A study of residents’ intentions to participate in the renovation of older communities under the perspective of urban renewal: evidence from Zhangjiakou, China

Figures & data

Figure 1. A model of the factors influencing residents’ intention to participate in the renovation of old communities.

Table 1. Measurement items of the variables.

Constructs	Measuring items	Source
Awareness of consequences	Community renovation projects can help reduce energy consumption and carbon emissions	Sang et al. (2020) Han et al. (2019)
	Participation in community renovation projects helps to reduce environmental deterioration
	Community renovation projects can help reduce pollutant emissions
Ascription of responsibility	I feel a common responsibility for energy consumption	Sang et al. (2020) Han et al. (2019)
	I feel a common responsibility for environmental degradation
	I feel a common responsibility for the discharge of pollutants
Subjective norm	My family thinks I should participate in community renovation projects	Maichum, Parichatnon, and Peng (2016) Liobikiene, Mandravickaite, and Bernatoniene (2016)
	My friends think I should participate in community renovation projects
	Construction practitioners think that I should be involved in the community renovation
Perceived behavioural control	I believe I will participate in community renovation projects if I want to	Armitage and Conner (2001) Kim and Han (2010)
	I can participate in community renovation projects in the future
	I have enough time and resources to participate in community renovation projects
Personal norm	I have a moral obligation to participate in community renovation projects	Sang et al. (2020) Han et al. (2019)
	I have a moral responsibility to participate in community renovation projects
	I think I should do my best to participate in community renovation projects
Governmental incentives	The establishment of a dedicated residents’ services organisation by the government will increase our participation	Chan et al. (2017) Diyana and Abidin (2013) Yin et al. (2016) Zhang et al. (2019)
	Government supplies direct grants to participate in community renovation projects
	Government supplies a soft loan incentive to participate in community renovation projects
Residents are willing to participate in the renovation of old houses	I want to experience the community renovation project	Liu et al. (2018)
	I would like to recommend the community renovation project to others to attend
	I want to participate in the community renovation project

Table 2. Demographic characteristics of the respondents (N = 353).

Variable	Group	Frequency	Percentage(%)
Gender	Male	208	58.9%
	Female	145	41.1%
Age	18–25	67	19.0%
	26–35	198	56.1%
	36–45	44	12.5%
	46–55	28	7.9%
	>55	16	4.5%
Education	High school and below	40	11.3%
	Junior college	77	21.8%
	Bachelor degree	187	53.0%
	Master degree	44	12.5%
	Ph.D.	5	1.4%
Income	<1000	40	11.3%
	1001–3000	44	12.5%
	3001–5000	52	14.7%
	5001–7000	50	14.2%
	7001–9000	54	15.3%
	>9000	113	32.0%
Occupation	Senior manager	17	4.8%
	Middle manager	60	17.0%
	General staff	201	56.9%
	Self-employed person	26	7.4%
	Student	38	10.8%
	Retiree	11	3.1%

In occupation, senior manager is usually the main leader and sub-main leader of the organisation (e.g. school principal, vice principal, party secretary, etc.), middle manager is the person in charge of specific matters in the organisation (e.g. director in charge of teaching, director in charge of safety, etc.), general staff is usually the person who performs actions in the organisation (e.g. teachers, cleaners in the school) and self-employed person is a freelancer who does not have an agency.

Table 3. Standardised item loadings, average extracted, combined reliability and alpha values.

Latent variable	Item	Standardised item loading	Cronbach’s α	CR	AVE
Awareness of consequences	AC1	0.887	0.891	0.892	0.734
	AC2	0.849
	AC3	0.831
Ascription of responsibility	AR1	0.794	0.879	0.881	0.712
	AR2	0.843
	AR3	0.891
Subjective norm	SN1	0.87	0.893	0.894	0.738
	SN2	0.844
	SN3	0.862
Perceived behavioural control	PBC1	0.844	0.885	0.886	0.721
	PBC2	0.865
	PBC3	0.84
Personal norm	PN1	0.84	0.894	0.894	0.737
	PN2	0.873
	PN3	0.862
Governmental incentives	GI1	0.843	0.895	0.895	0.740
	GI2	0.814
	GI3	0.913
Participate intention	PI1	0.867	0.890	0.894	0.739
	PI2	0.858
	PI3	0.856

Table 4. Constructs’ correlations and square roots of AVE.

Construct	AC	AR	SN	PBC	PN	GI	PI
AC	0.857
AR	0.323	0.844
SN	0.143	0.027	0.859
PBC	0.424	0.383	0.265	0.849
PN	0.264	0.288	0.186	0.393	0.859
GI	0.402	0.440	0.213	0.469	0.404	0.860
PI	0.402	0.404	0.197	0.424	0.338	0.481	0.859

The bold italic numbers presented diagonally are the square root of AVE.

Table 5. Model fit indices.

Fit Indices	Criteria	Indicators
Chi-square		372.892
Chi-square/(degree of freedom)	<5 acceptable; <3 good	2.119
Goodness-of-fit index (GFI)	>0.8 acceptable; >0.9 good	0.915
Adjusted goodness-of-fit index (AGFI)	>0.8 acceptable; >0.9 good	0.888
Normed fit index (NFI)	>0.9	0.927
Comparative fit index (CFI)	>0.9	0.960
Incremental fit index (IFI)	>0.9	0.960
Tucker–Lewis index (TLI)	>0.9	0.952
Root mean square error of approximation (RMSEA)	<0.1 acceptable; <0.08 good	0.056

Table 6. Standardised path coefficients of the structural model and hypothesis testing.

Path	Path coefficient	Standard Deviation	T-values	P-values	Result
H1: PBC→PI	0.218	0.065	3.242	***	Support
H2: PN→PI	0.122	0.053	2.179	0.029*	Support
H3: AC→PN	0.098	0.062	1.582	0.114	No Support
H4: AC→AR	0.375	0.057	6.366	***	Support
H5: AR→PN	0.174	0.062	2.888	0.004**	Support
H6: SN→PN	0.100	0.057	1.728	0.084	No Support
H7: SN→PBC	0.175	0.051	3.254	***	Support
H8: PBC→PN	0.315	0.063	5.128	***	Support
H9: GI→PBC	0.503	0.057	8.689	***	Support
H10: GI→PI	0.370	0.063	5.693	***	Support

*p < 0.05;**p < 0.01;***p ≤ 0.001.

Table 7. Results of the mediation effect test.

items	c	a*b	a*b (95% Boot CI)	c’	c’ (95% Boot CI)	Mediating effect	percentage
SN→PBC→PN	0.194	0.102	0.050 ~ 0.156	0.092	−0.012 ~ 0.196	Totally	100.00%
AC→AR→PN	0.295	0.081	0.035 ~ 0.120	0.213	0.097 ~ 0.329	Partial	27.61%
PBC→PN→PI	0.429	0.080	0.033 ~ 0.138	0.348	0.246 ~ 0.450	Partial	18.76%
GI→PBC→PI	0.480	0.119	0.061 ~ 0.194	0.360	0.260 ~ 0.461	Partial	24.85%

The c denotes total effect value; a*b denotes the mediating effect value; c’ denotes the direct effect value.

Table 8. Multi-group comparison result.

Group	Model	DF	CMIN	P	Δ NFI	Δ IFI	Δ RFI	Δ TLI
Gender	A	14	9.231	0.816	0.002	0.002	−0.004	−0.004
	B	10	13.819	0.181	0.003	0.003	−0.001	−0.001
Age	A	14	8.355	0.870	0.002	0.002	−0.003	−0.003
	B	10	9.903	0.449	0.002	0.002	−0.001	−0.001
Income	A	14	18.367	0.191	0.004	0.004	−0.001	−0.001
	B	10	23.379	0.009	0.005	0.005	0.001	0.002
Education	A	14	9.552	0.794	0.002	0.002	−0.003	−0.004
	B	10	13.215	0.212	0.002	0.003	−0.001	−0.001

Figure 2. Modelling of regulatory effects.

Table 9. Critical ratio of path coefficient.

Hypothesis	H1	H2	H3	H4	H5	H6	H7	H8	H9	H10
CR	3.297	0.217	−0.477	0.542	−0.908	−0.557	−0.732	−0.105	1.377	−1.082

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