Sage Journals: Discover world-class research

Abstract

As a technological breakthrough, large language models stand to revolutionize operations in many industries, including the tourism sector. Despite the transformative potential of artificial intelligence (AI)-generated content (AIGC), its role in marketing performance remains unclear. This research used a prediction–interpretation–inference machine learning framework to evaluate how AIGC affects Airbnb listings’ performance. Specifically, we scrutinized AIGC’s efficacy under two conditions (i.e., with an AIGC tool serving as a language assistant vs. a content creator) and examined the impacts of prompt design strategies. Three key findings emerged. First, the prompt design strategy greatly influenced AIGC’s effectiveness. Second, AIGC did not uniformly enhance listing performance: although it boosted revenue and bookings for underperforming listings, its impact on low-performing listings was limited and not as strong as anticipated. AIGC may even negatively influence high-performing listings. Third, the AIGC tool appeared more effective as a language assistant (vs. a content creator) for underperforming listings.

Keywords

artificial intelligence generative AI AIGC prompt engineering GPT-4o peer-to-peer accommodation market

Get full access to this article

View all access options for this article.

References

Acheampong

F. A.

Nunoo-Mensah

Chen

(2021). Transformer models for text-based emotion detection: A review of BERT-based approaches. Artificial Intelligence Review, 54(8), 5789–5829. https://doi.org/10.1007/s10462-021-09958-2

Aira & Verblio. (2022). The future of AI in content marketing. https://aira.net/future-of-ai-content-marketing/

Apley

D. W.

Zhu

(2020). Visualizing the effects of predictor variables in black box supervised learning models. Journal of the Royal Statistical Society Series B: Statistical Methodology, 82(4), 1059–1086. https://doi.org/10.1111/rssb.12377

Boulding

Kirmani

(1993). A consumer-side experimental examination of signaling theory: Do consumers perceive warranties as signals of quality? Journal of Consumer Research, 20(1), 111–123. https://doi.org/10.1086/209337

Brito

P. Q.

Pratas

(2015). Tourism brochures: Linking message strategies, tactics and brand destination attributes. Tourism Management, 48, 123–138. https://doi.org/10.1016/j.tourman.2014.10.013

Cao

Liu

Yan

Dai

P. S.

Sun

(2023). A comprehensive survey of AI-Generated Content (AIGC): A history of generative AI from GAN to ChatGPT. arXiv preprint arXiv: 2303.04226.

Carvalho

Ivanov

(2024). ChatGPT for tourism: Applications, benefits and risks. Tourism Review, 79(2), pp. 290–303. https://doi.org/10.1108/TR-02-2023-0088

Chang

J. B.

Lusk

J. L.

Norwood

F. B.

(2009). How closely do hypothetical surveys and laboratory experiments predict field behavior? American Journal of Agricultural Economics, 91(2), 518–534. https://doi.org/10.1111/j.1467-8276.2008.01242.x

Chattopadhyay

Mitra

S. K.

(2019). Do Airbnb host listing attributes influence room pricing homogenously? International Journal of Hospitality Management, 81, 54–64. https://doi.org/10.1016/j.ijhm.2019.03.008

10.

Chen

Baird

Straub

(2020). A linguistic signaling model of social support exchange in online health communities. Decision Support Systems, 130, Article 113233. https://doi.org/10.1016/j.dss.2019.113233

11.

Chen

Guestrin

(2016, August 13–17). XGBoost: A scalable tree boosting system [Conference session]. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA. https://doi.org/10.1145/2939672.2939785

12.

Chen

Wang

Pan

(2019). How do product recommendations affect impulse buying? An empirical study on WeChat social commerce. Information & Management, 56(2), 236–248. https://doi.org/10.1016/j.im.2018.09.002

13.

Chen

Zhou

Jin

Chen

(2023). Investigating the determinants of medical crowdfunding performance: A signaling theory perspective. Internet Research, 33(3), 1134–1156. https://doi.org/10.1108/INTR-09-2021-0652

14.

Chung

Sarnikar

(2022). Understanding host marketing strategies on Airbnb and their impact on listing performance: A text analytics approach. Information Technology & People, 35(7), 2075–2097. https://doi.org/10.1108/ITP-10-2020-0718

15.

Cillo

Rubera

(2024). Generative AI in innovation and marketing processes: A roadmap of research opportunities. Journal of the Academy of Marketing Science. https://doi.org/10.1007/s11747-024-01044-7

16.

Cohen

Liu

Hanna

Hopkins

Higham

Gössling

(2021). The rich kids of Instagram: Luxury travel, transport modes, and desire. Journal of Travel Research, 61(7), 1479–1494. https://doi.org/10.1177/00472875211037748

17.

Connelly

B. L.

Certo

S. T.

Ireland

R. D.

Reutzel

C. R.

(2010). Signaling theory: A review and assessment. Journal of Management, 37(1), 39–67. https://doi.org/10.1177/0149206310388419

18.

Deng

(2018). Feeling a destination through the “right” photos: A machine learning model for DMOs’ photo selection. Tourism Management, 65, 267–278. https://doi.org/10.1016/j.tourman.2017.09.010

19.

Dolnicar

Ring

(2014). Tourism marketing research: Past, present and future. Annals of Tourism Research, 47, 31–47. https://doi.org/10.1016/j.annals.2014.03.008

20.

Dwivedi

Y. K.

Kshetri

Hughes

Slade

E. L.

Jeyaraj

Kar

A. K.

Baabdullah

A. M.

Koohang

Raghavan

Ahuja

Albanna

Albashrawi

Al-Busaidi

Balakrishnan

Barlette

Basu

Bose

Brooks

Buhalis

…Wright

(2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, Article 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642

21.

Efron

Tibshirani

R. J.

(1994). An introduction to the bootstrap. CRC Press.

22.

Erdem

Swait

(2004). Brand credibility, brand consideration, and choice. Journal of Consumer Research, 31(1), 191–198. https://doi.org/10.1086/383434

23.

Falk

Scaglione

(2023). Effects of regulations on the Airbnb market in Geneva. Tourism Economics, 30(3), 615–632. https://doi.org/10.1177/13548166231175049

24.

Fan

Lai

Fan

Z.-P.

Chen

(2023). Exit and transition: Exploring the survival status of Airbnb listings in a time of professionalization. Tourism Management, 95, Article 104665. https://doi.org/10.1016/j.tourman.2022.104665

25.

Fan

Lai

Fan

Z.-P.

(2021). Exploring the role of social function integrated in peer-to-peer property rental platform: Evidence from Airbnb in Beijing. Journal of Travel & Tourism Marketing, 38(3), 282–302. https://doi.org/10.1080/10548408.2021.1906384

26.

Filieri

Milone

F. L.

Paolucci

Raguseo

(2023). A big data analysis of COVID-19 impacts on Airbnbs’ bookings behavior applying construal level and signaling theories. International Journal of Hospitality Management, 111, Article 103461. https://doi.org/10.1016/j.ijhm.2023.103461

27.

Filieri

Raguseo

Vitari

(2020). Extremely negative ratings and online consumer review helpfulness: The moderating role of product quality signals. Journal of Travel Research, 60(4), 699–717. https://doi.org/10.1177/0047287520916785

28.

Flesch

(1948). A new readability yardstick. Journal of Applied Psychology, 32(3), 221–233. https://doi.org/10.1037/h0057532

29.

Friedman

J. H.

(2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5), 1189–1232. http://www.jstor.org/stable/2699986

30.

Gao

C. A.

Howard

F. M.

Markov

N. S.

Dyer

E. C.

Ramesh

Luo

Pearson

A. T.

(2023). Comparing scientific abstracts generated by ChatGPT to real abstracts with detectors and blinded human reviewers. npj Digital Medicine, 6(1), Article 75. https://doi.org/10.1038/s41746-023-00819-6

31.

Garfinkle

(2023). ChatGPT on track to surpass 100 million users faster than TikTok or Instagram: UBS. https://finance.yahoo.com/news/chatgpt-on-track-to-surpass-100-million-users-faster-than-tiktok-or-instagram-ubs-214423357.html

32.

Goh

K.-Y.

Heng

C.-S.

Lin

(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. https://doi.org/10.1287/isre.1120.0469

33.

Golder

P. N.

Dekimpe

M. G.

J. T.

van Heerde

H. J.

Kim

D. S. U.

Alba

J. W.

(2022). Learning from data: An empirics-first approach to relevant knowledge generation. Journal of Marketing, 87(3), 319–336. https://doi.org/10.1177/00222429221129200

34.

Govers

F. M.

(2005). Projected destination image online: Website content analysis of pictures and text. Information Technology & Tourism, 7(2), 73–89. https://doi.org/10.3727/1098305054517327

35.

Guo

Zhang

Wang

Jiang

Nie

Ding

Yue

(2023). How close is ChatGPT to human experts? Comparison corpus, evaluation, and detection. arXiv preprint arXiv:2301.07597.

36.

Gursoy

Song

(2023). ChatGPT and the hospitality and tourism industry: An overview of current trends and future research directions. Journal of Hospitality Marketing & Management, 32(5), 579–592. https://doi.org/10.1080/19368623.2023.2211993

37.

Deng

(2021). How to “read” a destination from images? Machine learning and network methods for DMOs’ image projection and photo evaluation. Journal of Travel Research, 61(3), 597–619. https://doi.org/10.1177/0047287521995134

38.

Hitsuwari

Ueda

Yun

Nomura

(2023). Does human–AI collaboration lead to more creative art? Aesthetic evaluation of human-made and AI-generated haiku poetry. Computers in Human Behavior, 139, Article 107502. https://doi.org/10.1016/j.chb.2022.107502

39.

T. K.

(1995, August 14–16). Random decision forests [Conference session]. Proceedings of 3rd International Conference on Document Analysis and Recognition, Montreal, QC, Canada. IEEE.

40.

Hossin

Sulaiman

M. N.

(2015). A review on evaluation metrics for data classification evaluations. International Journal of Data Mining & Knowledge Management Process, 5(2), 1–11. https://doi.org/10.5121/ijdkp.2015.5201

41.

Hsu

C. H. C.

Tan

Stantic

(2024). A fine-tuned tourism-specific generative AI concept. Annals of Tourism Research, 104, Article 103723. https://doi.org/10.1016/j.annals.2023.103723

42.

Hsu

Lachenbruch

P. A.

(2014). Paired t test. In Wiley StatsRef: Statistics Reference Online. https://doi.org/10.1002/9781118445112.stat05929

43.

Huang

G. I.

Wong

I. A.

Law

(2020). Dilemma of hotel reviews: The role of information processing and validation through metacognition. Journal of Travel Research, 60(6), 1301–1317. https://doi.org/10.1177/0047287520938863

44.

Jain

A. K.

Mao

Mohiuddin

K. M.

(1996). Artificial neural networks: A tutorial. Computer, 29(3), 31–44. https://doi.org/10.1109/2.485891

45.

Jiang

Benbasat

(2007). Investigating the influence of the functional mechanisms of online product presentations. Information Systems Research, 18(4), 454–470. http://www.jstor.org/stable/23015415

46.

Kim

J. H.

Kim

Park

Kim

Jhang

King

(2025). When ChatGPT gives incorrect answers: The impact of inaccurate information by generative AI on tourism decision-making. Journal of Travel Research, 64(1), 51–73. https://doi.org/10.1177/00472875231212996

47.

Kim

(2019). The influence of authenticity of online reviews on trust formation among travelers. Journal of Travel Research, 59(5), 763–776. https://doi.org/10.1177/0047287519868307

48.

Kim

T. K.

(2015). T test as a parametric statistic. Korean Journal of Anesthesiology, 68(6), 540–546. https://doi.org/10.4097/kjae.2015.68.6.540

49.

Kirkos

(2021). Airbnb listings’ performance: Determinants and predictive models. European Journal of Tourism Research, 30, 3012. https://doi.org/10.54055/ejtr.v30i.2142

50.

Kirmani

Rao

A. R.

(2000). No pain, no gain: A critical review of the literature on signaling unobservable product quality. Journal of Marketing, 64(2), 66–79. https://doi.org/10.1509/jmkg.64.2.66.18000

51.

Köbis

Mossink

L. D.

(2021). Artificial intelligence versus Maya Angelou: Experimental evidence that people cannot differentiate AI-generated from human-written poetry. Computers in Human Behavior, 114, Article 106553. https://doi.org/10.1016/j.chb.2020.106553

52.

Kunz

M. M.

Bretschneider

Erler

Leimeister

J. M.

(2017). An empirical investigation of signaling in reward-based crowdfunding. Electronic Commerce Research, 17(3), 425–461. https://doi.org/10.1007/s10660-016-9249-0

53.

Kwok

Xie

K. L.

(2019). Pricing strategies on Airbnb: Are multi-unit hosts revenue pros? International Journal of Hospitality Management, 82, 252–259. https://doi.org/10.1016/j.ijhm.2018.09.013

54.

Lai

Fan

(2021). Understanding the attenuation of the accommodation recommendation spillover effect in view of spatial distance. Journal of the Association for Information Science and Technology, 72(11), 1448–1453. https://doi.org/10.1002/asi.24488

55.

Cui

Peng

(2017). The signaling effect of management response in engaging customers: A study of the hotel industry. Tourism Management, 62, 42–53. https://doi.org/10.1016/j.tourman.2017.03.009

56.

Srinivasan

(2019). Competitive dynamics in the sharing economy: An analysis in the context of Airbnb and Hotels. Marketing Science, 38(3), 365–391. https://doi.org/10.1287/mksc.2018.1143

57.

Liu

Fan

(2024). How do different types of user-generated content attract travelers? Taking story and review on Airbnb as the example. Journal of Travel Research, 63(2), 371–387. https://doi.org/10.1177/00472875231158588

58.

Petrick

J. F.

(2008). Tourism marketing in an era of paradigm shift. Journal of Travel Research, 46(3), 235–244. https://doi.org/10.1177/0047287507303976

59.

Liang

Schuckert

Law

Chen

C.-C.

(2017). Be a “Superhost”: The importance of badge systems for peer-to-peer rental accommodations. Tourism Management, 60, 454–465. https://doi.org/10.1016/j.tourman.2017.01.007

60.

Liang

Schuckert

Law

Chen

C.-C.

(2020). The importance of marketer-generated content to peer-to-peer property rental platforms: Evidence from Airbnb. International Journal of Hospitality Management, 84, Article 102329. https://doi.org/10.1016/j.ijhm.2019.102329

61.

Linardatos

Papastefanopoulos

Kotsiantis

(2021). Explainable AI: A review of machine learning interpretability methods. Entropy, 23(1), Article 18. https://doi.org/10.3390/e23010018

62.

Liu

Ranka

Kahveci

(2008). Classification and feature selection algorithms for multi-class CGH data. Bioinformatics, 24(13), i86–i95. https://doi.org/10.1093/bioinformatics/btn145

63.

Liu

Yuan

Jiang

Hayashi

Neubig

(2023). Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys, 55(9), 1–35. https://doi.org/10.1145/3560815

64.

Liu

Zhang

Yue

Zhao

Cheng

Zhang

(2023). ArguGPT: evaluating, understanding and identifying argumentative essays generated by GPT models. arXiv preprint arXiv:2304.07666.

65.

Liu

(2023). Is this abstract generated by AI? A research for the gap between AI-generated scientific text and human-written scientific text. arXiv preprint arXiv:2301.10416.

66.

Marchenko

(2019). The impact of host race and gender on prices on Airbnb. Journal of Housing Economics, 46, Article 101635. https://doi.org/10.1016/j.jhe.2019.101635

67.

Mavlanova

Benbunan-Fich

Koufaris

(2012). Signaling theory and information asymmetry in online commerce. Information & Management, 49(5), 240–247. https://doi.org/10.1016/j.im.2012.05.004

68.

Medhat

Hassan

Korashy

(2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093–1113. https://doi.org/10.1016/j.asej.2014.04.011

69.

Mitra

Fay

(2010). Managing service expectations in online markets: A signaling theory of e-tailer pricing and empirical tests. Journal of Retailing, 86(2), 184–199. https://doi.org/10.1016/j.jretai.2010.02.003

70.

Murtagh

(1991). Multilayer perceptrons for classification and regression. Neurocomputing, 2(5), 183–197. https://doi.org/10.1016/0925-2312(91)90023-5

71.

Naseri

Waygood

E. O. D.

Wang

Patterson

(2023). Interpretable machine learning approach to predicting electric vehicle buying decisions. Transportation Research Record, 2677(12), 704–717. https://doi.org/10.1177/03611981231169533

72.

OpenAI. (2023). Prompt engineering. https://platform.openai.com/docs/guides/prompt-engineering

73.

OpenAI. (2024). GPT-4o Model. https://platform.openai.com/docs/models/gpt-4o

74.

Polak

M. P.

Morgan

(2024). Extracting accurate materials data from research papers with conversational language models and prompt engineering. Nature Communications, 15(1), Article 1569. https://doi.org/10.1038/s41467-024-45914-8

75.

Press

S. J.

Wilson

(1978). Choosing between logistic regression and discriminant analysis. Journal of the American Statistical Association, 73(364), 699–705. https://doi.org/10.1080/01621459.1978.10480080

76.

Qian

Liu

Ling

Jiang

Chai

Liu

(2022). Popularity prediction for marketer-generated content: A text-guided attention neural network for multi-modal feature fusion. Information Processing & Management, 59(4), Article 102984. https://doi.org/10.1016/j.ipm.2022.102984

77.

Quinlan

J. R.

(1986). Induction of decision trees. Machine Learning, 1(1), 81–106. https://doi.org/10.1007/BF00116251

78.

Sahadev

Seiler

Scarf

(2023). The impact of review sentiments on occupancy: Evidence for signalling theory from peer-to-peer accommodation. Journal of Vacation Marketing, 31(2), 351–365. https://doi.org/10.1177/13567667231201406

79.

Sainaghi

Baggio

(2020). Substitution threat between Airbnb and hotels: Myth or reality? Annals of Tourism Research, 83, Article 102959. https://doi.org/10.1016/j.annals.2020.102959

80.

Sayfuddin

A. T. M.

Chen

(2021). The signaling and reputational effects of customer ratings on hotel revenues: Evidence from TripAdvisor. International Journal of Hospitality Management, 99, Article 103065. https://doi.org/10.1016/j.ijhm.2021.103065

81.

Sengupta

Biswas

Kumar

Shankar

Gupta

(2021). Examining the predictors of successful Airbnb bookings with Hurdle models: Evidence from Europe, Australia, USA and Asia-Pacific cities. Journal of Business Research, 137, 538–554. https://doi.org/10.1016/j.jbusres.2021.08.035

82.

Shabbir

Noureldin

Sim

S.-H.

(2025). Data-driven model for seismic assessment, design, and retrofit of structures using explainable artificial intelligence. Computer-Aided Civil and Infrastructure Engineering, 40(3), 281–300. https://doi.org/10.1111/mice.13338

83.

Shahriar

Lund

B. D.

Mannuru

N. R.

Arshad

M. A.

Hayawi

Bevara

R. V.

Mannuru

Batool

(2024). Putting GPT-4o to the sword: A comprehensive evaluation of language, vision, speech, and multimodal proficiency. Applied Sciences, 14(17), Article 7782.

84.

Shin

Kim

Lee

Yhee

Koo

(2025). ChatGPT for trip planning: The effect of narrowing down options. Journal of Travel Research, 64(2), 247–266. https://doi.org/10.1177/00472875231214196

85.

Shirshikov

(2023). AI + Airbnb: What Hosts Need to Know. https://awning.com/news/ai-airbnb-hosts

86.

Short

C. E.

Short

J. C.

(2023). The artificially intelligent entrepreneur: ChatGPT, prompt engineering, and entrepreneurial rhetoric creation. Journal of Business Venturing Insights, 19, Article e00388. https://doi.org/10.1016/j.jbvi.2023.e00388

87.

Song

Park

S. B.

Park

(2021). Social media marketing strategies for tourism destinations: Effects of linguistic features and content types. Journal of Smart Tourism, 1(3), 21–29. https://doi.org/10.52255/smarttourism.2021.1.3.5

88.

Sparks

B. A.

Browning

(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

89.

Spence

(1973). Job market signaling. The Quarterly Journal of Economics, 87(3), 355–374. https://doi.org/10.2307/1882010

90.

Tang

Nawijn

(2023). How destination social responsibility shapes resident emotional solidarity and quality of life: Moderating roles of disclosure tone and visual messaging. Journal of Travel Research, 62(1), 105–120. https://doi.org/10.1177/00472875211056683

91.

Tao

Fang

Luo

Wan

(2022). Which marketer-generated-content is more effective? An experimental study in the context of a peer-to-peer accommodation platform. International Journal of Hospitality Management, 100, Article 103089. https://doi.org/10.1016/j.ijhm.2021.103089

92.

Uthaisar

Eves

Wang

X. L.

(2024). Tourists’ online information search behavior: Combined user-generated and marketer-generated content in restaurant decision making. Journal of Travel Research, 63(6), 1549–1573. https://doi.org/10.1177/00472875231195314

93.

Vapnik

(1995). The nature of statistical learning theory. Springer.

94.

Wahid

Mero

Ritala

(2023). Editorial: Written by ChatGPT, illustrated by Midjourney: Generative AI for content marketing. Asia Pacific Journal of Marketing and Logistics, 35(8), 1813–1822. https://doi.org/10.1108/APJML-10-2023-994

95.

Wang

Liu

Xiao

Gong

Dang

(2025). General vs. specific: How location information control drives performance in peer-to-peer accommodation. Tourism Management, 107, Article 105037. https://doi.org/10.1016/j.tourman.2024.105037

96.

Wang

Nicolau

J. L.

(2017). Price determinants of sharing economy based accommodation rental: A study of listings from 33 cities on Airbnb.com. International Journal of Hospitality Management, 62, 120–131. https://doi.org/10.1016/j.ijhm.2016.12.007

97.

Wang

Yang

Cai

Yin

(2024). Unleashing ChatGPT’s power: A case study on optimizing information retrieval in flipped classrooms via prompt engineering. IEEE Transactions on Learning Technologies, 17, 629–641. https://doi.org/10.1109/TLT.2023.3324714

98.

Wang

(2021). Human territoriality in P2P accommodation: An examination of guest experience. Journal of Travel Research, 60(8), 1735–1751. https://doi.org/10.1177/0047287520964588

99.

Wang

Yang

Wang

Zheng

Peng

(2024). How do voice characteristics affect tourism interpretation purchases? An empirical study based on voice mining. Journal of Travel Research, 63(2), 481–495. https://doi.org/10.1177/00472875221151070

100.

Webb

Holyoak

K. J.

(2023). Emergent analogical reasoning in large language models. Nature Human Behaviour, 7(9), 1526–1541. https://doi.org/10.1038/s41562-023-01659-w

101.

Wells

J. D.

Valacich

J. S.

Hess

T. J.

(2011). What signal are you sending? How website quality influences perceptions of product quality and purchase intentions. MIS Quarterly, 35(2), 373–396. https://doi.org/10.2307/23044048

102.

Xie

Mao

(2017). The impacts of quality and quantity attributes of Airbnb hosts on listing performance. International Journal of Contemporary Hospitality Management, 29(9), 2240–2260. https://doi.org/10.1108/IJCHM-07-2016-0345

103.

Xie

K. L.

Chen

(2016). Online consumer review factors affecting offline hotel popularity: Evidence from Tripadvisor. Journal of Travel & Tourism Marketing, 33(2), 211–223. https://doi.org/10.1080/10548408.2015.1050538

104.

Zhang

Qiao

Zheng

Dam

S. K.

Zhang

Kim

J. U.

Kim

S. T.

Choi

Park

G.-M.

Bae

S.-H.

Lee

L.-H.

Hui

Kweon

I. S.

Hong.

C. S

. (2023). One small step for generative AI, one giant leap for AGI: A complete survey on ChatGPT in AIGC era. arXiv preprint arXiv: 2304.06488.

105.

Zhang

Wei

Zhang

Liang

Law

(2024). Power of sentiment expressions on peer-to-peer rental platforms: A mixed-method approach. Journal of Travel Research, 63(2), 428–446. https://doi.org/10.1177/00472875231158598

The Power of AI-Generated Content: Evidence From the Peer-to-Peer Accommodation Market

Abstract

Keywords

Get full access to this article

References