FACTORS INFLUENCING
HOTEL BOOKING INTENTION FOR
5-STAR HOTELS IN
THAILAND
Jooyeon Kim
Student, Assumption University
Thailand
ABSTRACT
Purpose – This
study investigates the key factors influencing travelers’ intentions to book
five-star hotels in Thailand through online platforms. It addressed the growing
competition in the luxury hotel industry and examined how online reviews,
price, trust, brand awareness, and website quality shape booking behavior.
Design/Methodology/Approach – A
quantitative approach using a structured online questionnaire was applied. Data
were collected from 385 international travelers who booked 5-star hotels in
Thailand within the past three years. Descriptive statistics and multiple
linear regression were employed to assess the impact of the five independent
variables on booking intention.
Findings – Price
emerged as the strongest positive predictor, followed by trust and brand
awareness, whereas online reviews and website quality demonstrated unexpected
negative effects. The model explained 46.4% of the variance in booking
intention.
Practical
implications – The findings provide actionable guidance for hotel operators and online
booking platforms. Key recommendations include strategically managing online
reviews, optimizing website usability, implementing transparent pricing
strategies, and reinforcing brand presence to enhance booking intentions.
Originality/value – This
study broadens the understanding of digital consumer behavior in luxury tourism
by identifying expected and unexpected factors influencing online hotel
bookings in Thailand. It provides practical insights for enhancing marketing
strategies and online engagement in the luxury hospitality industry.
Keywords: 5-star
hotels, Booking intention, Online reviews, Price, Trust
JEL Classification
Code: C12, M31, Z32
1. INTRODUCTION
1.1 Background of the
Study
Thailand consistently
ranks among the world’s leading tourist destinations, attracting millions of
international travelers each year and contributing significantly to the
national economy (JLL, 2025). The luxury hotel sector, particularly 5-star
hotels, plays a critical role in meeting the demands of travelers seeking
premium accommodations and exceptional services. Despite the impacts of
COVID-19, Thailand’s travel industry has recovered rapidly, with international
arrivals projected to reach approximately 35.5 million in 2024, approaching
pre-pandemic levels (JLL, 2025).
This recovery has
driven rapid growth in the luxury hotel market, especially in key locations
such as Bangkok and Phuket, where new 5-star properties continue to emerge
alongside ongoing developments (LH Bank, 2024). As a result, competition has
intensified, prompting luxury hotels to adopt innovative strategies to attract
and retain customers.
Concurrently, the
digital transformation of the travel industry has reshaped how travelers
research, compare, and book accommodations. Travelers increasingly rely on online
reviews, digital platforms, and user-generated content to inform their
decisions (Xiang et al., 2017). In both e-commerce and e-hospitality sectors,
trust is a crucial factor, reducing perceived risk and influencing users’
willingness to engage in transactions (Gefen et al., 2003; Kim et al., 2011).
Website quality and brand reputation also significantly affect booking
intentions in the luxury segment (Kandampully and Hu, 2007). However, limited
research exists on how these online and hotel-specific factors influence
booking behavior in Thailand’s 5-star hotel market.
Understanding these
factors is essential for hotel operators seeking to enhance online visibility
and optimize marketing strategies. This study addresses this gap by examining
the key determinants of international travelers’ intentions to book 5-star
hotels in Thailand, providing insights relevant to both academic research and
practical management in the luxury hospitality industry.
1.2 Problem Statement
Despite the rapid
growth of Thailand’s luxury hotel market, 5-star hotels face increasing
competition in attracting and retaining travelers. While online platforms play
a crucial role in booking decisions, limited research exists on how specific
digital and hotel-related factors, such as online reviews, website quality,
trust, and brand reputation, affect booking intentions in the Thai luxury hotel
sector (Hudson and Thal, 2013; Ye et al., 2009; Wang et al., 2016).
Without clear
insights into these factors, hotels may struggle to optimize their digital
presence and marketing strategies, potentially impacting competitiveness and
profitability. Addressing this gap is essential to guide both academic
understanding and practical management in Thailand’s 5-star hotel industry.
1.3 Objectives of the
Study
1.4 Research
Questions
1.5 Significance of the
Study
This study
contributes to understanding how digital and hotel-specific factors influence
travelers’ booking intentions for 5-star hotels in Thailand. While Thailand’s
luxury hotel market is rapidly growing, research on the role of online reviews,
website quality, trust, and brand awareness in shaping booking decisions
remains limited (LH Bank, 2024; JLL, 2025; Hudson and Thal, 2013). By examining
these factors, the study advances academic knowledge on digital decision-making
in luxury tourism and offers practical guidance for hotel operators seeking to
optimize online platforms and marketing strategies.
Moreover, the
findings highlight broader implications of digital interactions for the travel
industry, illustrating how online engagement influences traveler behavior and
supporting sustainable growth and strategic planning in Thailand’s competitive
luxury hotel sector (Roziqin et al., 2023; Zhong et al., 2021). A focused
investigation on 5-star hotel bookings online ensures that the insights are relevant
for enhancing competitiveness and operational effectiveness in the luxury
segment.
2. LITERATURE REVIEW
2.1 Theoretical
Background
This study
investigates factors influencing travelers’ intention to book 5-star hotels
online, drawing on two prominent theoretical frameworks: the Theory of
Planned Behavior (TPB) and the Stimulus-Organism-Response (S-O-R)
model. These frameworks provide a robust foundation for understanding the
psychological and behavioral mechanisms underlying online hotel booking
decisions.
2.1.1 Theory of
Planned Behavior (TPB)
Ajzen’s (1991) Theory
of Planned Behavior posits that behavioral intentions are determined by
attitudes, subjective norms, and perceived behavioral control. In online hotel
booking, attitude reflects travelers’ evaluations of booking a hotel
(e.g., confidence in the website), subjective norms represent social
influences (e.g., peer reviews), and perceived behavioral
control refers to the ease of completing the booking process. Previous
studies have applied TPB to hotel booking decisions, showing that online
reviews, price, and trust significantly shape booking intentions (Han and Kim,
2010).
2.1.2
Stimulus-Organism-Response (S-O-R) Model
The S-O-R model
(Mehrabian and Russell, 1974) explains consumer behavior as a process in which
external stimuli (S), such as website quality and brand awareness,
influence internal organism states (O) like trust and perceived
value, which in turn generate behavioral responses (R), such as booking
intention. In online hospitality, stimuli including visual appeal, information
quality, and social proof have been shown to affect trust and perceived value,
ultimately shaping booking behavior (Huang et al., 2017).
Together, TPB and the
S-O-R model underpin the conceptual framework of this study, showing how
digital and hotel-specific factors, online reviews, price, trust, brand
awareness, and website quality, influence travelers’ intentions to book 5-star
hotels in Thailand.
2.2 Independent Variables
Based on prior
research, five independent variables were identified as key determinants of
online hotel booking intention: Online Reviews, Price, Trust, Brand
Awareness, and Website Quality.
2.2.1 Online Reviews
Online reviews
provide peer-generated insights into hotel quality and reliability. Both
quantitative ratings and qualitative feedback, such as photos and comments,
influence travelers’ perceptions (Sparks and Browning, 2011; Filieri and
McLeay, 2014). Positive reviews enhance perceived authenticity and trust,
increasing booking likelihood (Ye et al., 2009; Hudson and Thal, 2013).
2.2.2 Price
Price reflects not
only monetary cost but also perceived value and service quality (Noone and
Mattila, 2009). Travelers evaluate price relative to amenities and brand
reputation. Competitive and transparent pricing, including promotions and
discounts, positively affects purchase intention in online booking environments
(Kim et al., 2017; Assaker, 2020).
2.2.3 Trust
Trust represents a
traveler’s confidence in hotel reliability, integrity, and secure transactions
(Gefen et al., 2003). In online contexts, trust reduces perceived risk and
promotes booking behavior (Sparks and Browning, 2011). Verified reviews, secure
platforms, and strong brand reputation enhance trust, particularly for high-end
hotels (Kim et al., 2008; Yoon and Uysal, 2005).
2.2.4 Brand Awareness
Brand awareness
facilitates recognition and perceived reliability, influencing consumer choice
(Keller and Swaminathan, 2020). In luxury hotels, familiarity signals quality
and reduces uncertainty, supporting confident booking decisions (Han and Hyun,
2017; Kim et al., 2018).
2.2.5 Website Quality
Website quality
encompasses design, content accuracy, navigability, and security. High-quality
websites improve user experience, support trust formation, and positively
influence booking behavior (Wang et al., 2015; Ali, 2016; Bahari et al., 2018).
For luxury accommodations, website quality also reflects professionalism and
service standards.
2.3 Conceptual
Framework
Based on the
literature, this study proposes that online reviews, price, trust, brand
awareness, and website quality act as independent variables directly
influencing the dependent variable, booking intention. Each variable has
been widely recognized as critical in shaping consumer decision-making in
online hospitality.
Figure 1. Conceptual Framework: Factors
influencing hotel booking intention for 5-star hotels in Thailand
Source: Constructed
by the author.
3. RESEARCH METHODS
AND MATERIALS
3.1 Research Design
This study employed a
quantitative research design to examine the influence of five independent
variables—online reviews, price, trust, brand awareness, and website quality—on
the dependent variable, booking intention for 5-star hotels in Thailand.
A structured
questionnaire was developed, consisting of screening questions, demographic
items, and 24 measurement items for the study constructs. All items were
measured using a five-point Likert scale (1 = Strongly Disagree, 5 = Strongly
Agree). A pilot test with 30 respondents was conducted to refine the
instrument, and internal consistency was assessed through Cronbach’s alpha. The
final dataset was analyzed using descriptive statistics, reliability testing,
and multiple linear regression.
3.2 Sampling and Data
Collection
The target population
comprised international travelers who had booked a 5-star hotel in Thailand
online within the past three years. This population was chosen to ensure respondents
possessed relevant booking experience with luxury accommodations and digital
platforms.
The minimum required
sample size was determined using a 95% confidence level, a 5% margin of error,
and a population proportion of 50%, resulting in 385 respondents. A convenience
sampling technique was adopted, and data were collected through an online
questionnaire distributed to eligible participants. Screening questions ensured
respondent suitability.
3.3 Research
Instrument
The survey instrument
consisted of three sections. The first included screening questions confirming
respondent eligibility. The second collected demographic details such as
gender, age, education, income, and travel frequency. The third section
contained 24 items measuring online reviews, price, trust, brand awareness,
website quality, and booking intention.
A pilot test (n = 30)
was conducted to ensure clarity and reliability. All constructs achieved
Cronbach’s alpha values above the threshold of 0.70, confirming good internal
consistency (see Table 1). The final survey items are provided in Appendix A.
Table 1. Reliability Analysis of Study
Constructs (Pilot Test, n = 30)
|
Variables/Measurement Items |
Cronbach’s Alpha |
Strength of Association |
|
Dependent Variable |
||
|
Booking Intention |
0.79 |
Acceptable |
|
Independent Variable |
||
|
Online Reviews |
0.88 |
Good |
|
Price |
0.85 |
Good |
|
Trust |
0.81 |
Good |
|
Brand Awareness |
0.79 |
Acceptable |
|
Website Quality |
0.89 |
Good |
Source: Constructed by the
author.
3.4 Validity and
Reliability
Content validity was
ensured by adapting measurement items from prior studies in hospitality and
e-commerce research. Reliability was confirmed through pilot testing, with
Cronbach’s alpha values exceeding 0.70 for all constructs. These results
demonstrate the questionnaire’s suitability for full-scale data collection.
3.5 Statistical
Treatment of Data
Survey data from 385
participants were analyzed using SPSS. Descriptive statistics summarized
demographic characteristics. Reliability was assessed through Cronbach’s alpha,
and multiple linear regression was used to test hypotheses regarding the
influence of the independent variables on booking intention.
Table 2. Research hypotheses and statistical
techniques
|
Hypotheses |
Description |
Statistical Techniques |
|
|
H1 |
H1o |
Online reviews have no significant influence
on travelers’ booking intention for 5-star hotels in Thailand. |
Multiple Linear Regression |
|
H1a |
Online reviews have an influence on travelers’
booking intention for 5-star hotels in Thailand. |
||
|
H2 |
H2o |
Price has no significant influence on
travelers’ booking intention for 5-star hotels in Thailand. |
Multiple Linear Regression |
|
H2a |
Price has an influence on travelers’ booking
intention for 5-star hotels in Thailand. |
||
|
H3 |
H3o |
Trust has no significant influence on
travelers’ booking intention for 5-star hotels in Thailand. |
Multiple Linear Regression |
|
H3a |
Trust has an influence on travelers’ booking
intention for 5-star hotels in Thailand. |
||
|
H4 |
H4o |
Brand awareness has no significant influence
on travelers’ booking intention for 5-star hotels in Thailand. |
Multiple Linear Regression |
|
H4a |
Brand awareness has an influence on travelers’
booking intention for 5-star hotels in Thailand. |
||
|
H5 |
H5o |
Website quality has no significant influence
on travelers’ booking intention for 5-star hotels in Thailand. |
Multiple Linear Regression |
|
H5a |
Website quality has an influence on travelers’
booking intention for 5-star hotels in Thailand. |
||
Source: Constructed by the
author.
4. RESULTS AND
DISCUSSION
4.1 Descriptive
Analysis
Table 3 presents the
demographic profile of the respondents (N = 385). The majority were female
(72.7%) and between 35–44 years old (55.1%). Most participants resided in Asia
(66%), held a master’s degree (75.8%), and were employed full-time (67.8%). The
most common annual income range was USD 40,000–59,999 (52.5%). Regarding travel
frequency, 87.5% reported traveling one to two times per year, confirming that
the sample represents active international travelers with relevant booking
experience.
Table 3. Demographic characteristics of
respondents (N = 385)
|
Demographic Factors |
Frequency |
Percent |
|
|
Gender |
Male |
105 |
27.3 |
|
Female |
280 |
72.7 |
|
|
Age |
18 - 24 years old |
29 |
7.5 |
|
25 - 34 years old |
107 |
27.8 |
|
|
35 - 44 years old |
212 |
55.1 |
|
|
45 - 54 years old |
37 |
9.6 |
|
|
Residence region |
Asia |
254 |
66 |
|
Europe |
104 |
27 |
|
|
North America |
16 |
4.1 |
|
|
South America |
11 |
2.9 |
|
|
Education level |
High school or
equivalent |
3 |
0.8 |
|
Bachelor’s degree |
58 |
15.1 |
|
|
Master’s degree |
292 |
75.8 |
|
|
Doctoral degree or
higher |
32 |
8.3 |
|
|
Occupation |
Student |
15 |
3.9 |
|
Employed part-time |
9 |
2.3 |
|
|
Employed full-time |
261 |
67.8 |
|
|
Self-employed |
100 |
26 |
|
|
Annual Income |
Less than $20,000 |
8 |
2.1 |
|
$20,000 - $39,999 |
19 |
4.9 |
|
|
$40,000 - $59,999 |
202 |
52.5 |
|
|
$60,000 - $79,999 |
134 |
34.8 |
|
|
$80,000 and above |
22 |
5.7 |
|
|
Travel frequency |
1-2 times per year |
337 |
87.5 |
|
3-5 times per year |
44 |
11.4 |
|
|
More than 5 times |
4 |
1.1 |
|
|
Source: Constructed by the author |
4.2 Descriptive
Statistics of Study Variables
Descriptive
statistics for the study constructs are shown in Table 4. All variables
achieved mean scores above 4.40 on a five-point scale, suggesting generally
positive perceptions among respondents. Price (M = 4.55, SD = 0.54) and website
quality (M = 4.53, SD = 0.51) received particularly favorable ratings, while
brand awareness, although slightly lower (M = 4.40, SD = 0.53), still reflected
a positive perception of luxury hotel brands. Booking intention achieved the
highest overall mean (M = 4.60, SD = 0.52), highlighting respondents’ strong
willingness to consider five-star hotels in Thailand for future stays.
Table 4. Descriptive statistics of constructs (N
= 385)
|
Variable |
Mean |
Std. Deviation |
|
Online reviews |
4.48 |
0.53 |
|
Price |
4.55 |
0.54 |
|
Trust |
4.50 |
0.54 |
|
Brand awareness |
4.40 |
0.53 |
|
Website quality |
4.53 |
0.51 |
|
Booking intention |
4.60 |
0.52 |
Source: Constructed by the
author.
4.3 Hypothesis
Testing Results
Multiple linear
regression was employed to test the proposed hypotheses. Multicollinearity was
not a concern, as the Variance Inflation Factor (VIF) values were all below 5.
The regression model
was statistically significant, explaining 46.4% of the variance in booking
intention (R² = 0.471, adjusted R² = 0.464). As shown in Table 5, price (β
= 0.582, p < 0.001) exerted the strongest positive effect, followed by trust
(β = 0.261, p < 0.001) and brand awareness (β = 0.176, p <
0.001). Interestingly, online reviews (β = –0.315, p < 0.001) and
website quality (β = –0.108, p = 0.049) demonstrated significant negative
effects on booking intention.
Table 5. Multiple linear regression results
|
Hypothesis |
B |
SE B |
β |
t |
Sig. |
VIF |
Decision
Ho |
|
H1: OR → BI |
-0.303 |
0.052 |
-0.315 |
-5.85 |
0.001* |
2.07 |
Rejected |
|
H2: P → BI |
0.575 |
0.040 |
0.582 |
14.26 |
0.001* |
1.19 |
Rejected |
|
H3: T → BI |
0.280 |
0.058 |
0.261 |
4.84 |
2.09 |
Rejected |
|
|
H4: BA → BI |
0.176 |
0.045 |
0.176 |
3.94 |
0.001* |
1.43 |
Rejected |
|
H5: WQ → BI |
-0.133 |
0.067 |
-0.108 |
-1.97 |
0.049 |
2.14 |
Rejected |
|
R² |
0.471 |
||||||
|
Adjusted R² |
0.464 |
||||||
Source: Constructed by the
author.
5. DISCUSSION AND
CONCLUSION
5.1 Discussion of Key
Findings
This study
investigated the determinants of international travelers’ intention to book
5-star hotels in Thailand via online platforms, focusing on online reviews,
price, trust, brand awareness, and website quality. The regression analysis
confirmed that all five variables significantly affected booking intention,
though their directions and magnitudes varied.
Price emerged as the
strongest positive predictor (β = 0.582, p < 0.001), suggesting that
even in the luxury segment, travelers remain value-conscious. This aligns with
prior research highlighting the critical role of perceived price fairness in
online booking decisions (Haddad et al., 2015). Trust (β = 0.261, p <
0.001) and brand awareness (β = 0.176, p < 0.001) also enhanced booking
intention, reinforcing earlier findings that emphasize the importance of
credibility and brand familiarity in high-cost, high-involvement purchases
(Gefen et al., 2003; Keller and Swaminathan, 2020).
Unexpectedly, online
reviews (β = -0.315, p < 0.001) and website quality (β = -0.108, p
= 0.049) negatively influenced booking intention. A plausible explanation is
that the study did not distinguish between official hotel websites and online
travel agencies (OTAs). Variation in consumer experiences across these
platforms may have led to divergent perceptions, generating the observed
negative effects. This outcome resonates with the notion that online consumer
behavior is highly context-dependent, particularly in the hospitality industry
(Sparks and Browning, 2011; Fang et al., 2014).
Collectively, the
findings highlight that while traditional factors such as price, trust, and
brand awareness remain positive drivers, digital touchpoints such as online
reviews and website quality require careful management to avoid unintended
negative effects.
5.2 Theoretical
Contributions
This study
contributes to the literature on online consumer behavior and luxury
hospitality by integrating the Theory of Planned Behavior (Ajzen, 1991) and the
Stimulus-Organism-Response model (Mehrabian and Russell, 1974) to explain
booking intentions. The positive effects of price, trust, and brand awareness
confirm existing theoretical assumptions, while the negative effects of online
reviews and website quality extend prior research by suggesting that these
factors may not always operate as expected in luxury contexts. These findings
underscore the need for more nuanced models that incorporate platform-specific
and situational factors in explaining consumer booking behavior.
5.3 Practical
Implications
The results provide
actionable guidance for hotel managers and online booking platforms. Negative
influences should be mitigated through the management of online reviews, for
instance, encouraging authentic and balanced feedback, categorizing reviews for
clarity, and responding promptly and professionally to negative comments.
Website quality can be improved by simplifying navigation, ensuring
cross-device compatibility, and integrating real-time support tools.
At the same time,
hotels should leverage positive drivers. Transparent pricing strategies,
avoidance of hidden charges, and clear communication of value can reinforce
booking confidence. Trust-building measures such as secure payment systems,
verified reviews, and consistency between online information and actual service
delivery are also essential. Furthermore, strengthening brand awareness through
global marketing campaigns and loyalty programs can increase consumer
recognition and confidence, thereby enhancing booking intentions.
5.4 Limitations and
Future Research Directions
Several limitations
must be acknowledged. First, the study was limited to 5-star hotels in Thailand,
which may restrict generalizability to other destinations or hotel categories.
Second, the absence of platform-specific analysis may explain the negative
findings for online reviews and website quality, suggesting a need for future
studies to examine OTA-specific versus official website dynamics. Third,
emerging technologies such as AI-driven personalization, chatbots, and
real-time pricing algorithms were not incorporated and warrant further
exploration. Fourth, this study focused on booking intention only, excluding
post-purchase behaviors such as satisfaction, loyalty, or electronic
word-of-mouth. Finally, future studies should conduct cross-cultural
comparisons to validate the robustness of the findings across diverse consumer
markets.
5.5 Conclusion
This study
demonstrates that price, trust, and brand awareness are significant positive
determinants of online booking intention for luxury hotels, while online
reviews and website quality exerted counterintuitive negative effects. These
findings emphasize the complexity of digital consumer behavior in luxury
hospitality and highlight the importance of strategic pricing, brand
management, and digital platform optimization.
By addressing both
positive and negative determinants, hotel operators can strengthen their online
competitiveness and enhance consumer engagement. The study thus contributes
both theoretically by extending existing behavioral models and practically by
offering evidence-based recommendations for managing digital booking environments
in the luxury hospitality sector.
REFERENCES
1. Ajzen, I. (1991). The
theory of planned behavior. Organizational Behavior and Human Decision
Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
2. Ali, F. (2016). Hotel
website quality, perceived flow, customer satisfaction and purchase
intention. Journal of Hospitality and Tourism Technology, 7(2),
213–228. https://doi.org/10.1108/jhtt-02-2016-0010
3. Assaker, G. (2020). Age and
gender differences in online travel reviews and user-generated-content (UGC)
adoption: Extending the technology acceptance model (TAM) with credibility
theory. Journal of Hospitality Marketing & Management, 29(4),
428–449. https://doi.org/10.1080/19368623.2019.1653807
4. Bahari, K. A., Abdullah,
D., Kamal, S. B. M., Johari, N. R., & Zulkafli, M. S. (2018). The influence
of hotel website design quality, perceived ease of use, and perceived
usefulness on loyalty intention. The Turkish Online Journal of Design
Art and Communication, 8(September), 701–710. https://doi.org/10.7456/1080SSE/102
5. Fang, Y., Qureshi, I., Sun,
H., McCole, P., Ramsey, E., & Lim, K. H. (2014). Trust, satisfaction, and
online repurchase intention. MIS Quarterly, 38(2), 407-428. https://doi.org/10.25300/MISQ/2014/38.2.04
6. Filieri, R., & McLeay,
F. (2014). E-WOM and accommodation: An analysis of the factors that influence
travelers’ adoption of information from online reviews. Journal of
Travel Research, 53(1), 44–57. https://doi.org/10.1177/0047287513481274
7. Gefen, D., Karahanna, E.,
& Straub, D. W. (2003). Trust and TAM in online shopping: An integrated
model. MIS Quarterly, 27(1), 51–90. https://doi.org/10.2307/30036519
8. Haddad, R., Hallak, R.,
& Assaker, G. (2015). Price fairness perceptions and hotel customers’
behavioral intentions. Journal of Travel Research, 55(7),
998–1010. https://doi.org/10.1177/1356766715573651
9. Han, H., & Hyun, S. S.
(2017). Impact of hotel-restaurant image and quality of physical-environment,
service, and food on satisfaction and intention. International Journal
of Hospitality Management, 63, 82–92. https://doi.org/10.1016/j.ijhm.2017.03.006
10. Han, H., & Kim, Y.
(2010). An investigation of green hotel customers’ decision formation:
Developing an extended model of the theory of planned behavior. International
Journal of Hospitality Management, 29(4), 659–668. https://doi.org/10.1016/j.ijhm.2010.01.001
11. Huang, Y.-C., Backman, S.
J., Backman, K. F., & Moore, D. (2017). Exploring user acceptance of 3D
virtual worlds in travel and tourism marketing. Tourism Management, 36,
490–501. https://doi.org/10.1016/j.tourman.2012.09.009
12. Hudson, S., & Thal, K.
(2013). The impact of social media on the consumer decision process:
Implications for tourism marketing. Journal of Travel & Tourism
Marketing, 30(1–2), 156–160. https://doi.org/10.1080/10548408.2013.751276
13. JLL. (2025). Bangkok
hotel market dynamics Q4 2024 [Research report]. https://www.jll.co.th/en/trends-and-insights/research/bangkok-hotel-market-dynamics
14. Kandampully, J., & Hu,
H. H. (2007). Do hoteliers need to manage image to retain loyal
customers? International Journal of Contemporary Hospitality
Management, 19(6), 435–443. https://doi.org/10.1108/09596110710775101
15. Keller, K. L., &
Swaminathan, V. (2020). Strategic brand management: Building,
measuring, and managing brand equity (5th ed.). Pearson Education.
16. Kim, D. J., Ferrin, D. L.,
& Rao, H. R. (2008). A trust-based consumer decision-making model in
electronic commerce: The role of perceived risk and uncertainty. Decision
Support Systems, 44(2), 544–564. https://doi.org/10.1016/j.dss.2007.07.001
17. Kim, M. J., Chung, N.,
& Lee, C. K. (2011). The effect of perceived trust on electronic commerce:
Shopping online for tourism products and services in South Korea. Tourism
Management, 32(2), 256–265. https://doi.org/10.1016/j.tourman.2010.01.011
18. Kim, S. Y., Kim, J. U.,
& Park, S. C. (2017). The effects of perceived value, website trust and
hotel trust on online hotel booking intention. Sustainability, 9(12),
2262. https://doi.org/10.3390/su9122262
19. Kim, W. G., Tang, C. H.,
& Roehl, W. S. (2018). The effect of hotel’s dual-branding on
willingness-to-pay and booking intention: A luxury/upper-upscale
combination. Journal of Revenue and Pricing Management, 17(3),
256–275. https://doi.org/10.1057/s41272-017-0107-z
20. LH Bank. (2024). 5-star
hotel business analysis [Special report]. Land and Houses Bank Public
Company Limited. https://www.lhbank.co.th/getattachment/f1b40250-9d0f-4003-97ae-d78ba6d74abf/economic-analysis-Industry-Outlook-2025-5-star_Hotel_Business
21. Mehrabian, A., &
Russell, J. A. (1974). An approach to environmental psychology. MIT
Press.
22. Noone, B. M., &
Mattila, A. S. (2009). Hotel revenue management and the Internet: The effect of
price presentation strategies on customers’ willingness to book. International
Journal of Hospitality Management, 28(2), 272–279. https://doi.org/10.1016/j.ijhm.2008.09.004
23. Roziqin, A., Kurniawan, A.
S., Hijri, Y. S., & Kismartini. (2023). Research trends of digital tourism:
A bibliometric analysis. Tourism Critiques: Practice and Theory, 4(1/2),
28–47. https://doi.org/10.1108/TRC-11-2022-0028
24. Sparks, B. A., &
Browning, V. (2011). The impact of online reviews on hotel booking intentions
and perception of trust. Tourism Management, 32(6),
1310–1323. https://doi.org/10.1016/j.tourman.2010.12.011
25. Wang, D., Xiang, Z., &
Fesenmaier, D. R. (2016). Smartphone use in everyday life and travel. Journal
of Travel Research, 55(1), 52–63. https://doi.org/10.1177/0047287514535847
26. Wang, Y., Law, R., &
Fesenmaier, D. R. (2015). Impact of hotel website quality on online booking
intentions: ETrust as a mediator. International Journal of Hospitality
Management, 47, 108–115. https://doi.org/10.1016/j.ijhm.2015.03.012
27. Xiang, Z., Du, Q., Ma, Y.,
& Fan, W. (2017). A comparative analysis of major online review platforms:
Implications for social media analytics in hospitality and tourism. Tourism
Management, 58, 51–65. https://doi.org/10.1016/j.tourman.2016.10.001
28. Ye, Q., Law, R., & Gu,
B. (2009). The impact of online user reviews on hotel room sales. International
Journal of Hospitality Management, 28(1), 180–182. https://doi.org/10.1016/j.ijhm.2008.06.011
29. Yoon, Y., & Uysal, M.
(2005). An examination of the effects of motivation and satisfaction on
destination loyalty: A structural model. Tourism Management, 26(1),
45–56. https://doi.org/10.1016/j.tourman.2003.08.016
30. Zhong, L., Zhu, M., SuniD,
S., & Law, R. (2021). Research progress and development of technology in
tourism research: a bibliometric analysis. Journal of Smart Tourism, 1(2),
3–12. https://doi.org/10.52255/smarttourism.2021.1.2.2