Factors affecting the economic impact for managing cycling cultural tourism route

Figures & data

Figure 1. Measurement model.

Figure 2. Cycling cultural tourism route at Ban Chiang, Udonthani, Thailand.

Table 1. Mean and S.D of variables

variables	N	Min	Max	Mean	S.D
Law (L)
Cyclists should respect the law while riding	433	1	5	4.00	1.069
Cyclists should observe the traffic laws regarding cycling	433	1	5	4.07	1.054
There should be a traffic law manual distributed or an orientation before riding	433	1	5	3.94	1.047
Economy (E)
The cycling route has characteristics that attract the tourists to come back again	433	1	5	3.84	.995
The cycling route can be used by tourists to refer others to invite them to use this cycling route	433	1	5	3.81	.953
There are rest stops, beverage shop,enough restaurants along the cycling route	433	1	5	3.74	.963
Social (S)
There are cultural landmarks on the cycling route	433	1	5	3.87	.976
There are various community organizations involved in managing the cycling route	433	1	5	3.80	.972
Stakeholder or network in the community has co-operated to develop cycling route	433	1	5	3.78	1.008
Route characteristic (P)
Signage design identifies a sense of place and is easy to perceive	433	1	5	3.62	1.079
The facility needed for cycling activities is found on the route and fulfil	433	1	5	3.69	1.082

Table 2. Factor loading

Variable	Law factor			Social factor			Economy factor			Route characteristic			R^2
	factor loading	SE	t	factor loading	SE	t	factor loading	SE	t	factor loading	SE	t
b2.2	.89												.78
b2.3	.93	.038	27.01										.87
b2.4	.85	.041	22.94										.73
b4.1				.84									.71
b4.2				.90	.041	23.95							.81
b4.3				.89	.047	19.46							.78
b3.1							.91						.81
b3.2							.92	.035	27.50				.86
b3.3							.82	.042	20.35				.66
b1.6										.90			.81
b1.7										.95	.042	25.1	.90
law				.77	.045	16.381	.23	.048	4.59	.24	.073	3.4	.00
social							.44	.059	7.54	.50	.079	6.8	.59
Route characteristic							.33	.042	7.44				.50
economy													.82

Table 3. Correlation Matrix of variables

	b1.6	b1.7	b2.2	b2.3	b2.4	b3.1	b3.2	b3.3	b4.1	b4.2	b4.3
b1.6
B1.7	.78
b2.2	.52	.53
b2.3	.52	.53	.83
b2.4	.53	.53	.75	.79
b3.1	.63	.66	.65	.66	.63
b3.2	.64	.65	.64	.67	.64	.74
b3.3	.62	.65	.55	.56	.56	.70	.75
b4.1	.55	.58	.61	.62	.58	.68	.65	.68
b4.2	.57	.58	.59	.64	.60	.68	.70	.65	.75
b4.3	.53	.56	.57	.61	.58	.67	.69	.60	.67	.78

Table 4. Regression Weights

	path		Estimate	S.E.	C.R.	P	Label
S	<—	L	.733	.045	16.381	***	par_9
P	<—	L	.248	.073	3.402	***	par_6
P	<—	S	.539	.079	6.823	***	par_7
E	<—	L	.221	.048	4.590	***	par_8
E	<—	S	.442	.059	7.544	***	par_10
E	<—	P	.310	.042	7.443	***	par_15
b2.2	<—	L	1.000
b2.3	<—	L	1.025	.038	27.139	***	par_1
b2.4	<—	L	.937	.041	22.987	***	par_2
b1.6	<—	P	1.000
b3.1	<—	E	1.000
b3.3	<—	E	.856	.040	21.491	***	par_3
b4.3	<—	S	1.000
b4.2	<—	S	.971	.040	23.997	***	par_4
b4.1	<—	S	.905	.047	19.283	***	par_5
b1.7	<—	P	1.052	.042	25.132	***	par_14
b3.2	<—	E	.985	.035	28.358	***	par_16