People
Push and Pull Factors in Adopting a
Crowdsourced Delivery System
Transportation Research Record
2019, Vol. 2673(7) 529–540
Ó National Academy of Sciences:
Transportation Research Board 2019
Article reuse guidelines:
sagepub.com/journals-permissions
DOI: 10.1177/0361198119842127
journals.sagepub.com/home/trr
Aymeric Punel1, Alireza Ermagun2, and Amanda Stathopoulos1
Abstract
This study explores push and pull factors affecting the adoption of crowdsourced delivery or crowd-shipping systems using a
structural equation method. The core data used in this study are obtained from an online survey distributed among 800 individuals in June 2016 in the United States (U.S.) Analyzing the answers of 533 respondents, the direct and indirect effects of
the personal attitudes, socio-demographic, and built-environment variables on the likelihood to be a crowd-shipper are
looked at. The model suggests that crowd-shipping is more likely for men, full time employed, younger respondents, and for
areas of higher population density yet lower density of employment opportunities. Concerning attitudinal motivations, the
results suggest that financially motivated users are less likely to become crowd-shipping users, while community orientation
and viewing the platform as a platform for helping relationships has only indirect effects on use. This leads to the observation
that the early adopters appear to hold quite distinct, more critical, views of the ability of the new shipping platforms to
deliver affordable and community-building service.
The adoption of sharing economic and collaborative systems is growing, fueled by the development of mobile
connected technology and public interest in service
affordability (1–6). Sharing economic and collaborative
systems have attracted attention in the transportation
sector with ridesharing and on-demand mobility services
as common examples (7). The rise of the sharing economy has affected both the passenger and freight industries. Crowdsourced delivery or crowd-shipping is one of
the on-demand mobility services that is currently growing in the freight industry. Crowd-shipping operates as
online platforms where senders are matched with
enrolled couriers from the crowd, sharing the empty
space in their vehicle for monetary compensation.
Crowdsourced delivery services are relatively new but
their penetration in the parcel delivery market is growing
(8). Advantages of the system, such as affordability and
flexibility, attract a growing customer base (9, 10).
Weaknesses, such as privacy and trust challenges, limit
its diffusion. In line with the theory of diffusion of innovation, crowd-shipping is in its early stages, and current
users can be considered as ‘‘early adopters’’ (11).
Knowing their characteristics helps foster the growth of
the system as it is maturing over time. Crowd-shipping
has attracted a sizeable body of research, which, until
now, has focused on the logistics and business aspects
(9, 12–14). To date, few studies have analyzed acceptance
and motivations for using crowd-shipping.
This paper contributes to the existing body of literature by defining latent constructs and studying their
direct and indirect effects on the use of crowd-shipping.
The goal of the research is to identify the main factors,
including personal motivations, socio-demographic and
built-environment variables, which play a role in the
adoption of crowdsourced delivery systems. Of particular
interest is whether latent factors related to saving money
and sense of community can foster the adoption of crowdshipping, in line with motivations in other sharing economy systems. Using online survey data, a confirmatory
factor analysis is run to define attitudes held by the
respondents to the survey. Then, a structural equation
model is developed where the explanatory power of the
defined factors for adopting crowd-shipping is assessed.
The research continues by examining the direct versus
the indirect effects generated by the broader context represented by socio-demographic information and builtenvironment variables. The results contribute to the
understanding of push and pull factors in crowd-shipping
1
Department of Civil and Environmental Engineering, Northwestern
University, Evanston, IL
2
Department of Civil and Environmental Engineering, Mississippi State
University, Mississippi State, MS
Corresponding Author:
Address correspondence to Aymeric Punel:
AymericPunel@u.northwestern.edu
530
use, and especially highlight the categories of people who
are more likely to be early adopters. The findings can
benefit the developing crowd-shipping industry by helping businesses to expand their user base. On the one
hand, current users’ socio-demographic information
allows companies to identify the categories of people
who are more likely to enroll in the system. On the other
hand, early adopters’ preferences and attitudes provide
insights regarding the aspects of the service that led to
adoption. Jointly, these insights would allow crowdshipping firms to adjust their marketing strategy to
attract new customers and build critical mass.
The rest of the paper is organized as follows. First,
there is a summary of the literature on consumers’ motivations for using sharing economic systems. Second, the
data is presented by introducing the sample and describing the variables used in the analysis. Then, the method
employed to design the structural equation model is
explained, followed by the discussion of the results.
Finally, the paper is concluded by summarizing the main
findings, and setting the limits of the current work that
need to be addressed for future studies.
Background
As part of the sharing economy, crowd-shipping is
likely to attract customers who have specific motivations and expectations toward the service. Most of
the existing research on crowd-shipping concerns the
logistics (9, 12–14). But some research also examines
the behavioral aspects (15, 16). However, none of the
previous work has focused on individual attitudinal
motivations for using a crowdsourced delivery system.
The remainder of this section reviews the literature on
consumer motivations for using sharing economic
systems more broadly by distinguishing studies using
structural equation modeling and others. Finally, the
existing literature involving survey related to crowdshipping is looked at.
Several studies use structural equation modeling to
understand individuals’ behavioral motivations for using
sharing economy systems (5, 17, 18). Hamari et al. (5)
define four motivational dimensions that they believe
explain attitudes toward sharing economy and behavioral intention to use these systems: (a) sustainability,
(b) enjoyment, (c) reputation, and (d) economic benefits.
They test their hypotheses in a structural equation model
using data from 168 users of a collaborative consumption platform in 2013. Their results show that intrinsic
motivations (perceived sustainability and perceived
enjoyment) do have a significant positive role in forming
attitudes toward sharing economic services, while extrinsic motivations (expected gains in reputation and economic benefits) do not. Likewise, two of the four
Transportation Research Record 2673(7)
constructs significantly predict behavioral intention of
using collaborative consumptions service: perceived
enjoyment and economic benefits, which both have a positive effect. Möhlmann focuses on two specific examples,
Business-to-Consumer car sharing services and the
Consumer-to-Consumer accommodation marketplace, by
collecting answers from 236 car2go and 187 Airbnb users
(17). To understand the determinants of choosing a sharing service, he uses partial least squares path modeling
analysis. The framework controls for a range of factors,
either directly leading to choosing a sharing service again,
or indirectly, as mediated through the choice satisfaction.
His global findings are that satisfaction and likelihood
for choosing a sharing economic service are essentially
explained by utility, trust, cost savings, and familiarity,
which appear to be determinants serving users’ self-benefit. He also finds evidence that service quality and sense
of community have a positive role in the satisfaction and
likelihood of choosing a ridesharing service. Airbnb is
also studied in So et al.’s research looking at consumer
motivations (18). They build a partial least squares path
model. This model looks at the impact of motivations
and constraints on the overall attitude towards Airbnb.
Analysis of 519 respondents from an online survey
reveals that price value, enjoyment, social interactions,
and home benefits significantly contribute to positively
explain the overall attitude towards Airbnb.
Other research explores individual motivations for
using collaborative platforms in the context of ridesharing (19–21). Amirkiaee and Evangelopoulos establish a
list of 11 hypotheses based on the literature, which are
categorized into two categories: (i) personal attributes
such as altruism or sustainability concern, and (ii) contextual attributes such as economic benefits or trust (in
rideshare) (20). They test these hypotheses in a structural
equation model and assess direct effects on the intention
to participate in rideshare, and indirect effects mediated
through attitudes toward ridesharing. To evaluate the
model, they used the answers of 300 undergraduate students from a public university via an online survey.
Results suggest that personal attributes do not play a significant role in ridesharing participation intention.
Indeed, a high sense of community, altruism, or environmental concerns are not associated with interest in rideshare programs. The authors do find that people who
believe in mutual assistance are more likely to use ridesharing. A different story emerges for their defined contextual attributes. Trust, especially, is one of the
strongest factors to explain intention to participate in
ridesharing directly, and mediated through attitudes
toward ridesharing. This feature is the focus of
Mittendorf’s work (19). He assesses the impact of trust
and familiarity with a ridesharing company and drivers,
and evaluates how this trust influences the likelihood to
Punel et al
531
Table 1. Summary of Studies on Crowd-Shipping Involving Survey Analysis
Study
Devari
et al. (23)
Marcucci
et al. (24)
Miller
et al. (25)
Le and
Ukkusuri (26)
Serafini
et al. (27)
Ta et al. (28)
Goal of the study
Study the potential benefits of people’s
social network for supporting
crowd-shipping activities
Investigate people’s willingness to
send or deliver packages via
crowd-shipping
Measure driver’s willingness
to work as crowd-shipper
Investigate crowd-shipping
sender’s behavior and potential
driver’s willingness to work
Model people’s willingness to act
as crowd-shipper using
public transport
Analyze effects of driver disclosure
and ethnicity on crowd-shipping
customers’ attitudes toward
drivers and retailers
Model developed
in the paper
Sample
size
Logistic Regression Model
104
Exploratory Analysis
190
Multinomial Logit Model
236
Exploratory Analysis
1,058
Multinomial Logit Model
225
Measurement Model with
Confirmatory Factor
Analysis
761
use the ridesharing service. To estimate the structural
equation model, Mittendorf developed a survey that was
disseminated in 2016 and completed by 221 participants,
where he asked the respondents to answer a list of 30
statements about Uber using a 5-point Likert scale. He
adds nuance to Amirkiaee and Evangelopoulos’s
research as he distinguishes the trust in the company
from that in its drivers (20). He finds that the degree of
trust in a driver does not have an impact on the intention
to use the system but the degree of trust in the company
does. Wang et al. build a Technology Acceptance Model
(TAM), which consists in a Confirmatory Factor
Analysis and Path Analysis, to evaluate the behavioral
intention to use ridesharing systems (21). They employ
Davis’ work as a base model that they enhance with
three innovative constructs, which are (a) personal innovativeness, (b) environmental awareness, and (c) perceived risk (22). They develop an online questionnaire
distributed in Spring 2017 to ca. 600 college students
from Chinese universities. Using the responses of 426
respondents, they show that personal innovativeness,
environmental awareness, and perceived usefulness have
a positive role in the intention to use ridesharing, while
perceived risk significantly decreases the willingness to
use rideshare services.
Finally, several studies investigate the crowd-shipping
phenomenon by designing and disseminating surveys and
experiments (23–28). A summary is presented in Table 1.
Devari et al. evaluate the potential benefit of customer’s
social network for supporting crowd-shipping activities
Population
of the survey
U.S. population paid to complete
the survey online on Survey
Monkey
Students at Roma Tre University
(1) Students at Northwestern
University completing the survey
online, and (2) online panel from
Illinois from Amazon’s Mechanical
Turk paid to complete the survey
online
Respondents from U.S. and Vietnam
recruited via different platforms
to complete an online survey
Population from Roma reached by
social media, email, and face to
face interviews in metro stations
Online panel from Amazon
Mechanical Turk and Qualtrics,
both coming from the U.S. and
compensated
(23). Using a binary logistic regression model, they show
that participants are more willing to perform a delivery
when the service requester is a friend. Marcucci et al.
investigate the key elements to promote a successful
development of a crowd-shipping system by focusing on
the early adopters (24). They find from their exploratory
analysis that 93% of the respondents are willing to use
crowd-shipping service to send their packages, and that
87% are willing to act as crowd-shippers against a correct monetary compensation, although no one would be
willing to stop more than 5 times along their regular
journey. Miller et al. also look at people’s willingness to
use crowd-shipping service, but with a focus on the driver
side (25). They design a mixed logit model using results
from a stated preference experiment. They estimate the
median value of the willingness to work as a crowdshipper to be $19 per hour. Their findings also highlight
that low and high income earners are less likely to work
as crowd-shippers. Two similar studies investigating people’s willingness to act as crowd-shipper were carried out
by Le and Ukkusuri, and Serafini et al. (26, 27). On the
one hand, Le and Ukkusuri examine both senders’ and
drivers’ behavior by developing a survey including
revealed preference (RP) and stated preference (SP) questions tailored to cover both sides of a crowd-shipping
system (26). Their main findings reveal a high interest in
participating in a crowd-shipping system as 80% of the
respondents are willing to work as crowd-shippers, essentially motivated by economic factors. On the other hand,
Serafini et al. innovate by focusing on the use of public
532
transport (27). They also develop a multinomial logit
model which identifies the most important features for a
crowd-shipper to be remuneration, bank crediting
modes, delivery booking, and location of delivery points.
Finally, Ta et al. develop a measurement model to investigate the effects of driver disclosure and ethnicity on
customers’ attitude towards drivers and retailers in a
crowd-shipping system (28). Their results show that companies which disclose drivers’ information increase the
trust and satisfaction of their customers, who are more
likely to use the system again.
Reviewing the literature on sharing economy and
crowd-shipping helped the identification of research
opportunities that are addressed in this paper. Structural
equation modeling (SEM) has seen several applications
in the field of sharing economy, but none of them actually treated specifically crowd-shipping. Developing an
SEM would be a novel contribution in this industry and
would allow the derivation of new findings and results
regarding customers’ attitude and behavior. Besides, the
majority of studies which investigated crowd-shipping
using survey data had a small sample size, with a median
of less than 200 respondents. This study benefits from a
larger sample which will allow more significant results to
be obtained and more insights to be drawn.
To investigate individual motivations for using a ridesharing system, the literature studies a limited number of
determinants, which most frequently are trust, environmental concern, economic benefits, or sense of community. The latter two are discussed in Bellotti’s paper that
highlights the motivation to make social connections and
self-interest motivations in sharing economic systems
(29). Taking inspiration from them, this study looks at
how motivation for saving money and for belonging to a
community impact the likelihood of being a crowdshipping user. Further, both direct and indirect effects of
various socio-demographic information and builtenvironment variables on the likelihood of being a
crowd-shipping user will be tested for. Finally, in comparison with the reviewed literature, this study has the
advantage of having one of the largest samples and will
be able to provide relatively more accurate results.
Data
The core data used in this study are obtained from an
online survey distributed among 800 individuals in June
2016 using Qualtrics survey software (www.qualtrics.com)
and disseminated on Amazon Mechanical Turks
(www.mturk.com). Participants were chosen from
California, Florida, Georgia, and Illinois as these four
states represent a high level of crowd-shipping use at the
national level. Out of 800 requests, 587 individuals completed the survey, which indicates a 67% return rate. The
Transportation Research Record 2673(7)
quality of the responses was controlled using two methods:
(a) adding two attention-test questions in the questionnaire
and (b) checking the speed of completion and repeated
logins. Screening the responses, it is found that 54 individuals failed to meet the two criteria. This resulted in 533
valid observations.
The questionnaire consists of five sections preceded by
an introduction of the crowd-shipping concept to participants, as the survey targets both users and non-users of
the crowd-shipping system. The exact wording is quoted
below:
‘‘Crowd-shipping involves using the general public to deliver
packages. The idea is to match people who need to ship a
package from an origin to a destination, with a driver planning
to commute with the same origin and destination. This system
is similar to ridesourcing and carpooling promoted by companies such as Uber and Blablacar. With some conditions, anyone can become a crowd-shipping courier in order to earn
additional income by delivering packages for the participants
in the crowd-shipping community. The system is generally
managed by a third party (a crowd-shipping company) which
provides the platform of communication (website or mobile
app). Crowd-shipping is frequently less expensive than traditional shipping methods.’’
The first section asks participants about their experience with parcel and crowd-shipping industries with
questions such as ‘‘Before taking this survey, were you
familiar with the concept of crowd-shipping?’’ or ‘‘On average, how many packages are you sending through a crowdsourced delivery service each year?’’
The second section presents to respondents a list of
nine stated preference scenarios randomly selected from
a full factorial design. Each scenario presents a delivery
context and asks survey participants to make a choice
between three crowd-shipping alternatives, one traditional delivery service alternative, and the alternative of
not sending the package at all. Further details regarding
this section are available in Punel and Stathopoulos (30).
The current study only accounts for the number of times
each participant selects a crowd-shipping option.
The third section examines respondents’ perceptions
of shipping attributes, as well as opinions of crowdshipping advantages and disadvantages, respectively.
The fourth section is comprised of attitude and preference statements toward crowd-shipping. This section
provides two categories of statements used in this
study which are highlighted as two motivations for
using sharing economic system in Bellotti et al.’s
work (29):
Sense of Community (SC): Statements about the
specific potential of crowd-shipping to build a
community as well as personal belief and
Punel et al
533
Table 2. Demographic Characteristics of Respondents
Demographic characteristics
Gender
Female
Male
Other
Age
Below 34
35 to 44
45 to 64
65 and older
Household annual incomeb
Less than $39,999
$40,000 to $79,999
More than $80,000
Education (.25 years old)
High school or less
Some college or Associate’s Degree
Bachelor’s Degree or higher
Survey
(count = 533)
U.S. Census Bureau (2012)a
59.10%
40.34%
0.56%
49.13%
50.87%
–
53.84%
22.33%
20.83%
3.00%
46.44%
14.19%
28.21%
11.16%
36.21%
37.53%
21.20%
–
–
–
10.13%
35.46%
54.41%
34.15%
28.36%
37.49%
Note: – = not applicable.
a
Data issued from the U.S. Census Bureau, Reported Internet Usage for Individuals 3 Years and Older, by Selected Characteristics: 2012.
b
Twenty-seven respondents did not share their household annual income.
motivation for belonging to a community in general. Sense of community is defined in this paper
as the membership to a group where its members
interact and help each other to improve their common well-being.
Monetary Saving (MS): Statements about the economic advantages of crowd-shipping and personal
motivations for saving money.
Each category is divided into two sub-categories: (1) a
list of items about individual motivations in the context
of crowd-shipping, and (2) a list of items about individuals’ motivations in general. This allows comparison of
respondents’ general attitude with how they apply in the
specific context of using crowd-shipping. 15 items were
included:
5 items related to the sense of community in
crowd-shipping systems (SCCS);
3 items related to the sense of community in general (SCGE);
3 items related to monetary saving in crowdshipping systems (MSCS);
4 items related to monetary saving in general
(MSGE).
These items were designed using 7-point Likert scales,
where respondents were asked to rate their level of agreement from ‘‘Strongly Disagree’’ to ‘‘Strongly Agree’’.
The fifth section includes socioeconomic and demographic information such as age, gender, employment
status, level of education, and annual household income.
Table 2 summarizes socioeconomic and demographic
information of the 533 valid respondents. The results show
that the sample is younger and more educated than the
overall United States (U.S.) population, and females are
more predominant than males by almost 20 points.
Looking at the crowd-shipping experience statistics, it is
found that 7.88% of the respondents have already used a
crowd-shipping service, which is twice as high as
Ghajargar et al. obtained from a similar study in Italy
(31). Just under half, 47.84%, are familiar with the crowdshipping concept. This latter data reveals a general lack of
awareness of the notion of crowd-shipping, also supported
by Marcucci et al.’s findings (24). Consequently, because
of their low proportion, current crowd-shipping users can
be considered as ‘‘early adopters’’ of the system.
To build the measurement model, the items of the
fourth section were used to identify the latent factors.
The structure was then enhanced by controlling for the
effects of respondents’ socioeconomic information and
the built-environment variables. The latter are extracted
from the EPA Smart Location Database and matched
with block-level indicators for survey responses. Table 3
depicts variables used in the analysis along with their
summary statistics.
Methodology: A Structural Equation Model
To explain push and pull factors in the adoption of crowdshipping, a structural equation model is developed. The
conceptual framework of the model is depicted in Figure
1. This framework aims to understand how attitudes
related to sense of community and saving money, positive
534
Transportation Research Record 2673(7)
Table 3. Description of the Variables Used in the Structural Equation Model
Type
Name
Items of Factor 1
Support Platform
SCCS1
SCCS2
SCCS3
Items of Factor 2
Community
Items of Factor 3
Finance
Respondent information
and stated choice
SCCS5
SCGE1
SCGE2
MSCS2
MSCS3
Gender—Male
Age \ 44
HHIncome \ 19k
Employment—FT
Shipping Experience
SP—Crowd
Built-environment
variables
Age Median
Working Pop.
Density
Job Access
Endogenous Variable
Crowd-shipping User
Description
Mean
Std. dev.
I believe crowd-shipping is a platform where its users, both
senders and drivers, can help each other.
I think crowd-shipping is a great way of creating a community of
drivers and people looking to send packages.
Crowd-shipping provides advantages for both drivers and
senders.
I would easily create relationships with other crowd-shipping
users if I were a member of such a community.
I like being part of a community.
I like being active in contributing to a community.
Crowd-shipping is a way of saving money.
The main advantage of crowd-shipping over traditional delivery
services is its attractive cost.
1: when the respondent indicates male as gender information | 0:
otherwise
1: when the respondent indicates an age lower than 44 | 0:
otherwise
1: when the respondent indicates an annual household income of
less than $19,000 | 0: otherwise
1: when the respondent indicates he/she is full-time employed |
0: otherwise
1: when the respondent indicates he/she sends more than 1
package a month | 0: otherwise
Number of times the respondent selected a crowd-shipping
option in the Stated Preference scenario (from 0 to 9)
Median age of the population
Proportion of population in working age
Gross residential density (HU/acre) on unprotected land
Jobs within 45 minutes auto travel time, time-decay (network
travel time) weighted (divided by 100,000)
1: when the respondent indicates he/she has already used crowdshipping systems | 0: otherwise
5.86
0.87
5.72
0.99
5.91
0.90
4.78
1.39
5.16
5.13
5.71
5.80
1.22
1.24
1.01
1.05
0.40
-
0.76
-
0.10
-
0.49
-
0.31
-
5.64
1.94
37.55
0.76
3.14
1.51
10.44
0.16
6.92
1.97
0.08
-
Note: SCCS = sense of community in crowd-shipping systems; SCGE = sense of community in general; MSCS = monetary saving in crowd-shipping systems;
Std. dev. = standard deviation.
attitudes towards crowd-shipping, socio-demographic
information, and built-environment variables impact
adoption of crowd-shipping. As shown in Figure 1,
Crowd-shipping User is the endogenous variable, which
represents whether an individual has already used a
crowd-shipping service in reality at the time of the survey.
The framework tests the following hypotheses:
Hypothesis H1: Viewing crowd-shipping as a support platform where users help each other
increases the likelihood of being a crowd-shipper;
Hypothesis H2: Having a high sense of community
increases the likelihood of being a crowd-shipper;
Hypothesis H3: Considering crowd-shipping’s
main advantage is to save money increases the
likelihood of being a crowd-shipper;
Hypothesis H4: Socio-demographic and builtenvironment variables affect both directly and
indirectly the likelihood of being a crowd-shipper.
The remainder of this section discusses (a) the factor
analysis used to create latent variables and (b) testing the
conceptual framework. R version 3.4.1. (32) is used along
with its packages lavaan (33) and psych (34) for the calculation and statistical analysis.
Factor Analysis
The factor analysis defines the factors of the measurement model, which add on to the items related to the attitudinal statements from the survey.
In a preliminary step, an item analysis is performed
to test each item versus the rest and remove the ones
which perform poorly. A visual technique is used by
plotting the Locally Weighted Scatterplot Smoother
(35) plots of each item against the rest, and it is checked
that the average of each item becomes higher as the
remaining item scores becomes higher. This translates
by a monotonically increasing line. This step leads to
removing two items.
To determine the appropriate number of latent factors, an Exploratory Factor Analysis is used. Several criteria are employed in tandem with critical interpretation
Punel et al
535
SUPPORT
PLATFORM
Socio-demographic
Information & Builtenvironment Variables
Socio-demographic
Information & Builtenvironment Variables
SCCS1
Socio-demographic
Information & Builtenvironment Variables
SCCS2
SCCS3
COMMUNITY
SCCS5
Socio-demographic
Information & Builtenvironment Variables
SCGE1
Crowd-shipping
User
SCGE2
FINANCE
MSCS2
MSCS3
Figure 1. The conceptual framework of the model.
for extracting and determining the content of the factors.
Cattell’s scree test is applied to have a preliminary visual
indication of the number of factors by looking for the natural break point in the data (36, 37). The proportion and
cumulative variance of the factors is also looked at and it
is checked that the proportion of variance of each factor is
above 10%. Finally, it is ensured that there are at least two
items with significant loadings (greater than 0.5) on each
factor, and that these variables, for each factor, share a
conceptual meaning which allows the factor to be defined.
All the criteria suggest adopting a three-factor structure,
the contents of which are presented in Table 4. The three
factors are defined as follows: (a) Support Platform, which
refers to an individual’s interest in the help aspect provided
in crowd-shipping service, (b) Community, which refers to
an individual’s sense of community and willingness of creating bonds with other members, and (3) Finance, which
refers to an individual’s willingness to save money when
using a crowdsourced delivery system.
A Confirmatory Factor Analysis is then run to validate the structure of the model and assess its goodnessof-fit. To estimate the model, the maximum likelihood
estimator is used with robust (Huber-White) standard
errors. Results of the latent constructs are presented in
Table 4, where the robust values are reported. Regarding
the overall fit of the model, the values for CFI and TLI
are greater than 0.95, however, the SRMR and RMSEA
are not below 0.08 suggesting a non-optimal fit.
Structural Equation Modeling
The structural equation model then regresses the crowdshipping user status against various combinations
of the latent factors SUPPORT PLATFORM,
COMMUNITY, and FINANCE. Both direct and indirect relationships are tested and the model with the best
fit and interpretability has a direct relationship between
FINANCE and user status, while the other factors have
an indirect association. The model is then completed by
testing for the different causal effects of the sociodemographic and built-environment variables on the
endogenous variable and the latent variables, and selecting the ones which are significant at the 90% confidence
interval and have an intuitive explanatory power.
Results of the final model are presented in Table 5, while
Figure 2 presents the overall final structure of the model.
To avoid cluttering, the figure items are omitted but
clearly listed in Tables 3 and 4. While the SRMR value
is still above 0.08, the CFI, TLI and RMSEA values outperform the cut-off values indicating a good model fit.
Results and Discussion
The results demonstrate that out of the three factors,
only FINANCE has a significant direct effect on the
crowd-shipping user likelihood. Its negative sign indicates that individuals who consider the main advantage
of crowd-shipping as saving money are less likely to use
536
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Table 4. CFA Results
Estimate
Std. err.
SUPPORT PLATFORM
SCCS1
1.000
–
SCCS2
0.953
0.050
SCCS3
0.938
0.057
FINANCE
MSCS2
1.091
0.055
MSCS3
0.819
0.067
Comparative Fit Index (CFI)
Tucker-Lewis Index (TLI)
Standardized Root Mean
Square Residual (SRMR)
Standardized
Estimate
Std. err.
0.927
0.847
0.900
COMMUNITY
SCCS5
SCGE1
SCGE2
0.994
0.746
0.956
0.935
0.221
Root Mean Square Error of Approximation (RMSEA)
Lower Bound 90% Confidence Interval
Upper Bound 90% Confidence Interval
1.000
1.187
1.220
–
0.052
0.052
Standardized
0.673
0.918
0.925
0.103
0.084
0.124
Table 5. SEM Results
Latent variables
Estimate
SUPPORT PLATFORM
SCCS1
1.000
SCCS2
0.921
SCCS3
0.909
FINANCE
MSCS2
0.750
MSCS3
0.587
Regression
Estimate
Factor FINANCE against
SUPPORT
0.897
PLATFORM
Working Pop.
–0.626
Job Access.
0.051
Std. err.
Standardized
–
0.048
0.054
0.936
0.854
0.906
0.049
0.043
0.976
0.762
Std. Err.
Standardized
0.098
0.672
0.216
0.022
–0.070
0.070
SP—Crowd
0.088
0.029
Factor SUPPORT PLATFORM against
COMMUNITY
0.278
0.041
Density
–0.012
0.005
SP—Crowd
0.126
0.020
Factor COMMUNITY against
Age Median
–0.009
0.004
Shipping
0.361
0.090
Experience
Overall Fit of the Model
Comparative Fit Index (CFI)
Tucket-Lewis Index (TLI)
Standardized Root Mean
Square Residual (SRMR)
0.120
0.264
–0.077
0.228
Estimate
SCCS5
SCGE1
SCGE2
Standardized
COMMUNITY
1.00
–
1.151
0.050
1.166
0.050
0.677
0.922
0.917
Estimate
Std. Err.
Standardized
0.011
–0.109
0.048
0.102
0.024
0.029
0.087
0.175
0.041
-0.064
0.040
0.010
–0.012
0.022
0.017
0.023
0.002
0.006
0.065
-0.070
0.075
0.247
–0.086
Crowd-shipping user against
FINANCE
–0.021
Gender—Make
Shipping
Experience
Age \ 44
HHIncome \ 19k
Employment—FT
Density
Job Access.
Std. err.
–0.092
0.164
0.922
0.905
0.118
Root Mean Square Error of Approximation (RMSEA)
Lower Bound 90% Confidence Interval
Upper Bound 90% Confidence Interval
the service. Regarding the result, hypothesis H3 can be
rejected as considering crowd-shipping’s main advantage
is to save money does not increase the likelihood of being
a crowd-shipper. Instead, SUPPORT PLATFORM has
an indirect effect on the use of crowd-shipping as it
0.061
0.053
0.069
positively impacts FINANCE. This means individuals
who believe that crowd-shipping systems represent a support platform with mutual help are more likely to maintain the view that crowd-shipping makes shipping more
affordable, thereby presenting only an indirect
Punel et al
537
COMM
COMMUNITY
SP - Crowd
Age Median
Survey
Results
SUPPORT
PLATFORM
Working
Pop.
Density
Crowd-shipping
User
FINANCE
Job
Access
Built-environment
Variables
Shipping
Experience
Employment
- FT
HHIncome
< $19k
Age < 44
Gender Male
Socio-demographic Variables
Figure 2. Final structural equation model.
Note: The items are not represented here (see Figure 1).
relationship to use. That means that belief in the platform’s support aspects is mediated by motivations to
derive economic benefits, when deciding to become a
crowd-shipping user. The positive sign suggests a rejection of hypothesis H1 as individuals who view crowdshipping as a support platform are indirectly less likely
to use such a system. Finally, people who have an elevated sense of community are more likely to accept the
mutual help aspect of the crowd-shipping system, but no
evidence was found that such an attitude has any direct
effect on the crowd-shipping use probability.
Consequently, hypothesis H2 cannot be confirmed or
rejected.
As far as the socio-demographic information is concerned, evidence is found of direct effects related to gender, age, employment, experience, and household
income. Men are more likely to be crowd-shippers, which
aligns with results from other studies related to the sharing economy (25, 38). People below the age of 44 are also
more likely to use a crowd-shipping system, as well as
full-time employed individuals who might prefer the convenience of the platform and do not have to adapt their
work schedule for pickup and delivery timings. The positive sign for the shipping experience variable shows that
people who regularly send packages using traditional
channels are more likely to try a crowd-shipping system.
This variable also impacts the COMMUNITY factor,
revealing that experienced senders tend to have a higher
sense of community. Finally, low household income has
a negative effect on crowd-shipping usage. Individuals
with low income are less likely to use crowd-shipping systems. The model does not reveal any indirect effects of
the socio-demographic information on the crowdshipping experience.
As far as the built-environment variables are concerned, density impacts crowd-shipping both positively
and directly. Crowd-shipping is more likely to be adopted
by people living in densely populated areas. Such environments are predisposed to the development of crowdsourced activities likely because of network effects.
Further, job accessibility has both direct and indirect
negative effects for the use of crowd-shipping, suggesting
that areas with high job accessibility are not the ideal
locations to develop crowd-shipping activities. These
results support the idea that crowd-shipping preferences
vary across locations and that the spatial distribution of
actors and characteristics of the network impacts the performance of crowd-shipping matching system (39, 14).
Furthermore, the working-age population has a negative
role on the factor FINANCE, meaning that working
people tend to less be attracted by the money-saving benefits of crowd-shipping systems. Consequently, this
variable indirectly affects positively the use of crowdshipping. Furthermore, the density variable has a negative effect on the factor SUPPORT PLATFORM. This
reveals that people living in densely populated areas are
less likely to believe in the mutual help aspect of crowdshipping platforms, which may not constitute their main
motivation for using a crowdsourced delivery system.
Both the findings from the socio-demographic information and built-environment variables confirm hypothesis H4 which supports that being a crowd-shipper is
directly and indirectly affected by the socio-demographic
and built-environment variables.
538
Conclusion
Crowd-shipping combines sharing economy and ondemand features to offer a new delivery solution relying
on internet platforms and crowd-carriers. The system
distinguishes itself from traditional delivery with its specific strengths and weaknesses, such as affordable delivery cost, convenience, and social interaction, versus
privacy and liability concerns, and reliability issues,
respectively. While crowd-shipping is not likely to
replace established traditional logistics companies, it
holds promise as a complementary shipping model
owing to its ability to attract new demand. Little is currently known about the profile and motivation of users.
Identifying the customer segments would help crowdshipping companies to better tailor their service to
address demand more effectively. Further, results of such
studies would also benefit research on the logistics and
operations side of crowd-shipping, which would be able
to build more realistic models informed by improved
behavioral assumptions.
This study attempted to gain an understanding of
crowd-shipping users’ motivations by analyzing their
socio-demographic characteristics and personal attitudes.
Their effects on crowd-shipping acceptance were tested
for through a structural equation model. This model was
developed using data from 533 respondents from an
online panel survey. Using the responses to attitudinal
statements, three factors were defined for the measurement model: (i) interest in reciprocal helping relationships in the crowd-shipping platform, (ii) sense of
community, and (iii) interest in crowd-shipping’s economic benefits. The direct and indirect effects of these
factors were then tested, along with the sociodemographic and built-environment variables to assess
the relationship to using crowd-shipping.
The model results suggest some unexpected findings.
Crowd-shipping use is more likely among respondents
who do not claim to be motivated by the service affordability, and simultaneously, the affordable price is not
viewed as the main advantage of crowd-shipping.
Similarly, through an indirect effect, it is shown that people who view crowd-shipping as a platform where members help each other are less likely to use the system.
While financial and community aspects do not seem to
be the main motivators for using crowd-shipping, users
do appear to be animated by other goals and preferences,
such as the convenience of the system. In line with expectations, looking at the socio-demographic profile, evidence is shown of a direct relationship for the following
features: men, full-time employed, individuals above 44
years of age, and experienced senders are more likely to
use crowd-shipping systems, in contrast to low-income
respondents. The built-environment variables of population and employment density also impact the
Transportation Research Record 2673(7)
development of crowd-shipping. Notably, higher population density and fewer accessible job opportunities lead
to more likely crowd-shipping adoption. From a business angle, findings encourage crowd-shipping companies not to emphasize the monetary advantage of the
system in their marketing campaigns but rather to
emphasize its convenience for controlling pick-up and
delivery conditions. Further, insights indicate that
crowd-shipping businesses should focus their operations
into dense residential areas to increase their activity.
On the whole, the structural equation model revealed
that it is challenging to find the causal structure of the
motivations to use crowd-shipping. In many cases the
actual users hold different, more critical, beliefs about
the service benefits than non-users. This experiential
change in values suggests the need to explore the adoption dynamics carefully as the user base expands.
Some caveats need to be noted so as to be addressed
in future studies. First, the low share of actual users in
the sample, as well as the general lack of awareness of
crowd-shipping, limit the generalizability of the results.
Second, because the sample is small (although it remains
larger than most similar studies), it is challenging to highlight all significant socio-demographic variables and
motivations. Third, while several types of crowd-shipping
businesses exist, the current study especially focused on
the deliveries for and made by private customers (9, 40).
The survey did not identify respondents who were business owners or reliant on logistics activities. Future
research should explore this feature to further help tailor
business strategies. Importantly, companies can have a
collective value orientation with social goals, similar to
the individuals in this research. The connection between
company needs and supply chain, customer base, and
value orientation merits further research. Finally, the
present research considered only two main motivations:
economic benefits and sense of community. Future studies should also consider other motivations and attitudes
highlighted in the literature, such as trust, openness to
innovation, and convenience.
Acknowledgments
This material is based upon work supported by the U.S. National
Science Foundation Partnerships for Innovation: Building
Innovation Capacity (PFI:BIC) Grant No. 1534138 Smart
CROwdsourced Urban Delivery (CROUD) System, and by the
Northwestern University Transportation Center (NUTC).
Author Contributions
The authors confirm contribution to the paper as follows—
study, conception, and design: AE, AP, AS; data collection:
AP, AS; literature review: AP; structural equation model: AE,
AP; interpretation of the results: AP; draft manuscript preparation: AE, AP, AS.
Punel et al
539
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