The “next day, free delivery” myth unravelled: Possibilities for sustainable last mile transport in an omnichannel environment

Abstract

Purpose
Currently, it is unclear how omnichannel retailers can create a last mile offer that is both attractive and sustainable from an economic and environmental point of view. The purpose of this paper is to explore to which extent consumers are willing to adopt last mile options that are more sustainable and how these options should be composed to remain attractive.

Design/methodology/approach
To this end, the authors surveyed a representative sample of Belgian consumers, using choice-based conjoint experiments, and analysed their preferences structures.

Findings
Consumers’ preference goes out to free, next day delivery to an address of choice, on regular office hours during the week. However, when free delivery and return are offered, consumers are willing to collect their orders themselves or wait longer for their orders to arrive.

Practical implications
The research findings are important for retailers that (plan to) operate an omnichannel model. For omnichannel retailers with a dense store network, the results indicate that consumers accept their store network as pick-up and return locations, allowing retailers to create a more efficient and sustainable supply chain in which their online and offline activities can be combined.

Originality/value
The research findings contribute to current literature and practice by combining “planet” and “profit” components of sustainability in last mile transport and applying it in the novel omnichannel environment.

Full text

International Journal of Retail & Distribution Management
The “next day, free delivery” myth unravelled: Possibilities for sustainable last
mile transport in an omnichannel environment
Heleen Buldeo Rai, Sara Verlinde, Cathy Macharis,
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To cite this document:
Heleen Buldeo Rai, Sara Verlinde, Cathy Macharis, (2018) "The “next day, free delivery”
myth unravelled: Possibilities for sustainable last mile transport in an omnichannel
environment", International Journal of Retail & Distribution Management, https://doi.org/10.1108/
IJRDM-06-2018-0104
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The “next day, free delivery”
myth unravelled
Possibilities for sustainable last mile transport
in an omnichannel environment
Heleen Buldeo Rai, Sara Verlinde and Cathy Macharis
Vrije Universiteit Brussel, Brussels, Belgium
Abstract
Purpose –Currently, it is unclear how omnichannel retailers can create a last mile offer that is both attractive
and sustainable from an economic and environmental point of view. The purpose of this paper is to explore to
which extent consumers are willing to adopt last mile options that are more sustainable and how these
options should be composed to remain attractive.
Design/methodology/approach –To this end, the authors surveyed a representative sample of Belgian
consumers, using choice-based conjoint experiments, and analysed their preferences structures.
Findings –Consumers’preference goes out to free, next day delivery to an address of choice, on regular
office hours during the week. However, when free delivery and return are offered, consumers are willing to
collect their orders themselves or wait longer for their orders to arrive.
Practical implications –The research findings are important for retailers that (plan to) operate an
omnichannel model. For omnichannel retailers with a dense store network, the results indicate that consumers
accept their store network as pick-up and return locations, allowing retailers to create a more efficient and
sustainable supply chain in which their online and offline activities can be combined.
Originality/value –The research findings contribute to current literature and practice by combining
“planet”and “profit”components of sustainability in last mile transport and applying it in the novel
omnichannel environment.
Keywords Sustainability, Consumer behaviour, Electronic commerce, Last mile, Omnichannel retail
Paper type Research paper
1. Introduction
Research on last mile transport investigates the final part of the supply chain from the last
distribution centre, consolidation point or local warehouse. It focusses on the ways in which
products reach their final destination in the consumer market (Xiao et al., 2017). In times of
intensifying retail digitalisation, consumers order more and more products over the internet.
As these products are often delivered to consumers’homes, concerns on last mile
sustainability are rising (Allen, Piecyk and Piotrowska, 2017; Allen, Piecyk, Piotrowska,
McLeod, Cherrett, Ghali, Nguyen, Bektas, Bates, Friday, Wise and Austwick, 2017). In their
review of environmental implications of online business-to-consumer (B2C) commerce,
Mangiaracina et al. (2015) demonstrate that transport has the greatest impact on
sustainability. They refer to last mile delivery as most important transport activity, as there
are little differences between online and conventional shopping for most of the other
transport activities involved. Accordingly, last mile transport is considered as one of the
biggest challenges in B2C e-commerce (Savelsbergh and Van Woensel, 2016).
Next to environmental concerns, last mile transport is also very costly to organise for
logistics service providers that carry out these deliveries. Depending on several factors, the
last mile accounts for 13–75 per cent of total supply chain costs (Gevaers et al., 2009).
Honeywell (2016) estimates that 50 per cent of total costs are attributed to the last mile.
International Journal of Retail &
Distribution Management
© Emerald Publishing Limited
0959-0552
DOI 10.1108/IJRDM-06-2018-0104
Received 7 June 2018
Revised 19 June 2018
3 July 2018
25 September 2018
25 September 2018
Accepted 28 October 2018
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0959-0552.htm
This work is supported by the Innoviris Anticipate programme: prospective research for Brussels-Capital
Region, and the retail group. The authors would like to thank the retail group for the opportunity to carry
out this research and Philippe Lebeau for his much appreciated help and suggestions.
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These high costs are partly caused by retailers who promise to serve their customers in two
days, the next day or even the day of purchase itself. Such short delivery terms hinder
efficient routing and consolidation of parcels (Savelsbergh and Van Woensel, 2016). Low
delivery location density and logistics facilities remote from the consumer market add to the
inefficiencies (Reyes et al., 2017). As a consequence, last mile delivery of a product is between
5 and 23 times more expensive for retailers than product purchases in-store (Allen et al.,
2017). What is more, consumers are largely unwilling to pay for these delivery services.
With an increasing number of retailers that move towards instant and on-demand
deliveries, efficiency and sustainability problems might only become larger.
As there is both an environmental and economic need, more efficient and sustainable last
mile delivery concepts are being explored. In their review of the impact of home delivery on
urban freight transport, Visser et al. (2014) suggest environmentally friendly vehicles
(e.g. electric vehicles and cargo-bicycles) and consolidation. Consolidation makes deliveries
more efficient, as more drops per trip reduce the number of vehicle kilometres per delivery
(Visser et al., 2014). There are several ways to foster consolidation, e.g. longer delivery terms
(Boyer and Prud’homme, 2009) and use of alternative delivery addresses (e.g. parcel pick-up
points and lockers) (Edwards, McKinnon and Cullinane, 2010; Edwards, McKinnon,
Cherrett, McLeod and Song, 2010). Within the recently conceptualised omnichannel retail
model, also retailers’stores serve as pick-up location (Gao and Su, 2016).
Omnichannel retail implies that online channels, such as web-shops, and offline channels,
such as physical stores, are integrated (Verhoef et al., 2015). For consumers, this means that
they can use various channels throughout their shopping journey in a flexible, convenient
and seamless way that matches their preferences and needs (Peltola et al., 2015;
Juaneda-Ayensa et al., 2016). For omnichannel retailers, channel integration provides a
response to the fierce competition from online-only players and creates more loyal and
profitable customers (Nash et al., 2013; Cao and Li, 2015). Using their store network allows to
reduce the number of expensive home deliveries and increase the overall efficiency of their
supply chain (Buldeo Rai et al., 2017).
The omnichannel retail model is gaining popularity among retailers. Similar to pure
online retailers, who are under pressure to provide efficient delivery in terms of speed, price,
service and quality (Conlumino for Barclays, 2014), omnichannel retailers need to figure out
how to organise last mile transport flows to their customers, using their store network
(Hübner et al., 2016). Currently, it is unclear how omnichannel retailers can create a last mile
transport offer that is both attractive from a customer point of view and sustainable from an
environmental point of view. National and international reports demonstrate the importance
of free and fast delivery (Comeos, 2017; MetaPack, 2016), but consumers’acceptance of more
sustainable last mile transport options has not been explored. As consumers are not willing
to compromise on quality, cost and convenience when making environment-friendly choices,
a comprehensive and industry-specific understanding of consumers’decision-making
process is important (Narula and Desore, 2016).
To this end, we set up a survey among a representative sample of Belgian consumers
using choice-based conjoint experiments. By analysing consumers’preference structures,
we identify which last mile attributes need to be combined to reach sustainability from a
“planet”and a “profit”point of view.
2. Literature review
Two last mile transport options are commonly offered to consumers when they order
products online: delivery at home (or any other address of choice) and collection at a local
pick-up point or locker. From an environmental point of view, home delivery is considered
the least favourable option (Mangiaracina et al., 2015). Several sustainability issues are
raised. First, delivery rounds are organised during regular office hours. Most consumers are
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at work during this time of day, which results in failed deliveries (Visser et al., 2014). Exact
percentages on the share of product deliveries that fail are scarce and inconsistent.
According to Edwards et al. (2009), failure rates can go from as low as 2 per cent to
30 per cent. Other figures are recorded in the UK: 13–14 per cent (IMRG, 2014), 25 per cent
(McLeod et al., 2006), 30 per cent (Fernie and Sparks, 1999) and even 60 per cent
(Retail Logistics Task Force, 2001). In the Netherlands, deliveries fail in 25 per cent of the
orders (Van Duin et al., 2016), while the Belgian postal organisation reports 14 per cent
(Gijsbrechts, 2017). The differences in percentages largely depend on logistics service
providers’policies with receivers that are not at home. In general, orders that could not be
delivered are dropped off at receivers’neighbours or at a local pick-up point. However, in
12 per cent of the cases such orders are offered a second time to receivers’homes (Visser
et al., 2014). This re-delivery process can be repeated up to four times (Van Duin et al., 2016).
In any case, delivery failure rates increase as home delivery grows (Weltevreden, 2008).
Second, retailers often offer next day delivery as part of their standard service. Fast
delivery reduces the opportunity to consolidate orders and organise efficient delivery routes
(Allen, Piecyk and Piotrowska, 2017), leading to an increase in vehicles and vehicle
kilometres (Verlinde et al., 2012). Similar to figures on delivery failure, knowledge on load
rates of delivery vehicles is limited. General freight studies refer to percentages less than 30
per cent in an urban context (Gebresenbet et al., 2011) and less than 50 per cent in a non-
urban context (McKinnon and Piecyk, 2009). Taking both a volume and a weight
perspective, it is commonly acknowledged that vehicles dedicated to home delivery are not
fully loaded (Allen, Piecyk and Piotrowska, 2017; Allen, Piecyk, Piotrowska, McLeod,
Cherrett, Ghali, Nguyen, Bektas, Bates, Friday, Wise and Austwick, 2017). Moreover,
vehicles often fail to collect additional volume on their return to the warehouse, resulting in
empty running (Edwards et al., 2011).
Third, home deliveries are mostly carried out with light goods vehicles or vans. These
vehicles consume more fuel and release more emissions per metric ton moved than larger
vehicles (Allen and Browne, 2010). In the UK, the number of licensed vans has increased by
70 per cent over the period from 1995 to 2015 (Allen, Piecyk and Piotrowska, 2017; Allen,
Piecyk, Piotrowska, McLeod, Cherrett, Ghali, Nguyen, Bektas, Bates, Friday, Wise and
Austwick, 2017). The number of vans in Belgium has grown by 55 per cent from 1991 to
2013, while the number of trucks remained stable (Strale et al., 2015). The rise in online
shopping is stated as one of the key reasons behind this evolution (Allen, Piecyk and
Piotrowska, 2017; Allen, Piecyk, Piotrowska, McLeod, Cherrett, Ghali, Nguyen, Bektas,
Bates, Friday, Wise and Austwick, 2017).
To respond to these challenges, innovative variations on regular home delivery are being
tested. Notable examples to avoid delivery failure include personal reception boxes (Punakivi
and Tanskanen, 2002), in-car delivery (Reyes et al., 2017) and in-fridge delivery (Bauerová and
Klepek, 2017). Such innovations rely on technological smartlock solutions to access boxes, car
trunks and even houses. Another solution to increase first-time delivery success entails to
offer a pre-agreed appointment or time slot in which the delivery takes place (Van Loon et al.,
2015). However, this option is not provided often by logistics service providers as it
complicates their routing schedules (Edwards, McKinnon and Cullinane, 2010).
Similarly, solutions have introduced to increase parcel consolidation. One of these
solutions combines efficient routing programmes with delivery-term flexibility from the
consumer’s side. Instead of offering next day delivery by default, retailers offer their
customers the option to pick a time slot in which deliveries to their neighbourhood are
already scheduled (Cullinane, 2009). This solution opposes the perceived need of companies
to deliver faster and faster (Reyes et al., 2017). Although consumers’waiting time increases,
it enables more sustainable deliveries and raises awareness of sustainability issues related
to delivery at home.
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Another solution to increase consolidation is created by parcel pick-up points and
lockers, which are mostly located in busy and/or residential areas. Pick-up points are
manned and organised in small local shops while lockers are unmanned and serviced by
technological solutions such as QR-codes and mobile phones (Visser et al., 2014). The main
advantage is that 100 per cent of the parcels are delivered (Van Duin et al., 2016), while
providing consumers with more locations and time slots to choose from when picking up
their parcels (Xiao et al., 2017). By collecting parcels within a certain neighbourhood, such
points enable consolidation and efficient routing of delivery vehicles (Allen, Piecyk and
Piotrowska, 2017). The negative environmental impact of parcel delivery is even more
reduced when consumers visit the pick-up point on foot or by bike and/or combine this trip
with other purposes (Xiao et al., 2017).
A similar solution to pick-up points and lockers is offered by so-called
omnichannel retailers. Next to delivery at home and pick-up at regular parcel points,
omnichannel retailers allow consumers to pick-up their orders in one of their stores
(Gao and Su, 2016). For omnichannel retailers, integrating their store network allows to
reduce the number of expensive home deliveries and increase the overall efficiency of their
supply chain (Hübner et al., 2016). Moreover, consumers are found to prefer this option
over regular pick-up points, which are managed by logistics service providers. This is
because stores offer advantages such as possibilities to return products and make
additional purchases, immediate refund for product returns and specialised product
advise (Buldeo Rai et al., 2017).
Alternatives to vans are ubiquitous in urban environments. Most logistics service
providers are experimenting with sustainable alternatives such as bikes and electric
vehicles. Such vehicles offer many advantages. For example, cargo-bikes are not subjected
to congestion and allow to guarantee delivery time accuracy (Gruber et al., 2014). In times of
increasing environmental awareness at the urban level, electric vehicles often receive more
favourable time windows and can access the city’s low emission zones (Quak et al., 2016).
Despite many innovations that aim to improve sustainability in last mile transport,
actual implementation is still limited. Part of the explanation is that retailers are not offering
sustainable options that consumers can choose from, acting as a so-called “filter”on the
product and service offer (Kostadinova, 2016). Young et al. (2010) found that consumers
make green purchases only if available in a range of options, while Theotokis and
Manganari (2015) recommended a system in which the most sustainable option is offered as
“opt-in”, to which consumers can deviate by explicitly “opting-out”. Another part of the
explanation points to consumers that fail to make sustainable choices even when given the
option. Research found that even if consumers are inclined to make an environment-friendly
purchase, they are not willing to compromise on quality, cost and convenience (Narula and
Desore, 2016). Hence, a comprehensive and industry-specific understanding of consumers’
decision-making process is important.
Various studies have investigated what consumers find important in last mile
transport. These studies show that consumers prefer free and fast delivery at home
(Comeos, 2017; MetaPack, 2016). Although consumers increasingly attach significance to
environmental sustainability in their purchase activities (Gonzalez-Lafaysse and
Lapassouse-Madrid, 2016; Quarshie et al., 2016; Bask et al., 2013), the topic has
received less attention in relation to consumers’last mile transport decisions. Currently,
environmental concerns and arguments seem to play a minor role (Lagey et al., 2016).
With this research, we explore to which extent consumers are willing to adopt last mile
options that are more sustainable and how these options should be composed to remain
attractive. We use choice-based conjoint analysis to examine how consumers trade-off
collection and delivery attributes in their choice of last mile transport options when they
make purchases online.
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3. Methodology
3.1 Choice-based conjoint analysis
Choice-based conjoint analysis is a stated preference technique that simulates a choice
situation involving a set of competing alternatives. Various alternatives are composed based
on a common set of attributes and presented to respondents. Given this set of attributes,
respondents select the option that matches best with their preferences. By observing the
preferred alternatives, choice-based conjoint analysis allows to estimate the trade-offs that
respondents make among the various attributes. The method has a long track record in
measuring preferences and understanding choices and trade-offs that consumers make
(Louviere, 1994). A major advantage of choice-based conjoint analysis is the realism it
provides in modelling consumers’decision-making processes (Hair et al., 2010). The method
has been applied in retailing since the early work of Green and Srinivasan (1978). For
investigating the importance of sustainability in consumers’decision-making, it is the most
commonly used method (Bask et al., 2013; Lebeau et al., 2016).
The selection of attributes is critical in choice-based conjoint design. According
to the literature review, many aspects influence the choice for last mile transport options.
According to Hair et al. (2010), a maximum of six attributes is recommended. To identify
the most relevant attributes, we analysed consumer preference surveys and
validated a final attribute set in focus groups with consumers. The survey results were
reported in 35 national and international reports and published by various parties:
logistics service providers (e.g. UPS, PostNord), communication agencies (e.g. Walker
Sands, Bizrate Insights), financial agencies (e.g. Barclays), consulting agencies
(e.g. McKinsey, KPMG) and associations (e.g. IMRG, Comeos). We collected and
categorised all aspects of importance to consumers in last mile delivery in a spreadsheet
file. Six focus groups were organised in June and July 2017 in three major Belgian cities:
Brussels, Ghent and Antwerp. Each focus group counted four respondents and lasted
approximately 3 h. These respondents were equally distributed in terms of motivation,
frequency and experience with online shopping, their perception of sustainability and also
age, gender and language (either French or Dutch). A topic list guided the conversation in
a semi-structured way and introduced several topics, including the respondents’online
shopping journey, perception of the online retail landscape, perception of and expectations
about last mile delivery and the future of online retailing. It is considered good practice to
introduce focus group discussions at the beginning of a research project that aims to
identify factors that influence behaviour, motivation, opinions or feelings and ideas that
people have (Krueger and Casey, 2000). Ultimately, we selected four attributes: “delivery
price”,“delivery term”,“delivery reception”and “return possibility”. Delivery location and
delivery time were grouped into one attribute or “composite factor”(Green and Srinivasan,
1978) that we named “delivery reception”, as both aspects are linked. For example,
delivery in a parcel locker allows for 24/7 pick-up possibilities and a slotted 2 h delivery
timeframe is only relevant for delivery at home (or any other fixed address). Similarly, the
“return possibility”attribute combines both the return location and price.
Several last mile transport aspects were ultimately excluded from our final selection as
they were considered less relevant for consumers: delivery information, delivery vehicle,
delivery flexibility, executing logistics service provider and aspects related to sustainability.
Remarkably, our focus groups showed that consumers do not feel responsible for enhancing
sustainability but instead expect businesses –including retailers and logistics service
providers –to act in a sustainable way.
Table I lists our final set of attributes and their possible values or “levels”. These levels
were selected to reflect a realistic last mile offer in omnichannel retail. Similarly to
attributes, the number of levels should be limited to ensure an efficient design. Moreover,
levels need to be both communicable and actionable (Hair et al., 2010). In this respect,
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communicable levels enable respondents to understand them in the same way and
evaluate them correctly, while actionable levels allow them to be put into practice.
As some attributes could be unclear for respondents, specifically “loyalty programme”
and “time slot”, we added a short description below every choice task. A loyalty
programme is offered by the retail group, based on purchase frequency and/or purchase
amount, and offers free deliveries and returns. The time slot represents a period of time in
which the delivery takes place, in this case of 2 h.
3.2 Utility computation
Different estimation methods are available to model preferences using the collected data.
We applied the multinomial logit model, which is the most frequently used model in
choice-based conjoint studies. This model is highly versatile and based on sound theoretical
assumptions (Rao, 2014). In this way, consumer utility values are calculated. Utility values
indicate the relative importance of attributes’levels and illustrate the extent of desirability
for a certain attribute level: the higher the utility, the more desirable the attribute level.
Levels with high utilities have a large positive impact on influencing respondent’choices
(Hair et al., 2010).
3.3 Survey sample and design
A large retail group in Belgium is expanding its online activities for non-food products.
In accordance with the omnichannel retail model, its online channel will be integrated in the
retail group’s dense store network. For the group, sustainability and customer service are
key values and drive internal and external actions and operations. Therefore, last mile
transport options offered to their customers should be attractive from a consumer point of
view and sustainable from a “green supply chain”point of view. This is important, as
sustainability initiatives ultimately depend on customer support (Quarshie et al., 2016). The
green supply chain concept integrates environmental considerations into supply chain
management, including product design, manufacturing processes and delivery of products
to consumers (Srivastava, 2008).
The choice-based conjoint analysis is based on a survey conducted among a sample of
one-thousand consumers. The sample is representative for the Belgian population according
to age, sex, degree, language, family composition and social class (CIM, 2017). The survey
Attributes
Delivery price Delivery term Delivery reception Return possibility
Levels
Free Within two hours Address of choice, during
the week (9u-18u)
Free, retail group’s store
(during opening hours)
Free as from €25 Tomorrow Address of choice, during
the week (18u-22u)
Free, pick-up point
(during opening hours)
Free as from €50 Day after tomorrow Address of choice, during
the weekend (9u-18u)
Free, parcel lockers (24/7)
Free as from €75 Within 1–3 days Address of choice, during
two-hour time slot
€2, retail group’s store
(during opening hours)
€2.95 Within 3–5 days Retail group’s store
(during opening hours)
€2, pick-up point
(during opening hours)
€5.95 Minimal 3 days, but
delivery date of choice
Pick-up point (during
opening hours)
€2, parcel lockers (24/7)
Free with a
loyalty
programme
Minimal 5 days, but
delivery date of choice
Parcel lockers (24/7) Free with a loyalty programme, retail
group’s store (during opening hours)
Table I.
Attributes and levels
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was administered online by using Sawtooth software (www.sawtoothsoftware.com/).
Web-based surveys are efficient and convenient (Bask et al., 2013). It allowed us to reach an
appropriate sample size and sample composition within a limited timeframe. To reach the
envisioned target group, three selection criteria were applied: all respondents are older than
18, made an online purchase in the last year and endorse the retail group. In total, 13 per cent
of respondents were rejected from the final sample because of this third criterion.
Accordingly, they did not receive access to the remainder of the survey. The data collection
was in collaboration with a recognised market research company (iVOX). Preceded by a
technical pre-test with 100 respondents, the survey ran two weeks (from 18 September 2017
until 29 September 2017).
We organised the survey in three parts. In the first section, screener questions were
asked to reject respondents based on the previously defined criteria. This part also
contained questions on socio-demographics to compose the sample. The second section was
dedicated to behaviour, preferences and experiences with regard to online shopping:
purchase frequency, product types, online stores and order collection and delivery. In this
section, we also proposed statements related to last mile transport: 12 on innovations
(including crowd logistics) and 9 on sustainability (including electric vehicles). The third
section comprised the on-screen choice tasks, providing a realistic last mile offer from which
respondents could choose. To generate the various choice tasks, it was necessary to make
several decisions concerning the choice-based conjoint design: number of alternatives,
number of choice tasks and the method for generating the choice tasks. Figure 1 illustrates
an example of a choice task included in the survey.
First, the number of alternatives presented in a choice task needs to be decided.
More alternatives provide richer trade-offs but reduce the design’s efficiency.
An increasing number of alternatives also require more efforts by respondents.
Following the recommendation by Hair et al. (2010), we include three alternatives per
choice task. Second, similar considerationshavetobemaderegardingthenumberof
choice tasks. Generally recommended is not to exceed 10–15 choice tasks, as it does not
provide additional insights into the preference structure of respondents (Hoogerbrugge
and van der Wagt, 2006). We decided to present respondents with eight choice tasks,
given that the survey also included additional questions. Third, choice tasks are composed
Figure 1.
Screenshot of
a choice task
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by combining levels from each attribute. In our design, no pairs of levels were prohibited
from occurring together. The method for choice task generation needs to take two key
principles into account: orthogonality and balance (Hair et al., 2010). Orthogonality of the
design means that no correlation should exist among the levels of an attribute. The
balance of the design means that each level of an attribute should appear the same number
of times. We used the balanced overlap option provided by Sawtooth software to generate
the choice tasks.
4. Results
4.1 Attitudes of consumers
To evaluate consumers’attitudes towards sustainability in last mile transport, we proposed
nine statements. These statements capture general attitudes (statements 1–3) and attitudes
towards delivery characteristics with a positive environmental effect (statements 4–9).
These delivery characteristics comprise the use of parcel pick-up points (Edwards,
McKinnon and Cullinane, 2010; Edwards, McKinnon, Cherrett, McLeod and Song, 2010),
consolidation of parcels and longer delivery terms (Boyer and Prud’homme, 2009) and the
use of environmentally friendly vehicles (Visser et al., 2014). Figure 2 illustrates responses
on these statements. These responses are useful for interpreting the choice-based conjoint
analysis. Moreover, attitudinal insights are important, as intentions to perform a certain
behaviour can be influenced by attitudes (Azjen, 1991).
The results of the survey show that approximately half of consumers take their
environment into account when making a purchase and agree that achieving less vehicle
kilometres for last mile deliveries is important. Consumers attach more importance to
reducing vehicle kilometres in general (52.5 per cent) as compared to reducing vehicle
kilometres in their specific neighbourhood (48.5 per cent). Therefore, we assume that assume
that consumers experience limited nuisances from local delivery activities. Nevertheless, in
line with Macharis and Milan (2015), the results show that consumers are concerned about
the overall negative impacts of these activities.
A reduction in vehicle kilometres is feasible by decreasing home deliveries in
favour of pick-up points and increasing delivery times. Survey results indicate that 56.2 and
As far as possible, I take my environment into
account when I make a purchase 17.2 37.4 45.4
13.9 33.7 52.4
14.9 36.6 48.5
17.1 26.7 56.2
28.9 26.5 44.6
70.9
18.8
10.3
17.4
57.1
55.2 30.3
29.1
60.5
22.1
13.8
14.5
0%
Strongly disagree and disagree Neutral Agree and strongly agree
10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
It is important for me that less kilometres are
driven in my neighbourhood for my parcels
It is important for me that less kilometres are
driven for my parcels
I am willing to collect my parcles in a parcel point
if less kilometres are driven
I am willing to wait longer for my parcels to arrive
if less kilometres are driven
It is important for me that the products that I
order together, are delivered together
I am willing to wait longer for my parcels, if my
parcels are delivered together
I am willing to pay more for a sustainable
delivery with an electric vehicle
I am willing to pay more for a sustainable
delivery with a cargo-bicycle
Note: All statements were measured on a Likert-type scale ranging from 5= strongly agree to
1= strongly disagree
Figure 2.
Consumers’attitudes
towards sustainable
last mile transport
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44.6 per cent, respectively, of consumers are willing to contribute to this. However,
consumers are not willing to pay for deliveries that employ more sustainable
alternatives to standard delivery vans, such as electric vehicles (57.1 per cent) or
cargo-bicycles (55.2 per cent). Research shows that consumers tend to resist new
technologies that are considered alien or unproved (Egbue and Long, 2012). In line with
deliveries with conventional vehicles (Allen, Piecyk and Piotrowska, 2017; Allen,
Piecyk, Piotrowska, McLeod, Cherrett, Ghali, Nguyen, Bektas, Bates, Friday, Wise and
Austwick, 2017), consumers’willingness to pay is low. In total, 70.9 per cent of consumers
want all products of the same order delivered together, but only 60.5 per cent is
prepared to wait longer.
Among the survey results, we detect a high percentage of neutral responses to the
statements (29 per cent on average). Consequently, we assume that interest in and/or
knowledge about sustainability in last mile delivery among consumers is low. Research
has shown that knowledge is an important predictor of green consumer behaviour
(Kostadinova, 2016). Possibly, it indicates that (a part of ) consumers can be convinced to
make more sustainable choices. This interpretation is in line with the green and social
delivery report published by B2C Europe (2018), which demonstrates that many
consumers lack knowledge on the environmental impact of deliveries, but are willing to
choose sustainable alternatives when negative impacts are explained.
4.2 Consumers’choice behaviour
As the findings reported in this section were analysed for the whole sample, analysis is
based on 8,000 choice tasks. The examined model was statistically significant relative to the
fixed model, using a χ
2
-test (po0.001). Consequently, respondent choices are significantly
affected by the attribute composition presented (Hosmer et al., 2013). Table II shows the
utility of attribute levels and the relative importance of attributes in percentages. Together,
these findings represent what consumers view as best last mile option.
The most important attribute to consumers is delivery price (53.47 per cent).
The second most important feature is return possibility (20.21 per cent), followed by
delivery term (13.67 per cent) and delivery reception (12.64 per cent). Consumers’
preference goes out to free, next day delivery to an address of choice, on regular
office hours during the week and with a free return possibility in a local pick-up point.
This agrees with current preference figures of collection and delivery alternatives in
Belgium (Comeos, 2017).
Next to free delivery, consumers indicate a positive preference for a minimal purchase
amount of €25 that allows free delivery, the lowest purchase amount among the price levels,
and free delivery using a loyalty programme. This finding is in accordance with Xing et al.
(2010), who point out the increasing price sensitiveness of consumers in the retailing market.
Loyalty programmes’importance has increased in the last few years (Hagberg et al., 2016).
Internationally, consumers are found to use and appreciate such programmes and want free
or quick delivery as a reward (MetaPack, 2016). At the time of research, loyalty programmes
were just gaining ground in Belgium. Consumers avoid high minimal purchase amounts
(€75) and high delivery prices (€5.95).
The results for the delivery-term attribute show a preference for levels that allow faster
deliveries, including delivery tomorrow, day after tomorrow and within one to three days.
Preference decreases as delivery terms increase. Most unfavourable to consumers is a
delivery term that can take at least five days, despite the possibility to freely choose a
delivery date within this term. Clearly, consumers do not mind unknown delivery dates.
Accordingly, a study found that 73 per cent of Belgian consumers do not consider this as a
limitation of online shopping (Bpost, 2017). Moreover, the results indicate that consumers
have no preference for instant orders that are delivered within 2 h, which contradicts
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recently formulated expectations (Dablanc et al., 2017). Also contrary to assumptions made
by Agatz et al. (2013), consumers have no distinct preference for slotted deliveries.
For receiving deliveries, consumers prefer an address of choice. Only small preference
differences are detected among the exact delivery times. Delivery during the week or the
weekend, during regular office hours or after and during a slotted two-hour or unknown
timeframe, consumers appear to be indifferent. In comparison, they avoid collecting parcels
in one of the retail group’s stores or parcel locker boxes.
The results for the return possibility attribute point out that consumers strongly prefer
to return unwanted or faulty orders free of charge, or free by employing a loyalty
programme. Returning goods in a pick-up point is valued most, next to stores of the retail
group or parcel locker boxes. Consumers avoid paying for their order returns, in particular
when the return location is a locker.
In accordance with the above analysis, delivery price is by far the most important factor
in consumers’choice behaviour for last mile options, the other attributes are less sensitive.
Creating a last mile offer that is both attractive and sustainable requires investigating the
trade-offs that consumers make in a more detailed way. To this end, we carry out market
simulations. Simulation is one of the key features of conjoint analysis (Rao, 2014). It is used
to predict individual choices under hypothetical scenarios, entered in the simulator as full-
profile descriptions. Individuals’utility functions are used to compute preferences for each
of the competing items (Green and Srinivasan, 1978). In this way, simulation helps in
answering various “what-if”questions based on conjoint data (Rao, 2014).
Attributes Attribute levels
Utility of
attribute levels SE
Relative attribute
importance (%)
Delivery
price
Free 1.24960 0.03098 53.47
Free as from €25 0.55657 0.03091
Free as from €50 −0.07456 0.03358
Free as from €75 −1.02177 0.04448
€2.95 −0.17266 0.03399
€5.95 −1.03270 0.04484
Free with a loyalty programme 0.49552 0.03093
Delivery
term
Within 2 h −0.08625 0.03471 13.67
Tomorrow 0.22022 0.03301
Day after tomorrow 0.14189 0.03310
Within 1–3 days 0.18667 0.03288
Within 3–5 days −0.07804 0.03418
Minimal 3 days, but delivery date of choice −0.02114 0.03388
Minimal 5 days, but delivery date of choice −0.36335 0.03571
Delivery
reception
Address of choice, during the week (9u-18u) 0.20582 0.03335 12.64
Address of choice, during the week (18u-22u) 0.14333 0.03305
Address of choice, during the weekend (9u-18u) 0.08163 0.03360
Address of choice, during two-hour time slot 0.13788 0.03320
Retail group’s store (during opening hours) −0.20334 0.03515
Pick-up point (during opening hours) −0.03140 0.03364
Parcel lockers (24/7) −0.20334 0.03591
Return
possibility
Free, retail group’s store (during opening hours) 0.24136 0.03288 20.21
Free, pick-up point (during opening hours) 0.41717 0.03240
Free, parcel lockers (24/7) 0.23179 0.03257
€2, retail group’s store (during opening hours) −0.30370 0.03539
€2, pick-up point (during opening hours) −0.25181 0.03526
€2, parcel lockers (24/7) −0.44561 0.03651
Free with a loyalty programme, retail group’s
store (during opening hours)
0.11078 0.03304
Table II.
Attribute levels
utilities and relative
attribute importance
percentages
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We undertake this market simulation using several scenarios that we developed
based on consumers’most preferred last mile option that acts as base scenario. These
scenarios are formulated as more sustainable variations on the base scenario. To guarantee
attractivity of the last mile options, we retain free of charge order delivery and return.
Six scenarios are tested:
(1) Scenario 1 is the base scenario consisting of next day delivery on an address of
choice during the week (9u-18u), with return possibility in a pick-up point.
(2) In scenario 2, orders are delivered on an address of choice during the week (9u-18u)
and returned in a pick-up point, although consumers have to wait one to three days.
(3) In scenario 3, orders are delivered on an address of choice during the week (9-18u)
and returned in a pick-up point, although consumers have to wait three to five days.
(4) In scenario 4, orders are delivered next day and returned in a pick-up point, although
consumers need to collect their orders in a pick-up point.
(5) In scenario 5, orders are delivered next day and returned in a pick-up point, although
consumers need to collect their orders in the retail group’s store.
(6) In scenario 6, orders are delivered next day on an address of choice during the week
(9u-18u), although consumers need to return their orders in the retail group’s store.
The results of these simulations are shown in Table III.
The simulation results demonstrate that 81.42 per cent of consumers would choose for
the most preferred base scenario. The fifth scenario is preferred least: 76.92 per cent of
consumerswouldchoosethisoption,whilepreferencesharesfortheremaining
scenarios are in between. The difference in preference share between the most and least
preferred scenario is 4.5 per cent. This fairly small difference confirms the fact that
consumers are largely indifferent towards delivery term and delivery reception conditions
when delivery and return are free. Accordingly, consumers are willing to wait longer for
their orders to arrive or collect their orders themselves instead of dedicated delivery to an
address of choice.
Our research contributes to current literature on sustainable last mile transport, by
demonstrating that consumers are in fact making trade-offs in their choice of last mile
transport options. This contradicts Gevaers et al. (2009), who state that consumers do not
make trade-offs that are able to improve environmental issues related to the last mile. Our
research also has practical implications. For omnichannel retailers with a dense store
network, the results indicate that consumers accept their store network as pick-up and
return locations, allowing retailers to create a more efficient and sustainable supply chain in
which their online and offline activities can be combined.
Retailers with an omnichannel retail model offer their customers complete flexibility
throughout their shopping journey (Piotrowicz and Cuthbertson, 2014). Accordingly,
retailers offer several last mile options to their customers, instead of merely one option as
No. Scenario description Share of preference (%)
1 Base scenario 81.42
2 Base scenario with variation on delivery term “within 1 to 3 days”81.09
3 Base scenario with variation on delivery term “within 3 to 5 days”78.22
4 Base scenario with variation on delivery reception “pick-up point”78.92
5 Base scenario with variation on delivery reception “retail group’s store”76.92
6 Base scenario with variation on return possibility “retail groups store”79.63
Table III.
Market simulation
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investigated in this research. Agatz et al. (2013) advocate for delivery service differentiation
coupled with dynamic pricing, to exceed the one-size-fits-all strategy that is unable to serve
a heterogeneous consumer market (Agatz et al., 2013). Moreover, not only consumers differ,
but also their purchases are characterised by variations that influence the delivery needs at
hand (Bpost, 2017). In line with this argument, we propose omnichannel retailers to apply
appropriate mechanisms to steer consumers towards a last mile option that is more
sustainable. An example of such strategy is a combination of free order collection in-store
and a small charge for delivery to an address of choice, or slow delivery free of charge to an
address of choice and fast but paid delivery to an address of choice. In this way, a
sustainable last mile transport offer for omnichannel retail can be created, while still
capturing the interest of consumers.
5. Conclusion
Last mile transport is a critical part of the supply chain and entails the various ways in
which products reach the end-customer in the consumer market. Not only is it a very
costly process for retailers and logistics service providers to organise, its environmental
impacts are also significant. Home delivery is considered the worst last mile option but
several more sustainable alternatives exist, including local pick-up points and lockers.
Retailers that adopt an omnichannel model add their stores as an alternative location for
consumers to collect (and return) their online orders. By means of a survey with choice-
based conjoint experiments, this paper investigates to which extent consumers are willing
to adopt last mile options that are more sustainable and how these options should be
composed to remain attractive. Results show that almost one third of consumers reflect a
neutral attitude towards sustainability in last mile transport, indicating low interest in
and/or knowledge about this topic among consumers. It also provides an opportunity to
convince consumers to choose for more sustainable last mile options. Although the
research shows that consumers’preference goes out to free, next day delivery to an
address of choice, on regular office hours during the week, they are willing to collect their
orders themselves or wait longer for their order to arrive when delivery and return are
free. The research confirms that consumers accept omnichannel retailers’store network as
pick-up and return locations, allowing retailers to organise their supply chain in a more
efficient and sustainable way.
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Van Loon, P., Mckinnon, A.C., Deketele, L. and Dewaele, J. (2014), “The growth of online retailing: a
review of its carbon impacts”,Carbon Management, Vol. 5 No. 3, pp. 285-292.
Corresponding author
Heleen Buldeo Rai can be contacted at: heleen.buldeo.rai@vub.be
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