Social media and online reviews have had a tremendous impact on the hotel accommodation industry. Online reviews are a highly appreciated source of information for travellers making decisions about booking a hotel room through online travel agencies (OTA). This encourages researchers to investigate the direct and serial mediation effects of review ratings (RR) and cewebrity reviews (CR) on the relationship between booking website quality (BWQ) and online booking intentions (OBI). The stimulus-organism-response (S-O-R) framework was used to validate the hypotheses. Data were obtained from 665 users who experienced booking through OTA in India’s cities. SPSS and partial least squares structural equation modelling (PLS-SEM) were used to analyze the data. Researchers have used importance-performance map analysis (IPMA) to map the attributes and priorities of OBI. These findings suggest that BWQ positively affects OBI. Serial mediation of RR and CR in the relationship between BWQ and OBI was statistically significant. The findings revealed that when guests became more concerned about reviews, the relationship between BWQ and OBI became stronger. This research contributes to online reviews in the field of hospitality services as well as to stakeholders involved in the hotel booking process.
AlbarqA. N. (2021). Effect of web atmospherics and satisfaction on purchase behavior: Stimulus–organism–response model. Future Business Journal, 7(1). 10.1186/s43093-021-00107-3
2.
AgagG., & El-MasryA. A. (2016). Understanding the determinants of hotel booking intentions and moderating role of habit. International Journal of Hospitality Management, 54, 52–67. 10.1016/j.ijhm.2016.01.007
3.
AliF. (2016). Hotel website quality, perceived flow, customer satisfaction and purchase intention. Journal of Hospitality and Tourism Technology, 7(2), 213–228. 10.1108/JHTT-02-2016-0010
4.
AliF., AliL., GaoZ., TerrahA., & TurktarhanG. (2022). Determinants of user’s intentions to book hotels: A comparison of websites and mobile apps. Aslib Journal of Information Management. 10.1108/AJIM-05-2022-0239
5.
AliF., RasoolimaneshS. M., SarstedtM., RingleC. M., & RyuK. (2018). An assessment of the use of partial least squares structural equation modeling (PLS-SEM) in hospitality research. International Journal of Contemporary Hospitality Management, 30(1), 514–538. 10.1108/ijchm-10-2016-0568
6.
AliM. A., TingD. H., Ahmad-ur-RahmanM., AliS., ShearF., & MazharM. (2021). Effect of online reviews and crowd cues on restaurant choice of customer: Moderating role of gender and perceived crowding. Frontiers in Psychology, 12. 10.3389/fpsyg.2021.780863
7.
AminM., RyuK., CobanogluC., & NizamA. (2021). Determinants of online hotel booking intentions: Website quality, social presence, affective commitment, and E-trust. Journal of Hospitality Marketing & Management, 30(7), 845–870. 10.1080/19368623.2021.1899095
8.
Anubha, & ShomeS. (2020). Intentions to use travel eWOM: Mediating role of Indian urban millennials’ attitude. International Journal of Tourism Cities, 7(3), 640–661. 10.1108/ijtc-04-2020-0073
9.
Asma, & AfreenA. (2022). The antecedents of Instagram store purchase intention: Exploring the role of trust in social commerce. Vision: The Journal of Business Perspective, 097226292211338. 10.1177/09722629221133887
10.
Belver-DelgadoT., San-MartínS., & Hernández-MaestroR. M. (2020). The influence of website quality and star rating signals on booking intention: Analyzing the moderating effect of variety seeking. Spanish Journal of Marketing - ESIC, 25(1), 3–28. 10.1108/sjme-09-2019-0076
11.
CasalóL. V., FlaviánC., GuinalíuM., & EkinciY. (2015). Do online hotel rating schemes influence booking behaviors?International Journal of Hospitality Management, 49, 28–36. 10.1016/j.ijhm.2015.05.005
12.
ChakrabortyU. (2019). Perceived credibility of online hotel reviews and its impact on hotel booking intentions. International Journal of Contemporary Hospitality Management, 31(9), 3465–3483. 10.1108/ijchm-11-2018-0928
13.
ChangY., HsuP., & LanY. (2019). Cooperation and competition between online travel agencies and hotels. Tourism Management, 71, 187–196. 10.1016/j.tourman.2018.08.026
14.
ChengY., & HoH. (2015). Social influence’s impact on reader perceptions of online reviews. Journal of Business Research, 68(4), 883–887. 10.1016/j.jbusres.2014.11.046
15.
ChoiK., WangY., & SparksB. A. (2022). The role of value and trust in shaping recommendation intentions: Evidence from travel app users. Tourism Analysis, 27(3), 395–401. 10.3727/108354221x16276940562105
16.
ChongA. Y., KhongK. W., MaT., McCabeS., & WangY. (2018). Analyzing key influences of tourists’ acceptance of online reviews in travel decisions. Internet Research, 28(3), 564–586. 10.1108/intr-05-2017-0212
17.
CohenJ. (2013). Statistical power analysis for the behavioral sciences.
18.
Cuic TankovicA., KapešJ., & BenazićD. (2022). Communication skills in Generation Z as future tourism employees. Communication Research and Practice, 1–17. 10.1080/22041451.2021.2017136
19.
DjafarovaE., & RushworthC. (2017). Exploring the credibility of online celebrities’ Instagram profiles in influencing the purchase decisions of young female users. Computers in Human Behavior, 68, 1–7. 10.1016/j.chb.2016.11.009
20.
DograP., & KaushalA. (2023). Investigating factors affecting trust and purchase intention towards online websites: Structural equation modelling approach. International Journal of Internet Marketing and Advertising, 18(1), 98. 10.1504/ijima.2023.10053069
21.
DonthuN., KumarS., PandeyN., PandeyN., & MishraA. (2021). Mapping the electronic word-of-mouth (eWOM) research: A systematic review and bibliometric analysis. Journal of Business Research, 135(July), 758–773. 10.1016/j.jbusres.2021.07.015
22.
DuttaK., SharmaK., & GoyalT. (2021). Customer’s digital advocacy: The impact of reviews and influencers in building trust for tourism and hospitality services. Worldwide Hospitality and Tourism Themes, 13(2), 260–274. 10.1108/whatt-09-2020-0123
23.
DwidienawatiD., TjahjanaD., AbdinagoroS. B., GandasariD., & Munawaroh. (2020). Customer review or influencer endorsement: Which one influences purchase intention more?Heliyon, 6(11), e05543. 10.1016/j.heliyon.2020.e05543
24.
El-SaidO. A. (2020). Impact of online reviews on hotel booking intention: The moderating role of brand image, star category, and price. Tourism Management Perspectives, 33, 100604. 10.1016/j.tmp.2019.100604
25.
FalkR. F., & MillerN. B. (1992). A primer for soft modeling. University of Akron Press.
26.
FangB., YeQ., KucukustaD., & LawR. (2016). Analysis of the perceived value of online tourism reviews: Influence of readability and reviewer characteristics. Tourism Management, 52, 498–506. 10.1016/j.tourman.2015.07.018
27.
FeinsteinE. (2018). OTA’s vs. direct hotel bookings: Which is the leading trend for 2018? Retrieved from https://www.traveldailynews.com/post/otas-vs-direct-hotel-bookings-which-is-the-leading-trend-for-2018
28.
FitrianiW. R., MulyonoA. B., HidayantoA. N., & MunajatQ. (2020). Reviewer’s communication style in YouTube product-review videos: Does it affect channel loyalty?Heliyon, 6(9), e04880. 10.1016/j.heliyon.2020.e04880
29.
GavilanD., AvelloM., & Martinez-NavarroG. (2018). The influence of online ratings and reviews on hotel booking consideration. Tourism Management, 66, 53–61. 10.1016/j.tourman.2017.10.018
30.
GefenD., StraubD., & BoudreauM. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the Association for Information Systems, 4. 10.17705/1cais.00407
31.
GohK., HengC., & LinZ. (2013). Social media brand community and consumer behavior: Quantifying the relative impact of user- and marketer-generated content. Information Systems Research, 24(1), 88–107. 10.1287/isre.1120.0469
32.
GulerY. C., & HuseynovF. (2021). The impact of online consumer reviews on online hotel booking intention. Journal of Business Research - Turk, 13(3), 2634–2652. 10.20491/isarder.2021.1282
33.
HairJ. F., RingleC. M., & SarstedtM. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152. 10.2753/mtp1069-6679190202
34.
Hair JrJ. F., HultG. T., RingleC., & SarstedtM. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications.
35.
Hair JrJ. F.SarstedtM., HopkinsL., & G. KuppelwieserV. (2014). Partial least squares structural equation modeling (PLS-SEM). European Business Review, 26(2), 106–121. 10.1108/ebr-10-2013-0128
36.
HayesA. F. (2017). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (2nd ed.). Guilford Publications.
37.
HenselerJ. (2021). Composite-based structural equation modeling: Analyzing latent and emergent variables. The Guilford Press.
38.
HenselerJ., HubonaG., & RayP. A. (2017). Partial least squares path modeling: Updated guidelines. Partial Least Squares Path Modeling, 19–39. 10.1007/978-3-319-64069-3_2
39.
HenselerJ., RingleC. M., & SarstedtM. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. 10.1007/s11747-014-0403-8
40.
HermawanD. (2022). The effects of web quality, perceived benefits, security and data privacy on behavioral intention and e-WOM of online travel agencies. International Journal of Data and Network Science, 6(3), 1005–1012. 10.5267/j.ijdns.2022.1.011
41.
HsuC. L., ChangK. C., & ChenM. C. (2012). The impact of website quality on customer satisfaction and purchase intention: Perceived playfulness and perceived flow as mediators. Information Systems and E-Business Management, 10(4), 549–570. 10.1007/s10257-011-0181-5
42.
IsmagilovaE., SladeE. L., RanaN. P., & DwivediY. K. (2019). The effect of electronic word of mouth communications on intention to buy: A meta-analysis. Information Systems Frontiers, 22(5), 1203–1226. 10.1007/s10796-019-09924-y
43.
JeongM., OhH., & GregoireM. (2003). Conceptualizing Web site quality and its consequences in the lodging industry. International Journal of Hospitality Management, 22(2), 161–175. 10.1016/S0278-4319(03)00016-1
44.
JohansonG. A., & BrooksG. P. (2009). Initial scale development: Sample size for pilot studies. Educational and Psychological Measurement, 70(3), 394–400. 10.1177/0013164409355692
45.
KareemS. A., & VenugopalP. (2023). Examining the relationship between gastronomic experience, revisit intention, destination image and destination brand love: A moderating role of cewebrity reviews. Leisure/Loisir, 1–27. 10.1080/14927713.2023.2211582
46.
KanaveeduA., & KalapurackalJ. J. (2022). Influencer marketing and consumer behaviour: A systematic literature review. Vision: The Journal of Business Perspective, 097226292211146. 10.1177/09722629221114607
47.
KangH. (2021). Sample size determination and power analysis using the G* Power software. Journal of Educational Evaluation for Health Professions, 18. 10.3352/jeehp.2021.18.17
48.
KimW. G., MaX., & KimD. J. (2006). Determinants of Chinese hotel customers’ E-satisfaction and purchase intentions. Tourism Management, 27(5), 890–900. 10.1016/j.tourman.2005.05.010
49.
KitsiosF., MitsopoulouE., MoustakaE., & KamariotouM. (2022). User-Generated Content behavior and digital tourism services: A SEM-neural network model for information trust in social networking sites. International Journal of Information Management Data Insights, 2(1), 100056. 10.1016/j.jjimei.2021.100056
50.
KlineR. B. (2015). Principles and practice of structural equation modeling (4th ed.).
51.
KockN. (2015). Common method bias in PLS-SEM. International Journal of e-Collaboration, 11(4), 1–10. 10.4018/ijec.2015100101
52.
KockN., & HadayaP. (2018). Minimum sample size estimation in PLS-SEM: The inverse square root and gamma-exponential methods. Information Systems Journal, 28(1), 227–261. 10.1111/isj.12131
53.
KockF., BerbekovaA., & AssafA. G. (2021). Understanding and managing the threat of common method bias: Detection, prevention and control. Tourism Management, 86, 104330. 10.1016/j.tourman.2021.104330
54.
LeH. T., & RyuS. (2023). The eWOM adoption model in the hospitality industry: The moderating effect of the vlogger’s review. Journal of Hospitality and Tourism Technology, 14(2), 225–244. 10.1108/jhtt-08-2021-0233
55.
LeeJ., ParkD., & HanI. (2008). The effect of negative online consumer reviews on product attitude: An information processing view. Electronic Commerce Research and Applications, 7(3), 341–352. 10.1016/j.elerap.2007.05.004
56.
LiL., PengM., JiangN., & LawR. (2017). An empirical study on the influence of economy hotel website quality on online booking intentions. International Journal of Hospitality Management, 63, 1–10. 10.1016/j.ijhm.2017.01.001
57.
LoA. S., & YaoS. S. (2019). What makes hotel online reviews credible?International Journal of Contemporary Hospitality Management, 31(1), 41–60. 10.1108/ijchm10-2017-0671
58.
Marine-RoigE., DariesN., Martin-FuentesE., & Ferrer-RosellB. (2023). Where you sleep tells what you care about. Information and Communication Technologies in Tourism 2023, 166–171. 10.1007/978-3-031-25752-0_19
59.
Martin-FuentesE., MellinasJ. P., & Parra-LopezE. (2020). Online travel review rating scales and effects on hotel scoring and competitiveness. Tourism Review, 76(3), 654–668. 10.1108/tr-01-2019-0024
60.
MehrabianA., & RussellJ. A. (1974). An approach to environmental psychology. MIT Press.
61.
MemonM. A., TingH., CheahJ., ThurasamyR., ChuahF., & ChamT. H. (2020). Sample size for survey research: Review and recommendations. Journal of Applied Structural Equation Modeling, 4(2), i–xx. 10.47263/jasem.4(2)01
62.
MoraisL. S. (2022). The impact of influencers on restaurants reviews: A Tripadvisor case (Doctoral dissertation).
63.
Morales-VargasA., Pedraza-JimenezR., & CodinaL. (2023). Website quality evaluation: A model for developing comprehensive assessment instruments based on key quality factors. Journal of Documentation. 10.1108/jd-11-2022-0246
64.
NaujoksA., & BenkensteinM. (2020). Who is behind the message? The power of expert reviews on eWOM platforms. Electronic Commerce Research and Applications, 44, 101015. 10.1016/j.elerap.2020.101015
65.
Netscribes (India) Pvt. Ltd. (2019, April29). Hotel industry in India (2018–2023) overview, revenue, growth forecast, trends, drivers, challenges, and data on tourist arrival, distribution channels, room tariff, and key, players (The Lalit, Hyatt hotels and resorts, ITC hotels, lemon tree hotels limit. https://www.marketresearch.com/Netscribes-India-Pvt-Ltd-v3676/Hotel-India-12379982/
66.
NirajR., & SinghJ. (2015). Impact of user-generated and professional critics reviews on Bollywood movie success. Australasian Marketing Journal, 23(3), 179–187. 10.1016/j.ausmj.2015.02.001
67.
NunkooR., TeeroovengadumV., RingleC. M., & SunnasseeV. (2020). Service quality and customer satisfaction: The moderating effects of hotel star rating. International Journal of Hospitality Management, 91, 102414. 10.1016/j.ijhm.2019.102414
68.
PeranoM., CasaliG. L., LiuY., & AbbateT. (2021). Professional reviews as service: A mix method approach to assess the value of recommender systems in the entertainment industry. Technological Forecasting and Social Change, 169, 120800. 10.1016/j.techfore.2021.120800
69.
PreacherK. J., & HayesA. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891. 10.3758/brm.40.3.879
70.
PuthM., NeuhäuserM., & RuxtonG. D. (2015). On the variety of methods for calculating confidence intervals by bootstrapping. Journal of Animal Ecology, 84(4), 892–897. 10.1111/1365-2656.12382
71.
QalatiS. A., VelaE. G., LiW., DakhanS. A., Hong ThuyT. T., & MeraniS. H. (2021). Effects of perceived service quality, website quality, and reputation on purchase intention: The mediating and moderating roles of trust and perceived risk in online shopping. Cogent Business & Management, 8(1). 10.1080/23311975.2020.1869363
72.
RamayahT. J. F. H., CheahJ., ChuahF., TingH., & MemonM. A. (2018). Partial least squares structural equation modeling (PLS-SEM) using smartPLS 3.0. An updated guide and practical guide to statistical analysis. Pearson
73.
Reza JalilvandM., & SamieiN. (2012). The effect of electronic word of mouth on brand image and purchase intention. Marketing Intelligence & Planning, 30(4), 460–476. 10.1108/02634501211231946
74.
RingleC. M., & SarstedtM. (2016). Gain more insight from your PLS-SEM results. Industrial Management & Data Systems, 116(9), 1865–1886. 10.1108/imds-10-2015-0449
75.
Ruiz-MafeC., Bigné-AlcañizE., & Currás-PérezR. (2020). The effect of emotions, eWOM quality and online review sequence on consumer intention to follow advice obtained from digital services. Journal of Service Management, 31(3), 465–487. 10.1108/josm-11-2018-0349
76.
SalemS. F. (2021). Do relationship marketing constructs enhance consumer retention? An empirical study within the hotel industry. Sage Open, 11(2), 215824402110092. 10.1177/21582440211009224
77.
SarkarA. R., & AhmadS. (2021). A new approach to expert reviewer detection and product rating derivation from online experiential product reviews. Heliyon, 7(7), e07409. 10.1016/j.heliyon.2021.e07409
78.
SarstedtM., & CheahJ.-H. (2019). Partial least squares structural equation modeling using SmartPLS: A software review. Journal of Marketing Analytics. 10.1057/s41270-019-00058-3
79.
ShaputraR. I., FitrianiW. R., HidayantoA. N., KumaralalitaL., & PurwandariB. (2023). How media richness and interactivity in hotel visualization affect hotel booking intention in online travel agency applications?Human Behavior and Emerging Technologies, 2023, 1–11. 10.1155/2023/5087488
80.
SharmaM., AntonyR., SehrawatR., CruzA. C., & DaimT. U. (2022). Exploring post-adoption behaviors of e-service users: Evidence from the hospitality sector /online travel services. Technology in Society, 68(July2021), 101781. 10.1016/j.techsoc.2021.101781
81.
ShmueliG., SarstedtM., HairJ. F., CheahJ. H., TingH., VaithilingamS., & RingleC. M. (2019). Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322–2347. 10.1108/EJM-02-2019-0189
82.
ShuklaA., & MishraA. (2021). Effects of visual information and argument concreteness on purchase intention of consumers towards online hotel booking. Vision: The Journal of Business Perspective, 097226292110380. 10.1177/09722629211038069
83.
SulistyoL. I. H., & PranataW. (2020). The effect of website quality on e-Trust of customers: ‘The case of ZEN rooms’. Pertanika Journal of Social Sciences and Humanities, 28, 301–310.
84.
SuparnoC. (2020). Online purchase intention of halal cosmetics: S-O-R framework application. Journal of Islamic Marketing, 12(9), 1665–1681. 10.1108/jima-09-2019-0192
85.
TalwarS., DhirA., KaurP., & MäntymäkiM. (2020). Why do people purchase from online travel agencies (OTAs)? A consumption values perspective. International Journal of Hospitality Management, 88, 102534. 10.1016/j.ijhm.2020.102534
86.
Tamil Nadu Tourism. (2021). Star Hotels in Tamil Nadu. Retrieved from https://tamilnadutourism.org/places/starhotels.html
87.
TranL. T. (2020). Online reviews and purchase intention: A cosmopolitanism perspective. Tourism Management Perspectives, 35, 100722. 10.1016/j.tmp.2020.100722
88.
VelosoM., & Gomez-SuarezM. (2023). The influential role of hotel-generated content on social media. Journal of Hospitality and Tourism Technology, 14(2), 245–257. 10.1108/jhtt-08-2021-0241
89.
WangL., & LawR. (2020). Relationship between hotels’ website quality and consumers’ booking intentions with internet experience as moderator. Journal of China Tourism Research, 16(4), 585–605. 10.1080/19388160.2019.1699484
90.
WangL., LawR., GuilletB. D., HungK., & FongD. K. C. (2015). Impact of hotel website quality on online booking intentions: ETrust as a mediator. International Journal of Hospitality Management, 47, 108–115. 10.1016/j.ijhm.2015.03.012
91.
WongJ., & LawR. (2005). Analysing the intention to purchase on hotel websites: A study of travellers to Hong Kong. International Journal of Hospitality Management, 24(3), 311–329. 10.1016/j.ijhm.2004.08.002
92.
WuJ., & LawR. (2019). Analysing behavioural differences between e- and M-bookers in hotel booking. International Journal of Hospitality Management, 83, 247–256. 10.1016/j.ijhm.2018.10.023
93.
YangJ. (2015). The effects of lighting temperature and complexity on hotel guests’ perceived servicescape, perceived value, and behavioral intentions. 10.31274/etd-180810-4247
94.
YeQ., LawR., GuB., & ChenW. (2011). The influence of user-generated content on traveler behavior: An empirical investigation on the effects of E-word-of-mouth to hotel online bookings. Computers in Human Behavior, 27(2), 634–639. 10.1016/j.chb.2010.04.014
95.
ZengG., CaoX., LinZ., & XiaoS. H. (2020). When online reviews meet virtual reality: Effects on consumer hotel booking. Annals of Tourism Research, 81, 102860. 10.1016/j.annals.2020.102860
96.
ZhangH., LiangX., & MoonH. (2020). Fashion cewebrity involvement in new product development: Scale development and an empirical study. Journal of Business Research, 120, 321–329. 10.1016/j.jbusres.2020.01.052
97.
ZhangY., MoeW. W., & SchweidelD. A. (2017). Modeling the role of message content and influencers in social media rebroadcasting. International Journal of Research in Marketing, 34(1), 100–119. 10.1016/j.ijresmar.2016.07.003
98.
ZhangZ., ZhangZ., & YangY. (2016). The power of expert identity: How website-recognized expert reviews influence travelers’ online rating behavior. Tourism Management, 55, 15–24. 10.1016/j.tourman.2016.01.004
99.
ZhouW., & DuanW. (2016). Do professional reviews affect online user choices through user reviews? An empirical study. Journal of Management Information Systems, 33(1), 202–228. 10.1080/07421222.2016.1172460
100.
ZhuL., LiH., WangF., HeW., & TianZ. (2020). How online reviews affect purchase intention: A new model based on the stimulus-organism-response (S-O-R) framework. Aslib Journal of Information Management, 72(4), 463–488. 10.1108/ajim-11-2019-0308
