Investigation and Analysis of Omni-channel Logistics Models: A

Study in the Retail Industry in Indonesia

Eliot Simangunsong, and Ivan Evander Subagyo

Department of Operations & SCM, Universitas Prasetiya Mulya, JL. RA. Kartini (TB Simatupang), Jakarta 12430,

Indonesia

Keywords: E-Commerce, B2C, Multi-channel, Omni-channel, Logistic.

Abstract: Retail industry has undergone several transformations over the years. There are three types of retail channel

from a logistical perspective. The first type is single-channel logistics, where retail traders operate one sales

channel and the logistic system is dedicated for one channel. The second type is multi-channel logistics,

where retail traders use several channels, such as store and direct sales. The third type is omni-channel

logistics, where retail buyers and traders do not differentiate channels. Shared logistics management is

usually available through e-commerce and online sales. The objectives of this research are to investigate

retail transformation trend from offline retail traders to multi/omni-channel logistics and identification of

suitable business strategy. By using an empirical quantitative approach in the form of a survey, data from

114 electronic retailers have been collected, consists of 70 respondents are retail stores that have both

offline and online stores and 44 respondents only have offline stores. The result of analysis shows that most

retail transactions are offline transactions where buyers have to go to the retail stores to claim their products.

However, there is enough empirical evidence that retail that use multi-channel and omni-channel logistics

have better financial performance compared to offline stores only. Three critical factors have been identified

to contribute to the total retail sales increase. Firstly, price discrimination in product delivery, secondly, the

existence of dedicated resources (space and staff) that are optimally used in terms of work time efficiency

and buyer service, and thirdly, the ability for consumers to see every goods or stocks in all retail

shops/warehouses.

1 BACKGROUND

Retail has undergone several transformations over

the years and encountered many shiftings. The main

cause is as a result of the digitalization that is

happening in business, transforming all processes

and, as an effect, consumer behavior (Waker, Nääs,

Duarte, & Papalardo, 2018). The internet has grown

rapidly as a commercial tool for retail owners in

conducting business. The advancement in

technology and internet connection infrastructure

that is easily accessible by smart phones has made

Indonesian consumers increasingly confident in

conducting online transactions. Consumers are

increasingly shifting from offline transactions to

online transactions. The size of the online shopping

market in Indonesia continues to increase, estimated

at 8.59 billion US dollars in 2018, up from 5.78

billion US dollars in 2016 (Statista, 2018). The trend

of e-commerce has given rise to many new express

shipping services. However, shipping services still

have problems including imperfect shipping

systems, poor service levels and inadequate

customer databases, unimproved mode of operation,

lack of scientific practice and efficiency in choosing

patterns, shipping modes and packaging services.

Online retailing, also known as e-commerce, has

dramatically increased the number of sales for the

past two decades, both nationally and globally.

These trends have prompted the immense

development of Internet-based retail just as the

difficulties and opportunities for the retail business.

The Amazon drove the way, setting up an incredible

upper hand over generally retailers. In June 2017,

Amazon reported it had procured Whole Foods, an

across the nation market chain in the United States,

with almost 500 stores, for $ 13.7 billion in real

money (Cusumano, 2017). It is also found that

omni-channel shoppers’ spending is 15-30 percent

Simangunsong, E. and Subagyo, I.

Investigation and Analysis of Omni-channel Logistics Models: A Study in the Retail Industr y in Indonesia.

DOI: 10.5220/0009198500340042

In Proceedings of the 2nd Economics and Business International Conference (EBIC 2019) - Economics and Business in Industrial Revolution 4.0, pages 34-42

ISBN: 978-989-758-498-5

bigger than traditional shoppers’ (Murfield, Boone,

Rutner, & Thomas, 2017).

This development has made traditional stores

also move to sell online to compete with online

retailers that do not have physical stores. There was

an increase in slices between online and

conventional retail, mainly due to the fact that

traditional retailers expanded their activities to e-

commerce, thereby increasing turnover from online

sales. However, despite the expansion in online

deals, physical stores continue acts as the primary

shopping venue, and traditional stores adopt multi-

channel strategies or omni-channel strategies by

incorporating online sales into their business. Recent

research by IBM (2015) has shown that combination

of offline and online retail enable traditional stores

to provide resistance to pure online retail traders.

The study found that physical stores offer services

that cannot be achieved by pure online retail traders

using the right omni-channel model that requires

digital integration throughout the organization.

Hübner, Holzapfel, & Kuhn (2016) defines three

types of retail channel from a logistical perspective.

The first type is single-channel logistics, where retail

traders operate one sales channel and the logistic

system is dedicated for one channel. The second

type is multi-channel logistics, where retail traders

use several channels, such as store and direct sales.

Generally, multi-channel logistics have separated

system for operation and logistic. Buyers get

products either in the store or through direct

delivery. Operational and logistical processes are not

integrated. An example is retail traders who have

offline stores and online stores that serve buyers

without operational coordination or exchange of

goods between channels. The third type is omni-

channel logistics, where retail buyers and traders do

not differentiate channels (Bell, Gallino, & Moreno,

2014; Brynjolfsson, Hu, & Rahman, 2013; Verhoef,

Kannan, & Inman, 2015). Simultant logistics

management is available, for example orders via e-

commerce can be processed through an offline store

or items in the store are used for shipping online

sales. Figure 1 provides an illustration and the

difference between the three types of logistics

channels.

Figure 1. Types of Logistic Channels

According to Zhang et al. (2018), the need to

understand the multi-channel and omni-channel

strategies is not new. Companies should be aware

that besides managing their channels, they need to

be aware of other strategies that will affect their

performance. Regardless of the channels used,

technology is crucial for retailers, specifically saying

in how they sell their goods and deliver them.

Technology enables a customer to connect with the

business in "all routes and all areas”, including

website, smartphones, PC or laptop, on TV and in-

store" (Saghiri, Wilding, Mena, & Bourlakis, 2017).

Here, customers can submit their requests in a single

channel, e.g., on a smartphone, then pick up the

purchased product through another channel, e.g.,

home delivery, and return items in a third channel

e.g., physical store (Brynjolfsson et al., 2013;

Saghiri et al., 2017). The challenge is to build a

business that utilize this multi channels including

inside logistics coordination of retailers and in the

structure and procedures of the store network/supply

chain. It is also important to investigate why and

how retailers use a single-channel logistics model to

multi-channel and omni-channel logistics. In

addition, there is an urgency to examine the logistics

model (as a combination of logistical variables) that

is optimally used by retail companies in

implementing omni-channel logistics strategies. The

study by PWC (2015) shows that omni-channel

logistics strategies are very important for the success

and prosperity of the future of the retail industry.

The objectives of this research are to investigate

why and how offline retail traders move from single-

channel logistics to omni-channel logistics and to

identify what is the relevant business logistics model

(as a combination of logistical variables)

implemented by the company in applying omni-

channel logistics strategy. The change in the

business paradigm from brick-and-mortar business

to online / internet business has caused problems in

managing the logistics system and distribution of

goods delivery. The omni-channel logistics concept

is a new reference for winning the competition.

Therefore, a comprehensive scientific study is

needed to fully understand the ideal omni-channel

logistics model in the Indonesian perspective and is

highly competitive. Existing logistical models must

be tested or validated for both endogenous and

exogenous variables that influence the optimization

of logistics management.

Investigation and Analysis of Omni-channel Logistics Models: A Study in the Retail Industry in Indonesia

2 LITERATURE STUDY

The use of the internet as a commercial media has

developed rapidly, especially for retail traders in

conducting business activities. These business

activities, known as e-commerce, have managed to

increase sales significantly over the past two decades

in all major markets, both national and global.

Zwass (1996) defines e-commerce as "business

information disclosure, maintaining business

relationships and conducting business transactions

through telecommunications networks". E-

commerce uses an electronic transactions payment

system, generally through the internet or cellular

media, where consumers use computers and

smartphones to get information and shop online. E-

commerce processes and activities are a union of

business processes, information processes, payment

processes and logistics (Xianglian & Hua, 2013).

Logistics is very important in the context of e-

commerce business because it affects various

performance -measures such as product and service

availability, communication speed between buyers

and sellers, lead time, scope of activities, flexibility

and reliability of supply (Kadłubek, 2015). The

study by PWC (PWC, 2015) mentions that there are

three important conditions to be considered for the

success of the omni-channel logistics strategy. First

is the readiness of technological infrastructure. This

readiness is assessed from the sophistication of

network and communication technologies such as

the use of mobile Internet broadband and

smartphone penetration; services that offer products

and product promotions to buyers according to

where they are; sophisticated application that is able

to provide depth of product information, buyer

reviews and price comparisons. Second is the ability

to meet high buyer expectations. Buyers may

demand the same online shopping experience as

shopping offline. Third is the readiness of retailers

themselves. To run the omni-channel model, retail

traders need to transform their thinking, including

renewing organizational culture. They must be able

to strategize and treat online and offline businesses

as a whole. Likewise, system integration, application

development and innovation to attract buyers into

the company's ecosystem.

The large online shopping market in Indonesia

has reached 7 billion dollars in 2017 and is projected

to exceed 8.59 billion US dollars in 2018 (Statista,

2018). Table 1 shows media products, electronic

devices, clothing and shoes, food and beverages, and

home care are the five most dominant retail industry

categories in online retail in Indonesia.

Table 1. Online Sales based on the Retail Industry

Category

IDR billion 2015 2016 2017

Media product 8,153 11,129 14,306

Electronic

devices

7,061 8,322 9,406

Clothes and

shoes

4,859 5,708 6,080

Food and

everage

586 762 956

Home care 493 662 877

Health 478 556 629

Accessories 349.80 497.20 595

Beaut

313 393 440

Household

appliances and

furniture

243 314 399

Personal

ment

190 231 336

Improved home

and

arden

14 19 25

Animal care 12 18 25

Sim

ames 3.7 4 6.4

Hardware in the

form of a game

0.4 0.4 0.5

Other online

retail

3,773 7,199 12,486

Total online

retail

26,535 35,822 46,573

The main activities in electronic commerce

generally consist of payment and logistics

information platforms (Hua & Jing, 2015). Logistics

part in e-commerce is not only to support the

platform or to engage the last link of e-commerce

last link, it is also happened to be the most crucial

factor in the success of e-commerce. However, once

the necessary infrastructure is available, the biggest

obstacle lies in the readiness of retail traders - or, in

other words, in focusing on how customer

experience it and their willingness to invest in a

EBIC 2019 - Economics and Business International Conference 2019

certain technology that helps the adoption of omni-

channel logistics strategies. (PWC, 2015). One of

the most significant drivers for selling goods online

is the best logistical strategy choice (Ghezzi,

Mangiaracina, & Perego, 2012).

It is a general idea that logistics involves

transferring physical goods from one location to

another (Lummus, Krumwiede, & Vokurka, 2001).

Logistics is the process of planning, implementing,

and controlling the flow of efficient, effective and

storage of goods, services and related information

from the origin to consumption point for the

destination according to the buyer's request. Joong-

Kun Cho et al. (2008) stated that there is a positive

relationship between logistics performance and

company performance in the e-commerce market.

Logistics performance here is an important

requirement to produce superior company

performance in the world of e-commerce.

From the literature studies that have been

conducted, it can be summarized that there are three

logistical model perspectives consisting of the

Classical Logistics Model, the Logistics Model with

the Integration of Functions and Processes, and the

Omni-channel Logistics Model. The Classical or

Traditional Logistics Model starts from the order

transaction by the customer, continues with a trip to

the store, picking up the goods which is continued

from the distribution center, to a smaller distribution

place, then directly to the destination (customer).

The second logistics models have function &

process integration (Saghiri et al., 2017). In the

model, there are 4 main processes for logistics,

namely Pre-Purchase (referring to integrated

promotion), Payment (referring to transactions and

integrated pricing), Delivery (referring to integrated

order fulfillment), and Return (referring to reverse

logistics).

The third is the omni-channel logistics model

suggested in the study of Marchet, Melacini, Perotti,

Rasini, & Tappia, (2018). The omni-channel

logistics model shows four sets of processes, namely

delivery services, distribution arrangements,

fulfillment strategies and return management. In the

delivery service process, there are four logistical

variables, namely delivery mode, speed, time slot

and slot price differentiation. Furthermore, in the

distribution arrangement process there are three

variables, namely choosing locations, shipping

areas, and transportation services. In the third

process, namely the fulfillment strategy, there are

three variables consisting of automation, integration,

and order allocation. Whereas for return

management, there is the only logistics variable,

namely the return mode.

Figure 2. Omni-channel Logistics Model

Based on these three logistic models, we find

that the omni-channel logistics model (Marchet et

al., 2018) offers a more comprehensive logistic

model for managing logistics management. This

model has also been adapted to e-commerce, so that

each variable is more suitable for e-commerce. In

addition, because this model has just been published,

this model is considered more suitable for the

current situation.

3 METHOD

This study consists of four stages: first, the

definition of the scope of the relevant research in the

perspective of omni-channel logistics. Second,

literature studies to understand the development of

the latest research for the scope of research. Third,

identification of gaps in existing studies to create

research designs. The fourth and final stage is to

carry out the research design.

This study uses an empirical quantitative

approach in the form of a survey. Surveys are

quantitative research methods that use a standard

format, for example questionnaires, which are used

to define or explain variables, and to analyze

relationships between variables (Malhotra & Grover,

1998). The research framework was adapted from

the study by Marchet (2018), consisting of four areas

of the company's logistical decisions namely:

shipping services, distribution arrangements,

fulfillment strategies and return management. Each

logistic decision field (also called endogenous

variable) consists of different logistical variables

(also called exogenous variables) that represent the

Investigation and Analysis of Omni-channel Logistics Models: A Study in the Retail Industry in Indonesia

design parameters to be applied, with several options

available for each variable. The first factor has 4

items that refer to Shipping Mode. While the second

factor has 3 items that refer to distribution

arrangements. The third factor has 3 items that refer

to the fulfillment strategy, and the last is the fourth

factor with 1 item that refers to the return

arrangement.

The survey questionnaire prepared in this study

will be distributed to the main respondents, namely

companies engaged in the retail industry. The use of

companies as respondents in logistics research was

common in past studies, i.e. study by Joong-Kun

Cho et al. (2008), Ghezzi et al. (2012), and Marchet

(2018). The survey data obtained will then be

analyzed using descriptive statistical analysis and

parametric statistics to identify dominant factors and

see the relationships between variables to get

answers to the research questions set in the purpose

of this study.

4 RESULTS AND DISCUSSION

This study, based on the results of a survey of

traditional retailers, describes the extent and

operational nature of their logistics operations and

examines the statistical profile of traditional

retailers. Differential demographic, behavioral, and

attitudinal characteristics of respondents are

provided. From 114 respondents, 70 respondents (or

61.4% of total respondents) are retail stores that

have both offline and online stores. There are 42

respondents whose store profile are both offline and

online have integration in fulfilling buyer’s orders.

This profile has the highest number of respondents

compared to the other profiles and it represents

36.8% of total respondents. The other 28

respondents are stores that do not have integration

between their offline and online-based operations.

Then, there are 44 respondents that only have an

offline store, either with no branches or with

branches / warehouse. The distribution of

respondents suits the objective of this research in the

context of omni-channel logistics. Table 2 presents

the respondents profile.

Table 2. Frequency distribution of respondents

Fre

uenc

Percenta

Offline onl

, no branches 23 20.2

Offline only, with branches

or warehouse

21 18.4

Offline and online, with

integration in fulfilling

uyer orders

42 36.8

Offline and online, no

integration in fulfilling

er orders

28 24.6

Total 114 100.0

All respondents were retailers located in Great

Jakarta area and focusing their business on

electronic devices. Most are doing business in big

shopping malls in central Jakarta and the rest are

small street stores. From the total of 114

respondents, there were more male respondents than

female respondents. There were 63 male respondents

(55.3% of total respondents), while the remaining 51

people are female respondents (44.7% of total

respondents). Table 3 presents the frequency of

respondents job status. 71.9% respondents were

permanent workers (82 people). This number is the

highest number compared to other employment

statuses. Then 13.2% respondents are part-time

workers. There were also respondents who owned

direct retail shop. These respondents were divided

into 2, which were owners who worked full time

(10.5%) and owners who worked part time (2.6%).

Two respondents (1.8%) were relatives to the store

owner. Therefore, 97 respondents (85.1%) whose

status were workers and 15 respondents (13.1%)

whose status were shop owners.

Table 3. Type of respondent’s job status

Respondents job status Frequenc

Percentage

Store owner (work full time) 12 10.5

Store owner (work part

time)

3 2.6

Family (Child, Wife, Close

relative

)

2 1.8

Permanent worker

(

Staff

)

82 71.9

Part-time worke

15 13.2

Total 114 100.0

EBIC 2019 - Economics and Business International Conference 2019

Customers of the respondents are end customers,

resellers or both of them. Most respondents (69.3%)

are retail stores that have both end customers as well

as resellers. There are stores that only serve end

customers (28.9%), while only 2 stores serve only

resellers (1.8%). Table 4 presents type of product

sold by respondents. From 114 respondents, there

were 107 respondents who expressed their primary

products and 98 respondents who also expressed

their secondary products. For primary products, the

highest number is handphone, followed by laptop,

handphone accessories, computer accessories, PCs

and laptop accessories. No respondents mention

tablets for primary products. For secondary

products, handphone accessories is the highest,

followed by laptop accessories, computer

accessories, laptops, PCs, mobile phones, and

tablets. Therefore, it is reasonable to assume that the

items that most often sold by our respondents from

the survey results are handphone and handphone

accessories.

Table 4. Type of product sold by respondents

Type of product Primar

Secondar

Total

Handphone 40 9 49

Laptop 33 10 43

PC 9 10 19

Tablet 0 1 1

Hand

hone Accessories 11 34 45

Accessories 4 22 26

Computer Accessories 10 12 22

Total 107 98

Handphone and Laptop are the two most popular

products for electronic retailers. As can be seen

from Table 5, offline and online stores without

integration in fulfilling buyer’s orders has the

highest range of average revenue for both products.

The second rank is offline only stores with branches

/ warehouses for hand phones and Offline and online

stores with integration for laptops. Stores with the

lowest average revenue is offline only stores without

branches for both products. Thus, it is reasonable to

assume that handphones and laptops are best sold in

offline and online stores without integration in

fulfilling buyer’s orders.

The result of the analysis also shows that most

transactions (80%) are offline, which means that

buyers have to go to the retail stores to claim their

products. Although offline and online stores without

integration in fulfilling buyer’s orders have the

highest sales among other types of stores, only about

10% of the transaction is online transaction in which

products are delivered to the customers. Multi-

channel retailers tend to have bigger revenues than

omni-channel retailers. The main reason of this

phenomenon probably the multi-channel retailers are

most likely big store, while omni-channel retailer is

a novel thing in Indonesia; these stores tend to be in

the development stage.

Table 5. Handphone and Laptop Sales per-Retailer

Average Monthly

Sales

(in hundred million

Rupiah)

Type of Retaile

Handphone Laptop

Offline only, no branches 2.80 2.25

Offline only, with branches or

warehouse

5.18 3.00

Offline and online, with

integration in fulfilling buyer

orders

3.57 4.25

Offline and online, no

integration in fulfilling buyer

orders

5.90 4.82

Average 4.50 4.00

The next analysis is inferential statistics using

SPSS. The first analysis is to test whether different

types of store‘s profile have any differences on sales.

Using ANOVA test, we find F-value = 3.967 and a

small p-value = 0.01. It means at alpha 5%, reject

the null hypothesis or there is at least 1 store’s

profile that has different amount of sales. Based on

the descriptive statistics, offline and online stores

without integration has the highest monthly revenue,

which is Rp. 200-250 million and the lowest is

offline only retailers without branches with range of

monthly revenue Rp. 50-100 million. These suggest

that omni-channel logistic system is not necessarily

needed by retail stores in Indonesia. The result of

hypothesis test can also imply that offline retail

stores without any branch in Indonesia have two

ways to increase their sales. The first way that can

Investigation and Analysis of Omni-channel Logistics Models: A Study in the Retail Industry in Indonesia

be done is by opening branches or warehouses in

different places. However, this first way is hard to be

done due to the requirement of high capital. The

second way that can be done is to open an online

store to reach more customers in other places.

Table 6 presents further ANOVA test (Post Hoc

Tests) using Tukey’s HSD. Post Hoc Tests using

Tukey’s HSD which compare offline only retailers

(no branch) with offline-online retailers (no

integration) shows small p-value (0.007). This also

confirms that retailers with offline only operations

(single channel) have different sales performance

compared to retailers that combine offline and online

operations (multi-channel).

Table 6. Post Hoc Tests using Tukey’s HSD

(I) Profile (J) Profile Mean

Diff (I-J)

Std.

Erro

Sig.

Offline

only, no

ranches

Offline only,

with branches

or warehouse

-1.827 0.786 0.1

Offline and

online, with

integration in

fulfilling buyer

orders

-1.149 0.687 0.344

Offline and

online, no

integration in

fulfilling buyer

orders

-2.451* 0.740 0.007

Dependent Variable: Monthly sales revenue

* The mean difference is significant at the .05 level.

The second hypothesis test is to understand

delivery service where different types of retailers

may have any differences in terms of time slot and

price differentiation. Time slot means buyer may

determine the hours / time when the goods will

arrive at the buyer's place. Price differentiation in

delivery service occurs when each selected time slot

has a different price depending on the seller. After

conducting ANOVA test, researcher get a p-value

0.099. Since the p-value is greater than alpha (0.05),

we fail to reject H0. Thus, it can be concluded that

every retailer does not have any differences in terms

of slot time and price differentiation.

The third hypothesis test is to understand how

retailers have any differences in the availability of

dedicated resources (space and staff) to serve

buyers. Since the p-value is less than alpha (0.05), it

can be implied that researcher rejects H0 or there is

at least one store type that has differences in the

existence of dedicated resources (space and staff)

that are optimally used in terms of work time

efficiency and buyer service. As seen from Table 7,

it is the Offline and online, with no integration in

fulfilling buyer orders. The descriptive statistics

shows there are 50% offline and online stores

without integration have optimally used dedicated

resources in fulfilling buyer’s orders. Researcher

presumes that these stores have the most sales, but

they do not have integrated stocks and systems

between channels. Thus, it is necessary for those

stores to have dedicated resources (space and staff)

in order to stabilize their sales with their stocks and

their lead times. On the contrary, offline and online

stores with integration in fulfilling buyer’s orders

seem to be the least to use dedicated resources

system. In fact, there are only 9.5% online and

offline stores with integration use dedicated

resources system. It is reasonable to assume that this

type of stores does not need this dedicated resources

because the stocks and systems between each

channel are already integrated, enabling them to

control the availability of stocks and lead times.

Table 7. Post Hoc Tests using Tukey’s HSD

(I) Profile (J) Profile Mean

Diff

(

I-J

)

Std.

Error

Sig.

Offline and

online, no

integration

in fulfilling

uyer orders

Offline

only, no

branches

.326* 0.109 0.018

Offline

only, with

branches

warehouse

.357* 0.112 0.010

Offline

and

online,

with

integration

fulfilling

buyer

orders

.405* 0.094 0.000

Dependent Variable: The availability of dedicated

resources (space and staff) to serve buyers

* The mean difference is significant at the .05 level.

EBIC 2019 - Economics and Business International Conference 2019

5 CONCLUSIONS

This study shows the extent and operational nature

of logistics operations in traditional electronic

retailers. Differential demographic, behavioral, and

attitudinal characteristics of respondents are

provided. Handphone and Laptop are the two most

popular products for electronic retailers. Offline and

online stores without integration in fulfilling buyer’s

orders has the highest range of average revenue for

both products while stores with the lowest average

revenue is offline only stores without branches for

both products. The result of the analysis also shows

that most transactions (80%) are offline, which

means that buyers have to go to the retail stores to

claim their products. Multi-channel retailers tend to

have bigger revenues than omni-channel retailers.

The main reason of this phenomenon probably the

multi-channel retailers are most likely big store,

while omni-channel retailer is a novel thing in

Indonesia; these stores tend to be in the development

stage.

After conducting an analysis test using the SPSS

application, it can be concluded that there are 3

important factors that can increase total sales and

revenues of retail stores. These 3 factors are time

slots that have different prices in product delivery,

the existence of dedicated resources (space and staff)

that are optimally used in terms of work time

efficiency and buyer service, and the ability for

consumers to see every good / stock in all shops /

warehouses (not limited in only 1 shop). In the

Marchet table (2018), time slots with different prices

is the “yes” option in the delivery service category

with slot price differentiation logistics variable. The

existence of dedicated resources (space and staff)

that are optimally used in terms of work time

efficiency and buyer service is the “capacity-

optimized and integrated” option in the fulfillment

strategy category with integration logistics variable.

The last factor, which is the ability of consumers to

see every good in all shops / warehouses, is the

“dynamic” option in the fulfillment strategy

category with order allocation logistics variable.

ACKNOWLEDGEMENTS

The authors gratefully acknowledge that the present

research is supported by Universitas Prasetiya

Mulya. The support is under the research grant of

Year 2018.

REFERENCES

Balcik, B., Beamon, B. M., & Smilowitz, K. (2008). Last

Mile Distribution in Humanitarian Relief. Journal of

Intelligent Transportation Systems, 12(2), 51–63.

https://doi.org/10.1080/15472450802023329

Bell, D. R., Gallino, S., & Moreno, A. (2014). How to win

in an omnichannel world. MIT Sloan Management

Review, 56(1), 45.

Boyer, K. K., Tomas Hult, G., & Frohlich, M. (2003). An

exploratory analysis of extended grocery supply chain

operations and home delivery. Integrated

Manufacturing Systems, 14(8), 652–663.

https://doi.org/10.1108/09576060310503465

Brynjolfsson, E., Hu, Y. J., & Rahman, M. S. (2013).

Competing in the age of omnichannel retailing. MIT.

Cusumano, M. A. (2017). Amazon and whole foods:

Follow the strategy (and the money). Communications

of the ACM, 60(10), 24–26.

https://doi.org/10.1145/3132722

Ghezzi, A., Mangiaracina, R., & Perego, A. (2012).

Shaping the E-Commerce Logistics Strategy: A

Decision Framework. International Journal of

Engineering Business Management, 4, 13.

https://doi.org/10.5772/51647

Hua, W., & Jing, Z. (2015). An Empirical Study on E-

commerce Logistics Service Quality and Customer

Satisfaction. WHICEB.

Hübner, A., Holzapfel, A., & Kuhn, H. (2016).

Distribution systems in omni-channel retailing.

Business Research, 9(2), 255–296.

https://doi.org/10.1007/s40685-016-0034-7

Joong-Kun Cho, J., Ozment, J., & Sink, H. (2008).

Logistics capability, logistics outsourcing and firm

performance in an e‐commerce market. International

Journal of Physical Distribution & Logistics

Management, 38(5), 336–359.

https://doi.org/10.1108/09600030810882825

Kadłubek, M. (2015). The Selected Areas of E-logistics in

Polish E-commerce. Procedia Computer Science, 65,

1059–1065.

https://doi.org/10.1016/j.procs.2015.09.052

Lummus, R. R., Krumwiede, D. W., & Vokurka, R. J.

(2001). The relationship of logistics to supply chain

management: Developing a common industry

definition. Industrial Management & Data Systems,

101(8), 426–432.

https://doi.org/10.1108/02635570110406730

Malhotra, M. K., & Grover, V. (1998). An assessment of

survey research in POM: from constructs to theory.

Journal of Operations Management, 16(4), 407–425.

Marchet, G., Melacini, M., Perotti, S., Rasini, M., &

Tappia, E. (2018). Business logistics models in omni-

channel: A classification framework and empirical

analysis. International Journal of Physical Distribution

& Logistics Management, 48(4), 439–464.

https://doi.org/10.1108/IJPDLM-09-2016-0273

Murfield, M., Boone, C. A., Rutner, P., & Thomas, R.

(2017). Investigating logistics service quality in omni-

channel retailing. International Journal of Physical

Investigation and Analysis of Omni-channel Logistics Models: A Study in the Retail Industry in Indonesia

Distribution & Logistics Management, 47(4), 263–

296. https://doi.org/10.1108/IJPDLM-06-2016-0161

PWC. (2015). The 2015 Global omni-channel retail index.

Saghiri, S., Wilding, R., Mena, C., & Bourlakis, M.

(2017). Toward a three-dimensional framework for

omni-channel. Journal of Business Research, 77, 53–

67. https://doi.org/10.1016/j.jbusres.2017.03.025

Statista. (2018). Indonesia: Retail e-commerce sales 2016-

2022 | Statistic. Retrieved August 16, 2018, from

Indonesia: Retail e-commerce sales 2016-2022 |

Statistic website:

https://www.statista.com/statistics/280925/b2c-e-

commerce-sales-in-indonesia/

Verhoef, P. C., Kannan, P. K., & Inman, J. J. (2015). From

multi-channel retailing to omni-channel retailing:

Introduction to the special issue on multi-channel

retailing. Journal of Retailing, 91(2), 174–181.

Waker, R. A., Nääs, I. D. A., Duarte, A. G., & Papalardo,

F. (2018). Impact of the new retail concepts on

logistics strategy. South American Development

Society Journal, 4(Esp01), 01.

https://doi.org/10.24325/issn.2446-5763.vespi1p1-10

Xianglian, C., & Hua, L. (2013). Research on e-commerce

logistics system informationization in chain. Procedia-

Social and Behavioral Sciences, 96, 838–843.

Zhang, M., Ren, C., Wang, G. A., & He, Z. (2018). The

impact of channel integration on consumer responses

in omni-channel retailing: The mediating effect of

consumer empowerment. Electronic Commerce

Research and Applications, 28, 181–193.

https://doi.org/10.1016/j.elerap.2018.02.002

Zwass, V. (1996). Electronic commerce: Structures and

issues. International Journal of Electronic Commerce,

1(1), 3–23.s

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