Antecedents of Guest Booking Intention in the Home-Sharing Industry: Lessons Learned from Airbnb

Figures & data

Table 1. Synthesized literature on determinants of Airbnb booking.

Theme	Author and Year	Research Objective	Method/Theories	Context	Determinants
(Micro-level) Consumer perspective	Julião et al. (2022)	This study set out to explore the consumer profile and analyze its motivations to use Airbnb service.	Quantitative method, Sharing Economy Model	Portugal	Socio-demographic characteristics, user experience
	Tiamiyu et al. (2022)	This paper aims to investigate the Muslim tourists’ intention to book on Airbnb.	Quantitative method, Stimulus-Organism-Response Theory (S-O-R)	Malaysia	Reputation, Perceived value
	Jang et al. (2021)	To investigate the spatially varying relationship between destination attributes and COVID-19-disrupted Airbnb performance change.	Spatial analytics and experiment	Florida, USA	Trip purpose, Perceived threat
	Jung et al. (2021)	This study adopted the technology acceptance model (TAM) to examine tourist repurchase intention in Airbnb.	Quantitative method, Technology Acceptance Model (TAM)	South Korea	Network externalities, Trust, Interactivity, Ease of use, Usefulness
	Mohsin and Lengler (2021)	This study is set out to investigate millennial (Airbnb non‐consumers) repurchase intentions toward Airbnb service.	Quantitative method, MEC (means-end chain), Prospect theory	New Zealand	Value, Risk, Satisfaction
	Tajeddini et al. (2021)	This study examines the factors that motivate visitors’ decision-making concerning accommodation purchases in Airbnb service.	Quantitative method, Theory of planned behavior, Value-attitude-behavior model	Switzerland	Subjective norm, Attitude, Perception of quality
	Yang et al. (2021)	To examine the impact of social media posts on Airbnb booking in the south-eastern U.S.	Information model	USA	Social media information, Frequency of photo posts and video posts
	Kong et al. (2019)	This study set out to examine the effect of social and technical enablers on consumer trust and eventually lead to the continued use of Airbnb.	Quantitative method, Socio-technical theory	Asia/Pacific region	Social referrals, Information quality, Transaction Safety, Trust
	Amaro et al. (2018)	To investigate the factors that influence millennials’ intentions to book on Airbnb.	Quantitative method, Theory of Reasoned Action	Germany and China	Economic Benefits, Perceived Risk, Subjective Norm, Attitude, Unique Accommodation and Variety
(Micro-level) Airbnb Host perspective	Boto-García (2022)	This study seeks to evaluate the different price adjustments developed by professional and nonprofessional hosts to meet demand in Airbnb bookings.	Hedonic regressions techniques	Barcelona, Spain	Intertemporal price
	Kirkos (2022)	To investigate the determinants that influence customers’ purchase intentions and as well as listings’ performance of Airbnb.	Machine learning techniques	Thessaloniki, Greece	Super-host badge, Rich host presentation, Amenities, Overall rating, Quick response to customers’ requests
(Macro-level)	Jang and Kim (2022)	The research aims to empirically investigate the spatially heterogeneous COVID-19 disruptions in the Airbnb business.	Spatial Econometric Models	Florida, USA	Social, Community capital, environmental resources, Tourism clusters
	Lee et al. (2021)	To investigate the influence of cultural and social factors on a consumer’s participation in Airbnb accommodation.	Quantitative method, Hofstede’s cultural dimensions theory, Ajzen’s theory of planned behavior	South Korea	Social factors, Cultural factors
(Micro-level) Consumer behavior	Current study	This study is set to test the role of price consciousness, reputation, trust and perceived value on tourists’ booking intention in Airbnb.	Quantitative method, Attribution Theory	Malaysia	Price consciousness, Reputation, Trust, Perceived value

Source: Compiled by the researcher

Figure 1. Conceptual framework.

Table 2. Evaluation of the measurement model.

Construct	Item	Loadings	Composite Reliability	AVE
Reputation	RPT1	0.760	0.823	0.608
	RPT2	0.783
	RPT3	0.796
Price Consciousness	PC1	0.628	0.767	0.526
	PC2	0.724
	PC3	0.813
Trust in Airbnb	TA1	0.749	0.808	0.513
	TA2	0.652
	TA3	0.749
	TA4	0.711
Perceived Value	PV1	0.742	0.770	0.529
	PV2	0.670
	PV3	0.765
Guest Booking Intention	GBI1	0.813	0.853	0.595
	GBI2	0.824
	GBI3	0.810
	GBI4	0.620

Note. TA5 was deleted to increase AVE

Table 3. Discriminant validity (Fornel-Larcker’s method).

	GBI	PC	PV	RPT	TA
GBI	0.772
PC	0.314	0.725
PV	0.451	0.386	0.727
RPT	0.426	0.276	0.403	0.780
TA	0.479	0.267	0.458	0.476	0.716

Table 4. Discriminant validity (HTMT method).

	GBI	PC	PV	RPT	TA
GBI
PC	0.481
PV	0.695	0.626
RPT	0.565	0.448	0.647
TA	0.652	0.445	0.753	0.683

Note. GBI – Guest Booking Intention, PV – Perceived Value, TA – Trust in Airbnb, RPT – Reputation, PC- Price consciousness

Table 5. Results of structural model (Direct relationships).

Hypothesis	Relationship	Path Coefficient	STD	t-value	R^2	Q2	F^2	Supported
H1	RPT -> TA	0.480	0.049	9.700	0.227	0.108	0.293	Yes
H3	PC -> PV	0.389	0.051	7.542	0.147	0.072	0.172	Yes
H2	TA -> GBI	0.264	0.061	4.328	0.336	0.182	0.072	Yes
H4	PV -> GBI	0.214	0.063	3.387			0.047	Yes
H5	RPT -> GBI	0.184	0.054	3.371			0.036	Yes
H6	PC -> GBI	0.113	0.055	2.017			0.016	Yes

Figure 2. Structural model.

Table 6. Results of structural model (Indirect relationships).

Hypothesis	Path	Indirect Effect	STD	t-values	95% CI (LL – UL)	Supported
H7	RPT -> TA-> GBI	0.083	0.028	2.961	0.034–0.141	Yes
H8	PC-> PV-> GBI	0.127	0.033	3.849	0.068–0.194	Yes

Table

Construct	Items	Source
Reputation	Airbnb is a large company that everyone recognizes.	Kim et al. (2013)
	Airbnb is well-known accommodation service.
	Airbnb has a good reputation for its accommodation service.
Price consciousness	When it comes to choosing accommodation service, I rely heavily on price.	Campbell et al. (2014)
	I am a price-conscious buyer when it comes to choose an accommodation.
	I purchase lowest priced accommodation that suits my needs.
Trust in Airbnb	Airbnb is trustworthy.	Liang et al. (2018)
	Airbnb gives impression that it keeps promises and commitments.
	I believe that Airbnb has my best interests in mind.
	I think Airbnb will keep promises it made to me.
	I think Airbnb wants to be known as one who keeps promises and commitments.
Perceived value	I think Airbnb provide good value for money (e.g., reasonably priced).	Chen and Chang (2018)
	I think Airbnb can help with special requirements (e.g., finding pet-friendly accommodations, arranging a small bed for baby etc.).
	I think booking for accommodations on Airbnb is worthwhile.
Guest booking intention	The probability that I would consider booking an accommodation from Airbnb is high.	Xu and Schrier (2019)
	If I were to book an accommodation, I would consider booking it from Airbnb.
	The likelihood of my booking an accommodation from Airbnb is high.
	My willingness to book an accommodation from Airbnb in the future is high.

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