Demystifying the nexus between social media usage and overtourism: evidence from Hangzhou, China

Figures & data

Figure 1. Hypothesised relationship between social media usage and overtourism.

A figure of the interrelationships among the dependent (overtourism) and independent variables (information seeking, entertainment, self-presentation, socialisation, social media usage, and tourist flows concentration).

Figure 2 #Study area (Xinhua, 2019).

A map of the specific study area (Hangzhou, the capital of Zhejiang Province) where we collected our survey data.

Table 1. Growth of Hangzhou population and tourists (2017–2019).

Year	Urban size (km^2)	Inhabitants (10,000 persons)	Tourist arrivals (10,000 person-times)	Growth in tourist arrivals	Visitors per resident	Visitors per km2
2017	8000	824.1	16287	13.1%	19.8	20,358.8
2018	8289	857	18403		21.5	21,764.1
2019	8289	911.9	20813.7		22.8	25,110.0

Source: Hangzhou Statistical Bureau (2019).

Table 2. Benchmarks for cities to assess overcrowding risk.

	Top quintile (high risk)	Second quintile	Third quintile	Fourth quintile	Fifth quintile (low risk)
Growth in tourist arrivals	>7.7%	6.2–7.7%	4.0–6.1%	2.0–3.9%	<2.0%
Number of visitors per square kilometre	>930,000	475,001–930,000	200,001–475,000	75,000–200,000	<75,000
Number of visitors per resident	>5.3	2.8–5.3	1.8–2.7	1.0–1.7	<1.0

Source: WTTC, McKinsey and Company (2018).

Figure 3. Factor loadings and path coefficients (PLS algorithm estimates).

A diagram portraying constructs of the study along with their respective factor loadings and path coefficients.

Table 3. Respondents profiles.

Characteristics	Frequency	Percentage (%)
Gender
Female	118	56.5
Male	91	43.5
Age
18–24 years	34	16.3
25–34 years	84	40.2
35–44 years	70	33.5
45–54 years	16	7.7
>55	5	2.4
Marital Status
Married	134	64.1
Single	71	34.0
Other	4	1.9
Education
High School	6	2.9
Diploma/Higher Diploma	20	9.6
Bachelor	99	47.4
Master and above	77	36.8
Other	7	3.3
Employment
Employed	130	62.3%
Unemployed	79	37.7%
Total	209	100

Table 4. Features of Hangzhou tourism.

Features	Frequency	Percentage (%)
Visit frequency
Only once	104	49.8
Twice	72	34.4
Greater than twice	33	15.8
Travel objectives
Sightseeing/city touring	109	52.2
As part of a holiday	45	21.5
Business	44	21.1
Visiting friends and relatives	33	15.8
As part of a visit to another nearby destination(s)	31	14.8
A day out	20	9.6
Shopping	7	3.3
Visit motivation
Desire to visit a specific touristic area	90	43.1
Convenience	68	32.5
Desire to a return visit	40	19.1
Word-of-mouth endorsement	33	15.8
Marketing campaign	22	10.5
Travel companions
Family/relatives	114	54.5
Friend(s)	74	35.4
Alone	34	16.3
Tour	9	4.3

Table 5. Tourists’ social media utilisation with overtourism.

Constructs/Items	Mean	Standard deviation
Social media usage (SMU) (2nd order factor)
Information seeking (IS)
1. I utilise social media to discover up-to-date tourism information.	5.97	1.098
2. I utilise social media to discover messages from friends simply.	5.62	1.318
3. I utilise social media to stay up to date on current events and news.	6.14	1.014
4. I cannot use social media to find the information I need.	5.72	1.161
Entertainment (EN)
1. Using social media helps me pass time.	5.12	1.576
2. I feel at ease when I use social media.	5.30	1.279
3. Using social media helps me escape from the ugliness of life.	4.61	1.652
4. It brings me joy to use social media.	5.58	1.328
Self-presentation (SP)
1. I regularly update my social media profiles.	3.51	1.693
2. I use social media to routinely publish personal information and details about my life.	3.56	1.686
3. I use social media to share my thoughts.	3.82	1.702
4. I often express myself on social media to let others get to know me better.	3.51	1.699
5. I share private information on social media to boost my self-esteem.	3.71	1.730
Socialisation (SO)
1. I have a community of friends on social media.	4.79	1.475
2. I can more easily keep in touch with my friends thanks to social media.	5.30	1.352
3. I utilise social media to reconnect with friends I had lost contact with.	5.18	1.504
4. Social media is an effective way to interact with others.	5.77	1.150
Tourist flow concentration (TFC) I chose and travelled to Hangzhou mostly because:
1. It is the most popular destination on social media.	4.73	1.668
2. Online influencers’ recommendations.	4.14	1.758
3. Online posts about the place generate a lot of “likes,” comments, and shares.	4.14	1.669
4. I must share travel-related information on social media that is engaging, passionate, exhilarating, and informative.	4.19	1.748
5. A lot of friends share their vacation experiences on social media.	4.34	1.756
6. It has a high reputation on social media.	4.02	1.825
7. There are many “instagrammable” spots there.	4.34	1.728
Overtourism (OV) During my visit to Hangzhou,
1. I noticed that the pricing was higher, yet the service quality was superb.	4.11	1.590
2. I encountered large lines and significant wait periods.	4.22	1.605
3. I discovered significant pollution issues	3.37	1.671
4. I encountered congestion and overcrowding, such as traffic jams and overcrowding in public spaces, stores, restaurants, and sights, among other things.	3.89	1.676
5. I found the folks to be hostile.	3.15	1.543
6. I discovered reckless tourist behaviour, such as congestion caused by selfie-takers and major damage to cultural monuments, among other things.	3.56	1.634
7. I believe Hangzhou is overrun with tourists.	4.13	1.606

Table 6. Measurement model assessment results.

	Outer loading	Cronbach’s alpha	CR	AVE
IS		0.793	0.866	0.618
IS1	0.827
IS2	0.767
IS3	0.824
IS4	0.723
EN		0.758	0.847	0.582
EN1	0.763
EN2	0.840
EN3	0.696
EN4	0.743
SP		0.894	0.922	0.704
SP1	0.756
SP2	0.889
SP3	0.805
SP4	0.882
SP5	0.857
SO		0.775	0.858	0.606
SO1	0.610
SO2	0.872
SO3	0.829
SO4	0.777
TFC		0.937	0.949	0.729
TFC1	0.718
TFC2	0.899
TFC3	0.913
TFC4	0.879
TFC5	0.795
TFC6	0.885
TFC7	0.872
OV		0.922	0.938	0.683
OV1	0.837
OV2	0.842
OV3	0.841
OV4	0.885
OV5	0.748
OV6	0.845
OV7	0.766

Table 7. Discriminant validity (HTMT).

	EN	IS	OV	SMU*	SO	SP	TFC
EN
IS	0.646
OV	0.134	0.091
SMU*	–	–	0.134
SO	0.637	0.529	0.095	–
SP	0.457	0.160	0.095	–	0.568
TFC	0.457	0.269	0.234	0.540	0.404	0.495

Note:* represents the high order construct.

Table 8. Hypothesis-testing results.

#	Hypothesis	Beta	Confidence interval	t-value	p-value	Decision
H1	SMU → TFC	0.464***	[0.348, 0.570]	8.002	0.000	Supported
H2	TFC → OV	0.360***	[0.214, 0.520]	4.656	0.000	Supported
H3	SMU → OV	−0.148	[−0.311, 0.010]	1.982	0.105	Unsupported
H4	SMU→TFC→OV	0.167***	[0.093, 0.266]	3.762	0.000	Supported
	SMU→ OV (Total effect)	0.019	[−0.135, 0.167]	0.247	0.805	Unsupported

Note: *p < 0.1; **p < 0.05; ***p < 0.01.

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