Do low-carbon rewards incentivize people to ridesplitting? Evidence from structural analysis View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2022-06-22

AUTHORS

Lei Wang, Wenxiang Li, Jinxian Weng, Dong Zhang, Wanjing Ma

ABSTRACT

Ridesplitting is a pooled version of a ridesourcing service that can reduce emissions per trip compared with exclusive ridehailing. However, it only takes a small share of the ridesourcing market currently. Fiscal and social incentives are potential leverage tools to attract passengers from solo hailed rides. This study aims to quantitatively identify the effects of carbon credits (CC) and monetary rewards (MR) on people's willingness to adopt ridesplitting. The theory of planned behavior realized by structural equation modeling decomposes the factors which affect passengers' intention and actual action on choosing ridesplitting. Confirmatory factor analysis suggests that pro-environmental attitudes do not directly force people to ridesplitting. Path effect analysis shows that subjective norms and perceived behavioral control significantly affect ridesplitting intentions. Immediate effect regression reveals that CC has greater direct effects on ridesplitting intention than MR (4.5 times more effective). Moderating effect analysis shows the MR effect reduces while the CC effect enhances when increasing the incentive value. The results encourage policymakers and operation designers to consider better marketing schemes combining monetary and social incentives to promote ridesplitting and to gain better fiscal and environmental benefits from ridesourcing systems. More... »

PAGES

1-33

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11116-022-10302-y

DOI

http://dx.doi.org/10.1007/s11116-022-10302-y

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1148871299


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/09", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Engineering", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/12", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Built Environment and Design", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/15", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Commerce, Management, Tourism and Services", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0905", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Civil Engineering", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1205", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Urban and Regional Planning", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1507", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Transportation and Freight Services", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "The Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, 201804, Shanghai, China", 
          "id": "http://www.grid.ac/institutes/grid.24516.34", 
          "name": [
            "College of Transport and Communications, Shanghai Maritime University, 201306, Shanghai, China", 
            "The Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, 201804, Shanghai, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wang", 
        "givenName": "Lei", 
        "id": "sg:person.012030700251.92", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012030700251.92"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Business School, University of Shanghai for Science and Technology, 200093, Shanghai, China", 
          "id": "http://www.grid.ac/institutes/grid.267139.8", 
          "name": [
            "Business School, University of Shanghai for Science and Technology, 200093, Shanghai, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Li", 
        "givenName": "Wenxiang", 
        "id": "sg:person.015647334531.22", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015647334531.22"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "College of Transport and Communications, Shanghai Maritime University, 201306, Shanghai, China", 
          "id": "http://www.grid.ac/institutes/grid.412518.b", 
          "name": [
            "College of Transport and Communications, Shanghai Maritime University, 201306, Shanghai, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Weng", 
        "givenName": "Jinxian", 
        "id": "sg:person.0700473772.06", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0700473772.06"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "School of Transportation and Logistics, Dalian University of Technology, 116024, Dalian, Liaoning, China", 
          "id": "http://www.grid.ac/institutes/grid.30055.33", 
          "name": [
            "School of Transportation and Logistics, Dalian University of Technology, 116024, Dalian, Liaoning, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhang", 
        "givenName": "Dong", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "The Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, 201804, Shanghai, China", 
          "id": "http://www.grid.ac/institutes/grid.24516.34", 
          "name": [
            "The Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, 201804, Shanghai, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ma", 
        "givenName": "Wanjing", 
        "id": "sg:person.015107654761.97", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015107654761.97"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1038/s41467-021-23287-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1138501134", 
          "https://doi.org/10.1038/s41467-021-23287-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11116-019-10070-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1122654690", 
          "https://doi.org/10.1007/s11116-019-10070-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02294210", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005655179", 
          "https://doi.org/10.1007/bf02294210"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11069-018-3223-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101223751", 
          "https://doi.org/10.1007/s11069-018-3223-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11069-018-3461-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1106388625", 
          "https://doi.org/10.1007/s11069-018-3461-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11116-020-10112-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1127471797", 
          "https://doi.org/10.1007/s11116-020-10112-0"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2022-06-22", 
    "datePublishedReg": "2022-06-22", 
    "description": "Ridesplitting is a pooled version of a ridesourcing service that can reduce emissions per trip compared with exclusive ridehailing. However, it only takes a small share of the ridesourcing market currently. Fiscal and social incentives are potential leverage tools to attract passengers from solo hailed rides. This study aims to quantitatively identify the effects of carbon credits (CC) and monetary rewards (MR) on people's willingness to adopt ridesplitting. The theory of planned behavior realized by structural equation modeling decomposes the factors which affect passengers' intention and actual action on choosing ridesplitting. Confirmatory factor analysis suggests that pro-environmental attitudes do not directly force people to ridesplitting. Path effect analysis shows that subjective norms and perceived behavioral control significantly affect ridesplitting intentions. Immediate effect regression reveals that CC has greater direct effects on ridesplitting intention than MR (4.5 times more effective). Moderating effect analysis shows the MR effect reduces while the CC effect enhances when increasing the incentive value. The results encourage policymakers and operation designers to consider better marketing schemes combining monetary and social incentives to promote ridesplitting and to gain better fiscal and environmental benefits from ridesourcing systems.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s11116-022-10302-y", 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1043848", 
        "issn": [
          "0049-4488", 
          "1572-9435"
        ], 
        "name": "Transportation", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }
    ], 
    "keywords": [
      "social incentives", 
      "carbon credits", 
      "monetary rewards", 
      "small share", 
      "effects regression", 
      "pooled version", 
      "people's willingness", 
      "marketing schemes", 
      "pro-environmental attitudes", 
      "incentives", 
      "willingness", 
      "environmental benefits", 
      "effect analysis", 
      "actual actions", 
      "incentive value", 
      "credit", 
      "market", 
      "share", 
      "policymakers", 
      "leverages tools", 
      "reward", 
      "ridehailing", 
      "direct effect", 
      "subjective norms", 
      "factor analysis", 
      "benefits", 
      "greatest direct effect", 
      "services", 
      "evidence", 
      "analysis", 
      "trips", 
      "regression", 
      "intention", 
      "effect", 
      "theory", 
      "passengers", 
      "ride", 
      "people", 
      "behavioral control", 
      "norms", 
      "confirmatory factor analysis", 
      "attitudes", 
      "emission", 
      "values", 
      "behavior", 
      "factors", 
      "version", 
      "results", 
      "scheme", 
      "tool", 
      "action", 
      "solo", 
      "study", 
      "system", 
      "structural analysis", 
      "control", 
      "designers", 
      "decomposes", 
      "passengers' intention", 
      "CC effects", 
      "ridesplitting", 
      "MR effect"
    ], 
    "name": "Do low-carbon rewards incentivize people to ridesplitting? Evidence from structural analysis", 
    "pagination": "1-33", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1148871299"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s11116-022-10302-y"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s11116-022-10302-y", 
      "https://app.dimensions.ai/details/publication/pub.1148871299"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-10-01T06:50", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20221001/entities/gbq_results/article/article_941.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s11116-022-10302-y"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s11116-022-10302-y'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11116-022-10302-y'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11116-022-10302-y'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11116-022-10302-y'


 

This table displays all metadata directly associated to this object as RDF triples.

190 TRIPLES      21 PREDICATES      94 URIs      76 LITERALS      4 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s11116-022-10302-y schema:about anzsrc-for:09
2 anzsrc-for:0905
3 anzsrc-for:12
4 anzsrc-for:1205
5 anzsrc-for:15
6 anzsrc-for:1507
7 schema:author Nb3a2792a18bb41988132912729ff3c03
8 schema:citation sg:pub.10.1007/bf02294210
9 sg:pub.10.1007/s11069-018-3223-1
10 sg:pub.10.1007/s11069-018-3461-2
11 sg:pub.10.1007/s11116-019-10070-2
12 sg:pub.10.1007/s11116-020-10112-0
13 sg:pub.10.1038/s41467-021-23287-6
14 schema:datePublished 2022-06-22
15 schema:datePublishedReg 2022-06-22
16 schema:description Ridesplitting is a pooled version of a ridesourcing service that can reduce emissions per trip compared with exclusive ridehailing. However, it only takes a small share of the ridesourcing market currently. Fiscal and social incentives are potential leverage tools to attract passengers from solo hailed rides. This study aims to quantitatively identify the effects of carbon credits (CC) and monetary rewards (MR) on people's willingness to adopt ridesplitting. The theory of planned behavior realized by structural equation modeling decomposes the factors which affect passengers' intention and actual action on choosing ridesplitting. Confirmatory factor analysis suggests that pro-environmental attitudes do not directly force people to ridesplitting. Path effect analysis shows that subjective norms and perceived behavioral control significantly affect ridesplitting intentions. Immediate effect regression reveals that CC has greater direct effects on ridesplitting intention than MR (4.5 times more effective). Moderating effect analysis shows the MR effect reduces while the CC effect enhances when increasing the incentive value. The results encourage policymakers and operation designers to consider better marketing schemes combining monetary and social incentives to promote ridesplitting and to gain better fiscal and environmental benefits from ridesourcing systems.
17 schema:genre article
18 schema:isAccessibleForFree false
19 schema:isPartOf sg:journal.1043848
20 schema:keywords CC effects
21 MR effect
22 action
23 actual actions
24 analysis
25 attitudes
26 behavior
27 behavioral control
28 benefits
29 carbon credits
30 confirmatory factor analysis
31 control
32 credit
33 decomposes
34 designers
35 direct effect
36 effect
37 effect analysis
38 effects regression
39 emission
40 environmental benefits
41 evidence
42 factor analysis
43 factors
44 greatest direct effect
45 incentive value
46 incentives
47 intention
48 leverages tools
49 market
50 marketing schemes
51 monetary rewards
52 norms
53 passengers
54 passengers' intention
55 people
56 people's willingness
57 policymakers
58 pooled version
59 pro-environmental attitudes
60 regression
61 results
62 reward
63 ride
64 ridehailing
65 ridesplitting
66 scheme
67 services
68 share
69 small share
70 social incentives
71 solo
72 structural analysis
73 study
74 subjective norms
75 system
76 theory
77 tool
78 trips
79 values
80 version
81 willingness
82 schema:name Do low-carbon rewards incentivize people to ridesplitting? Evidence from structural analysis
83 schema:pagination 1-33
84 schema:productId N65cb11fff69c4fd1bce32eb33433aec7
85 Nae0b58579d8148fca1cc291f2c619b04
86 schema:sameAs https://app.dimensions.ai/details/publication/pub.1148871299
87 https://doi.org/10.1007/s11116-022-10302-y
88 schema:sdDatePublished 2022-10-01T06:50
89 schema:sdLicense https://scigraph.springernature.com/explorer/license/
90 schema:sdPublisher Nce674a231a9f4daa8e2164f4a5b1cfc2
91 schema:url https://doi.org/10.1007/s11116-022-10302-y
92 sgo:license sg:explorer/license/
93 sgo:sdDataset articles
94 rdf:type schema:ScholarlyArticle
95 N0d5b51a70d444035967280613930c86c rdf:first sg:person.0700473772.06
96 rdf:rest Ne366c264930a4e74aba60e5019c14aab
97 N3e8d7d56ebe845cbae25d5d271d002dd rdf:first sg:person.015107654761.97
98 rdf:rest rdf:nil
99 N65cb11fff69c4fd1bce32eb33433aec7 schema:name doi
100 schema:value 10.1007/s11116-022-10302-y
101 rdf:type schema:PropertyValue
102 Nae0b58579d8148fca1cc291f2c619b04 schema:name dimensions_id
103 schema:value pub.1148871299
104 rdf:type schema:PropertyValue
105 Nb3a2792a18bb41988132912729ff3c03 rdf:first sg:person.012030700251.92
106 rdf:rest Nee9a094716754a5994e70b067b60af18
107 Nb7a85a89347f4c3698a491908a49a4f7 schema:affiliation grid-institutes:grid.30055.33
108 schema:familyName Zhang
109 schema:givenName Dong
110 rdf:type schema:Person
111 Nce674a231a9f4daa8e2164f4a5b1cfc2 schema:name Springer Nature - SN SciGraph project
112 rdf:type schema:Organization
113 Ne366c264930a4e74aba60e5019c14aab rdf:first Nb7a85a89347f4c3698a491908a49a4f7
114 rdf:rest N3e8d7d56ebe845cbae25d5d271d002dd
115 Nee9a094716754a5994e70b067b60af18 rdf:first sg:person.015647334531.22
116 rdf:rest N0d5b51a70d444035967280613930c86c
117 anzsrc-for:09 schema:inDefinedTermSet anzsrc-for:
118 schema:name Engineering
119 rdf:type schema:DefinedTerm
120 anzsrc-for:0905 schema:inDefinedTermSet anzsrc-for:
121 schema:name Civil Engineering
122 rdf:type schema:DefinedTerm
123 anzsrc-for:12 schema:inDefinedTermSet anzsrc-for:
124 schema:name Built Environment and Design
125 rdf:type schema:DefinedTerm
126 anzsrc-for:1205 schema:inDefinedTermSet anzsrc-for:
127 schema:name Urban and Regional Planning
128 rdf:type schema:DefinedTerm
129 anzsrc-for:15 schema:inDefinedTermSet anzsrc-for:
130 schema:name Commerce, Management, Tourism and Services
131 rdf:type schema:DefinedTerm
132 anzsrc-for:1507 schema:inDefinedTermSet anzsrc-for:
133 schema:name Transportation and Freight Services
134 rdf:type schema:DefinedTerm
135 sg:journal.1043848 schema:issn 0049-4488
136 1572-9435
137 schema:name Transportation
138 schema:publisher Springer Nature
139 rdf:type schema:Periodical
140 sg:person.012030700251.92 schema:affiliation grid-institutes:grid.24516.34
141 schema:familyName Wang
142 schema:givenName Lei
143 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012030700251.92
144 rdf:type schema:Person
145 sg:person.015107654761.97 schema:affiliation grid-institutes:grid.24516.34
146 schema:familyName Ma
147 schema:givenName Wanjing
148 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015107654761.97
149 rdf:type schema:Person
150 sg:person.015647334531.22 schema:affiliation grid-institutes:grid.267139.8
151 schema:familyName Li
152 schema:givenName Wenxiang
153 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015647334531.22
154 rdf:type schema:Person
155 sg:person.0700473772.06 schema:affiliation grid-institutes:grid.412518.b
156 schema:familyName Weng
157 schema:givenName Jinxian
158 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0700473772.06
159 rdf:type schema:Person
160 sg:pub.10.1007/bf02294210 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005655179
161 https://doi.org/10.1007/bf02294210
162 rdf:type schema:CreativeWork
163 sg:pub.10.1007/s11069-018-3223-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101223751
164 https://doi.org/10.1007/s11069-018-3223-1
165 rdf:type schema:CreativeWork
166 sg:pub.10.1007/s11069-018-3461-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1106388625
167 https://doi.org/10.1007/s11069-018-3461-2
168 rdf:type schema:CreativeWork
169 sg:pub.10.1007/s11116-019-10070-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1122654690
170 https://doi.org/10.1007/s11116-019-10070-2
171 rdf:type schema:CreativeWork
172 sg:pub.10.1007/s11116-020-10112-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1127471797
173 https://doi.org/10.1007/s11116-020-10112-0
174 rdf:type schema:CreativeWork
175 sg:pub.10.1038/s41467-021-23287-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1138501134
176 https://doi.org/10.1038/s41467-021-23287-6
177 rdf:type schema:CreativeWork
178 grid-institutes:grid.24516.34 schema:alternateName The Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, 201804, Shanghai, China
179 schema:name College of Transport and Communications, Shanghai Maritime University, 201306, Shanghai, China
180 The Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, 201804, Shanghai, China
181 rdf:type schema:Organization
182 grid-institutes:grid.267139.8 schema:alternateName Business School, University of Shanghai for Science and Technology, 200093, Shanghai, China
183 schema:name Business School, University of Shanghai for Science and Technology, 200093, Shanghai, China
184 rdf:type schema:Organization
185 grid-institutes:grid.30055.33 schema:alternateName School of Transportation and Logistics, Dalian University of Technology, 116024, Dalian, Liaoning, China
186 schema:name School of Transportation and Logistics, Dalian University of Technology, 116024, Dalian, Liaoning, China
187 rdf:type schema:Organization
188 grid-institutes:grid.412518.b schema:alternateName College of Transport and Communications, Shanghai Maritime University, 201306, Shanghai, China
189 schema:name College of Transport and Communications, Shanghai Maritime University, 201306, Shanghai, China
190 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...