the model Assessing an acceptable R-value can be challenging, as it varies based on the model's complexity and the research context According to Haử et al (2011), R2 values of 0.75, 0.5, and 0.25 are classified as significant, moderate, and weak, respectively.

Source: Authors analysis survey data

The adjusted R squared for "Perceived usefulness" is 0.713, indicating that independent variables account for 71.3% of its variance, with the remaining 28.7% attributed to systematic error and external factors Furthermore, the adjusted R squared values for "Attitudes towards UGC" and "Intention to choose a destination" are 0.798 and 0.823, respectively, signifying that "Perceived usefulness" explains 79.8% of the variance in "Attitudes towards UGC" and 82.3% of the variance in "Intention to choose a destination."

Besides evaluating the R Square value of the dependent variables, the change of

The R Square value, when an independent variable is excluded from a research model, serves as a key metric for assessing the impact of that variable on the dependent variable To further evaluate the significance of independent variables, Cohen (1988) introduced the f Square index table.

• f Square < 0.02: the effect is extremely small or has no effect.

Source: Authors analysis survey data

In the study's model, as shown in Table IV.8, the variables Perceived Usefulness (PU) and Social Norms Use (SNU) exhibit small effects on the dependent variable Attitude Toward Use (AU), with f-square coefficients ranging from 0.02 to 0.15 In contrast, Perceived Ease of Use (PEOU) and AU demonstrate medium impacts on AU and Intention to Continue (ITC), with f-square coefficients of 0.169 and 0.337, respectively Notably, both PEOU and PU significantly influence PU and ITC, displaying high impacts with f-square coefficients exceeding 0.35.

RECOMMENDATION AND CONCLUSION

Discussion

5.1.1, Result of descriptive statistics and signification

The descriptive statistics reveal that a significant majority of students interested in user-generated content (UGC) for travel destination selection are female (53.7%) and primarily students (48.4%), many of whom are either unemployed or working part-time with limited income Additionally, employees and self-employed individuals represent 32.8% and 15.3% of respondents, respectively, indicating they have the financial means to travel Notably, 51.9% of survey participants are aged 18-22, showcasing a younger demographic that shows a keen interest in tourist destinations, positioning them as potential future tourists who can enhance the tourism image of both Vietnam and local areas.

A recent study reveals that 45.3% of respondents spend 1-3 hours daily online, while 35.2% dedicate 3-5 hours, highlighting the significant growth of internet accessibility This trend presents a valuable opportunity for leveraging User-Generated Content (UGC) to enhance the tourism industry’s development.

The purpose of the study was to answer three questions identified at the outset of the study.

Question 1: Which factors of User-gene rated content (UGC) influence the choice of tourist destinations for residents of Ho Chi Minh City?

This study confirms the key factors influencing tourists' destination choices through User-generated content (UGC) The analysis highlights that perceived usefulness, perceived ease of use, and subjective norms positively impact attitudes towards UGC Notably, the findings reveal that the Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB) factors are crucial in shaping online travelers' acceptance of UGC for travel planning.

Question 2: What is the level of impact of each factor on the decision to choose a destination ?

The study reveals that perceived usefulness significantly influences usage intention, both directly and indirectly through attitudes toward usage Specifically, online travelers' assessment of the usefulness of User-Generated Content (UGC) has the most substantial direct effect on their intention to utilize UGC for travel planning This suggests that travelers are likely to intend to use UGC if they find it beneficial, irrespective of their emotional responses to its usage Previous research supports the link between perceived usefulness and usage intention (Casaló, Flavian, & Guinaliu, 2010; Huh et al., 2009; Venkatesh et al., 2003).

The findings highlight the critical role of perceived ease of use in user-generated content (UGC) scenarios, demonstrating that it significantly shapes travelers' opinions on the usefulness of UGC and their emotional reactions to its application in travel planning Moreover, the research indicates that perceived usefulness is predominantly affected by perceived ease of use, suggesting that travelers' assessment of the effort needed to engage with UGC greatly influences their perceptions of its value in planning their trips.

Surveys indicate that subjective norms have a limited yet positive influence on attitudes toward user-generated content (UGC) This suggests that individuals often consider UGC when making travel destination choices, influenced by perceived social pressure from others.

Hypothesis H6 proposes that attitude towards user-generated content (UGC) serves as a mediator between perceived usefulness, perceived ease of use, subjective norms, and the intention to choose a destination (ITC) The findings indicate that attitude towards UGC significantly influences the intention to select a destination within the tourism sector.

Question 3: What are the proposed solutions/policies to help local authorities in provinces access user-generated channels to develop local tourism?

To enhance user-generated content (UGC) accessibility, tourism stakeholders should prioritize its availability across various platforms, particularly tourism-related applications By diversifying UGC formats to include detailed reviews, images, videos, and personalized recommendations, the depth of content can be enriched, offering potential travelers a more comprehensive and nuanced view.

Local authorities should prioritize enhancing the usability of user-generated content (UGC) platforms, as this significantly influences travelers' perceptions and experiences By developing user-friendly interfaces, providing clear instructions, and improving overall usability, these platforms can better serve users For instance, incorporating an optional filter in booking apps to select destinations can streamline the search process and enhance user satisfaction.

To effectively promote local tourism through user-generated content (UGC), it is essential to understand the subjective norms of the target audience By fostering trends, contests, and discussions about travel UGC on popular social media platforms like TikTok, Twitter, Facebook, and YouTube, users can become more familiar with UGC Consequently, when planning their trips or selecting destinations, they are likely to prioritize UGC to enrich their travel experiences.

Practical implications

This study reveals that user-generated content (UGC) significantly influences tourist behavior and their intention to choose destinations within the tourism sector By gaining a deeper understanding of tourist motivations, local governments and investors can formulate effective policies to enhance and promote the country's tourism industry.

Perceived usefulness significantly influences tourists' destination choices, with a strong correlation (p = 0.517) supporting hypothesis Hl Notably, the variable "ƯGC makes my travel planning easier" has the highest weight at 0.891, indicating its critical role in enhancing the travel planning experience.

It can be concluded that when traveling, tourists will see opinions, reviews, and comments on social networking platforms to have an overview of their intended tourist destination.

Tourism stakeholders should prioritize improving the accessibility of User-Generated Content (UGC) across various platforms While apps like TripAdvisor, Booking.com, Airbnb, and Trave Ioka have integrated UGC, much of this feedback is limited to star ratings, lacking in-depth qualitative insights By expanding UGC formats to include descriptive reviews, photos, videos, and personalized recommendations, the content can become richer and offer potential travelers a more nuanced understanding of their experiences.

Developing a custom aggregation platform that collects user-generated content (UGC) from various sources based on specific hashtags, geolocations, or other criteria offers customers a comprehensive and authentic view of destinations, accommodations, and activities This tailored approach not only enhances the exploration of detailed information but also significantly improves users' decision-making processes, ultimately enriching their overall travel experience.

5.2.2 About perceived ease of use (PEOU)

Perceived ease of use significantly influences tourists' intention to choose a destination, with an impact coefficient of p = 0.845, supporting hypothesis H4 The research indicates that the item "I find UGC easy to understand" (PEOU1) has the highest outer weight of 0.885 This suggests that if tourists can easily access information from user-generated content (UGC) presented in simple language, free from slang or local dialects, they are more likely to find UGC helpful.

To enhance user satisfaction in travel planning, it is essential to simplify user-generated content (UGC) platforms This can be achieved by creating user-friendly interfaces, offering clear instructions, and improving overall usability For instance, a booking app can incorporate an optional filter that allows users to select specific destination types, such as mountains, forests, or beaches, thereby streamlining their search experience When these platforms prioritize easy navigation and clarity, users are more likely to find the process of utilizing UGC for travel planning straightforward and enjoyable.

Subjective norms play a notable role in influencing tourists' destination choices, though their impact is weaker compared to other factors, as indicated by a p-value of 0.331 supporting hypothesis H5 The item SNU6, which states, "The people I communicate with at work or school will continue to use UGC in the future to make decisions," received the highest outerweight of 0.868 This study found that 96.5% of respondents are students, employees, or self-employed individuals, highlighting that their colleagues and classmates significantly shape their perceptions and suggestions regarding the use of User-Generated Content (UGC) in travel decisions.

To enhance user-generated content (UGC) in the tourism industry, marketers should identify influential figures within travelers' social circles, such as friends and colleagues, and encourage them to share their experiences on popular platforms like TikTok and Facebook The rapid growth of these platforms allows for easy discovery of shared content Strategies like organizing travel-related competitions can further stimulate UGC by inviting participants to share their recommendations and experiences Additionally, analyzing travelers' interests on social media and engaging with influential tourism profiles can effectively leverage UGC to promote destinations and services.

Limitations of the research and future research

The study significantly contributed to both academic literature and marketing practices, yet it faced limitations primarily due to time and geographical constraints Factors like respondents' divided attention, interruptions in public settings, and potential reluctance to participate could introduce biases in the data collected Additionally, with a targeted sample size of 287 individuals, the research offers valuable insights into Ho Chi Minh City but may lack the diversity needed for broader generalizations.

To ensure the effectiveness of tourism solutions and policies, it is essential for planners and policymakers to tailor specific proposals to the unique economic, cultural, and social characteristics of each locality Recognizing the diverse cultural and economic aspects of different regions will enhance the implementation of these solutions, necessitating a blend of precision and adaptability to meet various challenges in the tourism sector.

This research article explores select factors of User-Generated Content (UGC) that impact tourists' destination choices, acknowledging the diversity of reference sources in tourism While numerous elements influence the decision-making process, this study highlights that many relevant factors remain underexplored in the context of UGC.

Future research should focus on the impact of user-generated content (UGC) on tourists' destination choices across various online platforms, including social media, travel forums, and specialized travel apps, to provide valuable insights for marketers Additionally, exploring the integration of emerging technologies, such as augmented reality (AR) and virtual reality (VR), can enhance the way travelers share and experience destinations, offering innovative opportunities for engagement.

Future research should investigate the role of user-generated content (UGC) in promoting sustainable and responsible tourism practices, a trend that has gained momentum as travelers become more environmentally conscious UGC not only offers economic advantages for sustainable tourism but also enhances environmental quality.

Conclusion

The research on "The Impact of User-Generated Content (UGC) on Tourists' Intention to Choose a Destination: A Case Study in HCM City" highlights the significant role of UGC in influencing tourist decisions, particularly as the tourism industry rebounds post-COVID-19 and adapts to the digital age The study identifies key factors such as Perceived Usefulness (PU), Perceived Ease of Use (PEOU), and Subjective Norms (SNU) within its model Utilizing SmartPLS software, findings indicate that PU has the strongest influence on tourists' destination choices, primarily driven by PEOU Additionally, while SNU affects attitudes towards UGC, its impact is comparatively lower, and no elements were excluded from the final analysis.

User -generated content (UGC), such as reviews and personal experiences from past visitors, is a valuable resource for potential tourists, shaping their expectations and influencing their travel decisions This research highlights the importance for tourism-rich regions to foster positive UGC, offering insights for tourism experts, advisors, and policymakers By understanding the impact of UGC on tourists' destination choices, these regions can implement strategies to enhance tourism promotion, improve services, and engage with visitors online Effectively leveraging UGC can increase a destination's appeal, boost tourist arrivals, and strengthen local economies, making it essential for tourism-driven areas to embrace the evolving dynamics of the digital age.

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