Research on Group Choice Behavior in Green Travel Based on Planned Behavior Theory and Complex Network

Motor vehicle exhaust emissions have made air pollution increasingly serious in China, and advocating for the concept of green travel can help alleviate the air pollution caused by motor vehicle exhaust. Thus, the research on the green travel choice behavior of limited rational individuals in the co...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Junjun Zheng [verfasserIn] Mingyuan Xu [verfasserIn] Runfa Li [verfasserIn] Liukai Yu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	green travel group choice behavior planned behavior theory scale-free network propagation dynamics

Übergeordnetes Werk:	In: Sustainability - MDPI AG, 2009, 11(2019), 14, p 3765
Übergeordnetes Werk:	volume:11 ; year:2019 ; number:14, p 3765

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/su11143765

Katalog-ID:	DOAJ073752118

Internformat


LEADER	01000caa a22002652 4500
001	DOAJ073752118
003	DE-627
005	20230309120827.0
007	cr uuu---uuuuu
008	230228s2019 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.3390/su11143765 \|2 doi
035			\|a (DE-627)DOAJ073752118
035			\|a (DE-599)DOAJ21a21d3b6518445cb027c78339ae5a5a
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
050		0	\|a TD194-195
050		0	\|a TJ807-830
050		0	\|a GE1-350
100	0		\|a Junjun Zheng \|e verfasserin \|4 aut
245	1	0	\|a Research on Group Choice Behavior in Green Travel Based on Planned Behavior Theory and Complex Network
264		1	\|c 2019
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a Motor vehicle exhaust emissions have made air pollution increasingly serious in China, and advocating for the concept of green travel can help alleviate the air pollution caused by motor vehicle exhaust. Thus, the research on the green travel choice behavior of limited rational individuals in the complex social network and the evolution of group behavior is the focus of this paper. Based on the theory of planned behavior, this paper established the individual cognition-behavior model. Meanwhile, an interaction model of individuals in the network is constructed based on the DeGroot model and scale-free network. The simulation results of the model show that: (1) it is difficult to control the behavior of green travel: even if the knowledge level of green travel is high, the proportion of green travel individuals in the group is still very low; (2) the individual intention for green travel is dependent on behavioral attitude, which can effectively improve the proportion of green travel individuals; (3) if the individual intention is too dependent on the subjective norm and the perception of behavioral result, the proportion of green travel individuals would become lower; and (4) when the network is connected, the proportion of individuals who choose green travel will reach the peak through social interaction and learning. This study has a certain practical significance for the environmental protection work of relevant departments, which can guide the behavior of individuals through the design of government institutions, and enable the concept of green travel to form an ideology by means of education and knowledge dissemination, so as to generate some kind of consensual behavioral consciousness. Meanwhile, this study provides a new research perspective for behavioral research and extends the research scope of group behavior.
650		4	\|a green travel
650		4	\|a group choice behavior
650		4	\|a planned behavior theory
650		4	\|a scale-free network
650		4	\|a propagation dynamics
653		0	\|a Environmental effects of industries and plants
653		0	\|a Renewable energy sources
653		0	\|a Environmental sciences
700	0		\|a Mingyuan Xu \|e verfasserin \|4 aut
700	0		\|a Runfa Li \|e verfasserin \|4 aut
700	0		\|a Liukai Yu \|e verfasserin \|4 aut
773	0	8	\|i In \|t Sustainability \|d MDPI AG, 2009 \|g 11(2019), 14, p 3765 \|w (DE-627)610604120 \|w (DE-600)2518383-7 \|x 20711050 \|7 nnns
773	1	8	\|g volume:11 \|g year:2019 \|g number:14, p 3765
856	4	0	\|u https://doi.org/10.3390/su11143765 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/21a21d3b6518445cb027c78339ae5a5a \|z kostenfrei
856	4	0	\|u https://www.mdpi.com/2071-1050/11/14/3765 \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/2071-1050 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a GBV_ILN_11
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 11 \|j 2019 \|e 14, p 3765

Indexfelder

author_variant	j z jz m x mx r l rl l y ly
matchkey_str	article:20711050:2019----::eerhnrucocbhvoigenrvlaeopandeai
hierarchy_sort_str	2019
callnumber-subject-code	TD
publishDate	2019
allfields	10.3390/su11143765 doi (DE-627)DOAJ073752118 (DE-599)DOAJ21a21d3b6518445cb027c78339ae5a5a DE-627 ger DE-627 rakwb eng TD194-195 TJ807-830 GE1-350 Junjun Zheng verfasserin aut Research on Group Choice Behavior in Green Travel Based on Planned Behavior Theory and Complex Network 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Motor vehicle exhaust emissions have made air pollution increasingly serious in China, and advocating for the concept of green travel can help alleviate the air pollution caused by motor vehicle exhaust. Thus, the research on the green travel choice behavior of limited rational individuals in the complex social network and the evolution of group behavior is the focus of this paper. Based on the theory of planned behavior, this paper established the individual cognition-behavior model. Meanwhile, an interaction model of individuals in the network is constructed based on the DeGroot model and scale-free network. The simulation results of the model show that: (1) it is difficult to control the behavior of green travel: even if the knowledge level of green travel is high, the proportion of green travel individuals in the group is still very low; (2) the individual intention for green travel is dependent on behavioral attitude, which can effectively improve the proportion of green travel individuals; (3) if the individual intention is too dependent on the subjective norm and the perception of behavioral result, the proportion of green travel individuals would become lower; and (4) when the network is connected, the proportion of individuals who choose green travel will reach the peak through social interaction and learning. This study has a certain practical significance for the environmental protection work of relevant departments, which can guide the behavior of individuals through the design of government institutions, and enable the concept of green travel to form an ideology by means of education and knowledge dissemination, so as to generate some kind of consensual behavioral consciousness. Meanwhile, this study provides a new research perspective for behavioral research and extends the research scope of group behavior. green travel group choice behavior planned behavior theory scale-free network propagation dynamics Environmental effects of industries and plants Renewable energy sources Environmental sciences Mingyuan Xu verfasserin aut Runfa Li verfasserin aut Liukai Yu verfasserin aut In Sustainability MDPI AG, 2009 11(2019), 14, p 3765 (DE-627)610604120 (DE-600)2518383-7 20711050 nnns volume:11 year:2019 number:14, p 3765 https://doi.org/10.3390/su11143765 kostenfrei https://doaj.org/article/21a21d3b6518445cb027c78339ae5a5a kostenfrei https://www.mdpi.com/2071-1050/11/14/3765 kostenfrei https://doaj.org/toc/2071-1050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 11 2019 14, p 3765
spelling	10.3390/su11143765 doi (DE-627)DOAJ073752118 (DE-599)DOAJ21a21d3b6518445cb027c78339ae5a5a DE-627 ger DE-627 rakwb eng TD194-195 TJ807-830 GE1-350 Junjun Zheng verfasserin aut Research on Group Choice Behavior in Green Travel Based on Planned Behavior Theory and Complex Network 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Motor vehicle exhaust emissions have made air pollution increasingly serious in China, and advocating for the concept of green travel can help alleviate the air pollution caused by motor vehicle exhaust. Thus, the research on the green travel choice behavior of limited rational individuals in the complex social network and the evolution of group behavior is the focus of this paper. Based on the theory of planned behavior, this paper established the individual cognition-behavior model. Meanwhile, an interaction model of individuals in the network is constructed based on the DeGroot model and scale-free network. The simulation results of the model show that: (1) it is difficult to control the behavior of green travel: even if the knowledge level of green travel is high, the proportion of green travel individuals in the group is still very low; (2) the individual intention for green travel is dependent on behavioral attitude, which can effectively improve the proportion of green travel individuals; (3) if the individual intention is too dependent on the subjective norm and the perception of behavioral result, the proportion of green travel individuals would become lower; and (4) when the network is connected, the proportion of individuals who choose green travel will reach the peak through social interaction and learning. This study has a certain practical significance for the environmental protection work of relevant departments, which can guide the behavior of individuals through the design of government institutions, and enable the concept of green travel to form an ideology by means of education and knowledge dissemination, so as to generate some kind of consensual behavioral consciousness. Meanwhile, this study provides a new research perspective for behavioral research and extends the research scope of group behavior. green travel group choice behavior planned behavior theory scale-free network propagation dynamics Environmental effects of industries and plants Renewable energy sources Environmental sciences Mingyuan Xu verfasserin aut Runfa Li verfasserin aut Liukai Yu verfasserin aut In Sustainability MDPI AG, 2009 11(2019), 14, p 3765 (DE-627)610604120 (DE-600)2518383-7 20711050 nnns volume:11 year:2019 number:14, p 3765 https://doi.org/10.3390/su11143765 kostenfrei https://doaj.org/article/21a21d3b6518445cb027c78339ae5a5a kostenfrei https://www.mdpi.com/2071-1050/11/14/3765 kostenfrei https://doaj.org/toc/2071-1050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 11 2019 14, p 3765
allfields_unstemmed	10.3390/su11143765 doi (DE-627)DOAJ073752118 (DE-599)DOAJ21a21d3b6518445cb027c78339ae5a5a DE-627 ger DE-627 rakwb eng TD194-195 TJ807-830 GE1-350 Junjun Zheng verfasserin aut Research on Group Choice Behavior in Green Travel Based on Planned Behavior Theory and Complex Network 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Motor vehicle exhaust emissions have made air pollution increasingly serious in China, and advocating for the concept of green travel can help alleviate the air pollution caused by motor vehicle exhaust. Thus, the research on the green travel choice behavior of limited rational individuals in the complex social network and the evolution of group behavior is the focus of this paper. Based on the theory of planned behavior, this paper established the individual cognition-behavior model. Meanwhile, an interaction model of individuals in the network is constructed based on the DeGroot model and scale-free network. The simulation results of the model show that: (1) it is difficult to control the behavior of green travel: even if the knowledge level of green travel is high, the proportion of green travel individuals in the group is still very low; (2) the individual intention for green travel is dependent on behavioral attitude, which can effectively improve the proportion of green travel individuals; (3) if the individual intention is too dependent on the subjective norm and the perception of behavioral result, the proportion of green travel individuals would become lower; and (4) when the network is connected, the proportion of individuals who choose green travel will reach the peak through social interaction and learning. This study has a certain practical significance for the environmental protection work of relevant departments, which can guide the behavior of individuals through the design of government institutions, and enable the concept of green travel to form an ideology by means of education and knowledge dissemination, so as to generate some kind of consensual behavioral consciousness. Meanwhile, this study provides a new research perspective for behavioral research and extends the research scope of group behavior. green travel group choice behavior planned behavior theory scale-free network propagation dynamics Environmental effects of industries and plants Renewable energy sources Environmental sciences Mingyuan Xu verfasserin aut Runfa Li verfasserin aut Liukai Yu verfasserin aut In Sustainability MDPI AG, 2009 11(2019), 14, p 3765 (DE-627)610604120 (DE-600)2518383-7 20711050 nnns volume:11 year:2019 number:14, p 3765 https://doi.org/10.3390/su11143765 kostenfrei https://doaj.org/article/21a21d3b6518445cb027c78339ae5a5a kostenfrei https://www.mdpi.com/2071-1050/11/14/3765 kostenfrei https://doaj.org/toc/2071-1050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 11 2019 14, p 3765
allfieldsGer	10.3390/su11143765 doi (DE-627)DOAJ073752118 (DE-599)DOAJ21a21d3b6518445cb027c78339ae5a5a DE-627 ger DE-627 rakwb eng TD194-195 TJ807-830 GE1-350 Junjun Zheng verfasserin aut Research on Group Choice Behavior in Green Travel Based on Planned Behavior Theory and Complex Network 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Motor vehicle exhaust emissions have made air pollution increasingly serious in China, and advocating for the concept of green travel can help alleviate the air pollution caused by motor vehicle exhaust. Thus, the research on the green travel choice behavior of limited rational individuals in the complex social network and the evolution of group behavior is the focus of this paper. Based on the theory of planned behavior, this paper established the individual cognition-behavior model. Meanwhile, an interaction model of individuals in the network is constructed based on the DeGroot model and scale-free network. The simulation results of the model show that: (1) it is difficult to control the behavior of green travel: even if the knowledge level of green travel is high, the proportion of green travel individuals in the group is still very low; (2) the individual intention for green travel is dependent on behavioral attitude, which can effectively improve the proportion of green travel individuals; (3) if the individual intention is too dependent on the subjective norm and the perception of behavioral result, the proportion of green travel individuals would become lower; and (4) when the network is connected, the proportion of individuals who choose green travel will reach the peak through social interaction and learning. This study has a certain practical significance for the environmental protection work of relevant departments, which can guide the behavior of individuals through the design of government institutions, and enable the concept of green travel to form an ideology by means of education and knowledge dissemination, so as to generate some kind of consensual behavioral consciousness. Meanwhile, this study provides a new research perspective for behavioral research and extends the research scope of group behavior. green travel group choice behavior planned behavior theory scale-free network propagation dynamics Environmental effects of industries and plants Renewable energy sources Environmental sciences Mingyuan Xu verfasserin aut Runfa Li verfasserin aut Liukai Yu verfasserin aut In Sustainability MDPI AG, 2009 11(2019), 14, p 3765 (DE-627)610604120 (DE-600)2518383-7 20711050 nnns volume:11 year:2019 number:14, p 3765 https://doi.org/10.3390/su11143765 kostenfrei https://doaj.org/article/21a21d3b6518445cb027c78339ae5a5a kostenfrei https://www.mdpi.com/2071-1050/11/14/3765 kostenfrei https://doaj.org/toc/2071-1050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 11 2019 14, p 3765
allfieldsSound	10.3390/su11143765 doi (DE-627)DOAJ073752118 (DE-599)DOAJ21a21d3b6518445cb027c78339ae5a5a DE-627 ger DE-627 rakwb eng TD194-195 TJ807-830 GE1-350 Junjun Zheng verfasserin aut Research on Group Choice Behavior in Green Travel Based on Planned Behavior Theory and Complex Network 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Motor vehicle exhaust emissions have made air pollution increasingly serious in China, and advocating for the concept of green travel can help alleviate the air pollution caused by motor vehicle exhaust. Thus, the research on the green travel choice behavior of limited rational individuals in the complex social network and the evolution of group behavior is the focus of this paper. Based on the theory of planned behavior, this paper established the individual cognition-behavior model. Meanwhile, an interaction model of individuals in the network is constructed based on the DeGroot model and scale-free network. The simulation results of the model show that: (1) it is difficult to control the behavior of green travel: even if the knowledge level of green travel is high, the proportion of green travel individuals in the group is still very low; (2) the individual intention for green travel is dependent on behavioral attitude, which can effectively improve the proportion of green travel individuals; (3) if the individual intention is too dependent on the subjective norm and the perception of behavioral result, the proportion of green travel individuals would become lower; and (4) when the network is connected, the proportion of individuals who choose green travel will reach the peak through social interaction and learning. This study has a certain practical significance for the environmental protection work of relevant departments, which can guide the behavior of individuals through the design of government institutions, and enable the concept of green travel to form an ideology by means of education and knowledge dissemination, so as to generate some kind of consensual behavioral consciousness. Meanwhile, this study provides a new research perspective for behavioral research and extends the research scope of group behavior. green travel group choice behavior planned behavior theory scale-free network propagation dynamics Environmental effects of industries and plants Renewable energy sources Environmental sciences Mingyuan Xu verfasserin aut Runfa Li verfasserin aut Liukai Yu verfasserin aut In Sustainability MDPI AG, 2009 11(2019), 14, p 3765 (DE-627)610604120 (DE-600)2518383-7 20711050 nnns volume:11 year:2019 number:14, p 3765 https://doi.org/10.3390/su11143765 kostenfrei https://doaj.org/article/21a21d3b6518445cb027c78339ae5a5a kostenfrei https://www.mdpi.com/2071-1050/11/14/3765 kostenfrei https://doaj.org/toc/2071-1050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 11 2019 14, p 3765
language	English
source	In Sustainability 11(2019), 14, p 3765 volume:11 year:2019 number:14, p 3765
sourceStr	In Sustainability 11(2019), 14, p 3765 volume:11 year:2019 number:14, p 3765
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	green travel group choice behavior planned behavior theory scale-free network propagation dynamics Environmental effects of industries and plants Renewable energy sources Environmental sciences
isfreeaccess_bool	true
container_title	Sustainability
authorswithroles_txt_mv	Junjun Zheng @@aut@@ Mingyuan Xu @@aut@@ Runfa Li @@aut@@ Liukai Yu @@aut@@
publishDateDaySort_date	2019-01-01T00:00:00Z
hierarchy_top_id	610604120
id	DOAJ073752118
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ073752118</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230309120827.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230228s2019 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/su11143765</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ073752118</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ21a21d3b6518445cb027c78339ae5a5a</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TD194-195</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TJ807-830</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">GE1-350</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Junjun Zheng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Research on Group Choice Behavior in Green Travel Based on Planned Behavior Theory and Complex Network</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Motor vehicle exhaust emissions have made air pollution increasingly serious in China, and advocating for the concept of green travel can help alleviate the air pollution caused by motor vehicle exhaust. Thus, the research on the green travel choice behavior of limited rational individuals in the complex social network and the evolution of group behavior is the focus of this paper. Based on the theory of planned behavior, this paper established the individual cognition-behavior model. Meanwhile, an interaction model of individuals in the network is constructed based on the DeGroot model and scale-free network. The simulation results of the model show that: (1) it is difficult to control the behavior of green travel: even if the knowledge level of green travel is high, the proportion of green travel individuals in the group is still very low; (2) the individual intention for green travel is dependent on behavioral attitude, which can effectively improve the proportion of green travel individuals; (3) if the individual intention is too dependent on the subjective norm and the perception of behavioral result, the proportion of green travel individuals would become lower; and (4) when the network is connected, the proportion of individuals who choose green travel will reach the peak through social interaction and learning. This study has a certain practical significance for the environmental protection work of relevant departments, which can guide the behavior of individuals through the design of government institutions, and enable the concept of green travel to form an ideology by means of education and knowledge dissemination, so as to generate some kind of consensual behavioral consciousness. Meanwhile, this study provides a new research perspective for behavioral research and extends the research scope of group behavior.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">green travel</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">group choice behavior</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">planned behavior theory</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">scale-free network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">propagation dynamics</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Environmental effects of industries and plants</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Renewable energy sources</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Environmental sciences</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Mingyuan Xu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Runfa Li</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Liukai Yu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Sustainability</subfield><subfield code="d">MDPI AG, 2009</subfield><subfield code="g">11(2019), 14, p 3765</subfield><subfield code="w">(DE-627)610604120</subfield><subfield code="w">(DE-600)2518383-7</subfield><subfield code="x">20711050</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:11</subfield><subfield code="g">year:2019</subfield><subfield code="g">number:14, p 3765</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/su11143765</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/21a21d3b6518445cb027c78339ae5a5a</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.mdpi.com/2071-1050/11/14/3765</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2071-1050</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">11</subfield><subfield code="j">2019</subfield><subfield code="e">14, p 3765</subfield></datafield></record></collection>
callnumber-first	T - Technology
author	Junjun Zheng
spellingShingle	Junjun Zheng misc TD194-195 misc TJ807-830 misc GE1-350 misc green travel misc group choice behavior misc planned behavior theory misc scale-free network misc propagation dynamics misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences Research on Group Choice Behavior in Green Travel Based on Planned Behavior Theory and Complex Network
authorStr	Junjun Zheng
ppnlink_with_tag_str_mv	@@773@@(DE-627)610604120
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut aut
collection	DOAJ
remote_str	true
callnumber-label	TD194-195
illustrated	Not Illustrated
issn	20711050
topic_title	TD194-195 TJ807-830 GE1-350 Research on Group Choice Behavior in Green Travel Based on Planned Behavior Theory and Complex Network green travel group choice behavior planned behavior theory scale-free network propagation dynamics
topic	misc TD194-195 misc TJ807-830 misc GE1-350 misc green travel misc group choice behavior misc planned behavior theory misc scale-free network misc propagation dynamics misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences
topic_unstemmed	misc TD194-195 misc TJ807-830 misc GE1-350 misc green travel misc group choice behavior misc planned behavior theory misc scale-free network misc propagation dynamics misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences
topic_browse	misc TD194-195 misc TJ807-830 misc GE1-350 misc green travel misc group choice behavior misc planned behavior theory misc scale-free network misc propagation dynamics misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Sustainability
hierarchy_parent_id	610604120
hierarchy_top_title	Sustainability
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)610604120 (DE-600)2518383-7
title	Research on Group Choice Behavior in Green Travel Based on Planned Behavior Theory and Complex Network
ctrlnum	(DE-627)DOAJ073752118 (DE-599)DOAJ21a21d3b6518445cb027c78339ae5a5a
title_full	Research on Group Choice Behavior in Green Travel Based on Planned Behavior Theory and Complex Network
author_sort	Junjun Zheng
journal	Sustainability
journalStr	Sustainability
callnumber-first-code	T
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2019
contenttype_str_mv	txt
author_browse	Junjun Zheng Mingyuan Xu Runfa Li Liukai Yu
container_volume	11
class	TD194-195 TJ807-830 GE1-350
format_se	Elektronische Aufsätze
author-letter	Junjun Zheng
doi_str_mv	10.3390/su11143765
author2-role	verfasserin
title_sort	research on group choice behavior in green travel based on planned behavior theory and complex network
callnumber	TD194-195
title_auth	Research on Group Choice Behavior in Green Travel Based on Planned Behavior Theory and Complex Network
abstract	Motor vehicle exhaust emissions have made air pollution increasingly serious in China, and advocating for the concept of green travel can help alleviate the air pollution caused by motor vehicle exhaust. Thus, the research on the green travel choice behavior of limited rational individuals in the complex social network and the evolution of group behavior is the focus of this paper. Based on the theory of planned behavior, this paper established the individual cognition-behavior model. Meanwhile, an interaction model of individuals in the network is constructed based on the DeGroot model and scale-free network. The simulation results of the model show that: (1) it is difficult to control the behavior of green travel: even if the knowledge level of green travel is high, the proportion of green travel individuals in the group is still very low; (2) the individual intention for green travel is dependent on behavioral attitude, which can effectively improve the proportion of green travel individuals; (3) if the individual intention is too dependent on the subjective norm and the perception of behavioral result, the proportion of green travel individuals would become lower; and (4) when the network is connected, the proportion of individuals who choose green travel will reach the peak through social interaction and learning. This study has a certain practical significance for the environmental protection work of relevant departments, which can guide the behavior of individuals through the design of government institutions, and enable the concept of green travel to form an ideology by means of education and knowledge dissemination, so as to generate some kind of consensual behavioral consciousness. Meanwhile, this study provides a new research perspective for behavioral research and extends the research scope of group behavior.
abstractGer	Motor vehicle exhaust emissions have made air pollution increasingly serious in China, and advocating for the concept of green travel can help alleviate the air pollution caused by motor vehicle exhaust. Thus, the research on the green travel choice behavior of limited rational individuals in the complex social network and the evolution of group behavior is the focus of this paper. Based on the theory of planned behavior, this paper established the individual cognition-behavior model. Meanwhile, an interaction model of individuals in the network is constructed based on the DeGroot model and scale-free network. The simulation results of the model show that: (1) it is difficult to control the behavior of green travel: even if the knowledge level of green travel is high, the proportion of green travel individuals in the group is still very low; (2) the individual intention for green travel is dependent on behavioral attitude, which can effectively improve the proportion of green travel individuals; (3) if the individual intention is too dependent on the subjective norm and the perception of behavioral result, the proportion of green travel individuals would become lower; and (4) when the network is connected, the proportion of individuals who choose green travel will reach the peak through social interaction and learning. This study has a certain practical significance for the environmental protection work of relevant departments, which can guide the behavior of individuals through the design of government institutions, and enable the concept of green travel to form an ideology by means of education and knowledge dissemination, so as to generate some kind of consensual behavioral consciousness. Meanwhile, this study provides a new research perspective for behavioral research and extends the research scope of group behavior.
abstract_unstemmed	Motor vehicle exhaust emissions have made air pollution increasingly serious in China, and advocating for the concept of green travel can help alleviate the air pollution caused by motor vehicle exhaust. Thus, the research on the green travel choice behavior of limited rational individuals in the complex social network and the evolution of group behavior is the focus of this paper. Based on the theory of planned behavior, this paper established the individual cognition-behavior model. Meanwhile, an interaction model of individuals in the network is constructed based on the DeGroot model and scale-free network. The simulation results of the model show that: (1) it is difficult to control the behavior of green travel: even if the knowledge level of green travel is high, the proportion of green travel individuals in the group is still very low; (2) the individual intention for green travel is dependent on behavioral attitude, which can effectively improve the proportion of green travel individuals; (3) if the individual intention is too dependent on the subjective norm and the perception of behavioral result, the proportion of green travel individuals would become lower; and (4) when the network is connected, the proportion of individuals who choose green travel will reach the peak through social interaction and learning. This study has a certain practical significance for the environmental protection work of relevant departments, which can guide the behavior of individuals through the design of government institutions, and enable the concept of green travel to form an ideology by means of education and knowledge dissemination, so as to generate some kind of consensual behavioral consciousness. Meanwhile, this study provides a new research perspective for behavioral research and extends the research scope of group behavior.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700
container_issue	14, p 3765
title_short	Research on Group Choice Behavior in Green Travel Based on Planned Behavior Theory and Complex Network
url	https://doi.org/10.3390/su11143765 https://doaj.org/article/21a21d3b6518445cb027c78339ae5a5a https://www.mdpi.com/2071-1050/11/14/3765 https://doaj.org/toc/2071-1050
remote_bool	true
author2	Mingyuan Xu Runfa Li Liukai Yu
author2Str	Mingyuan Xu Runfa Li Liukai Yu
ppnlink	610604120
callnumber-subject	TD - Environmental Technology
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.3390/su11143765
callnumber-a	TD194-195
up_date	2024-07-03T19:26:04.382Z
_version_	1803587173106057216
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ073752118</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230309120827.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230228s2019 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/su11143765</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ073752118</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ21a21d3b6518445cb027c78339ae5a5a</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TD194-195</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TJ807-830</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">GE1-350</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Junjun Zheng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Research on Group Choice Behavior in Green Travel Based on Planned Behavior Theory and Complex Network</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Motor vehicle exhaust emissions have made air pollution increasingly serious in China, and advocating for the concept of green travel can help alleviate the air pollution caused by motor vehicle exhaust. Thus, the research on the green travel choice behavior of limited rational individuals in the complex social network and the evolution of group behavior is the focus of this paper. Based on the theory of planned behavior, this paper established the individual cognition-behavior model. Meanwhile, an interaction model of individuals in the network is constructed based on the DeGroot model and scale-free network. The simulation results of the model show that: (1) it is difficult to control the behavior of green travel: even if the knowledge level of green travel is high, the proportion of green travel individuals in the group is still very low; (2) the individual intention for green travel is dependent on behavioral attitude, which can effectively improve the proportion of green travel individuals; (3) if the individual intention is too dependent on the subjective norm and the perception of behavioral result, the proportion of green travel individuals would become lower; and (4) when the network is connected, the proportion of individuals who choose green travel will reach the peak through social interaction and learning. This study has a certain practical significance for the environmental protection work of relevant departments, which can guide the behavior of individuals through the design of government institutions, and enable the concept of green travel to form an ideology by means of education and knowledge dissemination, so as to generate some kind of consensual behavioral consciousness. Meanwhile, this study provides a new research perspective for behavioral research and extends the research scope of group behavior.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">green travel</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">group choice behavior</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">planned behavior theory</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">scale-free network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">propagation dynamics</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Environmental effects of industries and plants</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Renewable energy sources</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Environmental sciences</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Mingyuan Xu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Runfa Li</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Liukai Yu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Sustainability</subfield><subfield code="d">MDPI AG, 2009</subfield><subfield code="g">11(2019), 14, p 3765</subfield><subfield code="w">(DE-627)610604120</subfield><subfield code="w">(DE-600)2518383-7</subfield><subfield code="x">20711050</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:11</subfield><subfield code="g">year:2019</subfield><subfield code="g">number:14, p 3765</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/su11143765</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/21a21d3b6518445cb027c78339ae5a5a</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.mdpi.com/2071-1050/11/14/3765</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2071-1050</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">11</subfield><subfield code="j">2019</subfield><subfield code="e">14, p 3765</subfield></datafield></record></collection>
score	7.398946

Nicht das Richtige dabei?

Schreiben Sie uns!

Research on Group Choice Behavior in Green Travel Based on Planned Behavior Theory and Complex Network

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?