Available via license: CC BY 4.0
Content may be subject to copyright.
197
Business Systems Research | Vol. 12 No. 1 |2021
Enterprise Digital Divide: Website e-
Commerce Functionalities among European
Union Enterprises
Božidar Jaković, Tamara Ćurlin
The University of Zagreb, Faculty of Economics and Business, Zagreb, Croatia
Ivan Miloloža
The University of Osijek, Faculty for Dental Medicine & Health, Osijek, Croatia
Abstract
Background: Information and communication technologies (ICTs) gained prevalent
organizational and structural value in the modern economy. E-commerce is one of
the sectors directly influenced by technological change. However, not all countries
have the same opportunities to develop e-commerce growth; there are significant
discrepancies in ICT utilization worldwide, known as the digital divide. Objectives: The
purpose of this paper is to explore the level of difference among European countries
regarding the e-commerce functionalities in their enterprises using a cluster analysis.
Methods/Approach: To accomplish the paper goal, the k-means cluster analysis was
conducted on the Eurostat data from 2019. Enterprises from 28 European countries
were taken into consideration. The Kruskal-Wallis test is used to explore if the
differences among clusters regarding the digital development, measured by the
Digital Economy and Society Index are significant. Results: The investigation confirmed
that there are significant differences among European countries regarding the
development of e-commerce. However, a similar level of e-commerce is not related
to economic and digital development. Conclusions: Since the relationship between
economic development and e-commerce development in European countries is not
linear, country-level policies are likely to be significant factors driving e-commerce
development, which leads to the need for further investigation of this issue.
Keywords: e-commerce; website functionalities; digital divide; European Union
JEL classification: O33
Paper type: Research article
Received: Jan 27, 2021
Accepted: Mar 17, 2021
Citation: Jaković, B., Ćurlin, T., Miloloža, I. (2021). Enterprise Digital Divide: Website e-
Commerce Functionalities among European Union Enterprises. Business Systems
Research, Vol. 12 No. 1, pp. 197-215.
DOI: https://doi.org/10.2478/bsrj-2021-0013
198
Business Systems Research | Vol. 12 No. 1 |2021
Introduction
ICT gained prevalent organizational and structural value in the modern economy
(Pazaitis et al., 2017). It became a significant backbone for economic growth and
social development (Latif et al., 2018). Disruptive technologies like robotics, AR, VR,
artificial intelligence, and the Internet of things are now used in various business sectors
daily (Bongomin et al., 2020). New mechanisms, organizations, relations, and
management are being developed by the ICT growth and it will do more in the future
(Neirotti et al., 2018).
E-commerce is an industry that lies in ICT development (Cui et al., 2017). It became
relevant in the 1990s with Internet expansion (Yue et al., 2020). E-commerce provides
various benefits such as overcoming geographical barriers by the ICT utilization, it
consolidates dispersed markets which results in a more immense supply of products
and services offered by the c-commerce enterprises. E-commerce has evolved with
the technological change and it became decisive to understand different dimensions
of it from all perspectives. Nowadays, e-commerce sales have reached 4,13 trillion
dollars, and it’s expected to grow even more extensive, because of mobile
commerce which is expected to take a market share of e-commerce of nearly 80%
(E-commerce, 2020)
Various authors seek to address questions about factors that affect e-commerce
utilization. For instance, Rodríguez-Ardura et al. (2008) stress that Internet security is
pivotal for e-commerce enterprises activities, Alnemr (2010) emphasized trust as an
essential factor for gaining competitive advantage in the e-commerce industry, which
was confirmed by Nica (2015) who also added reputation as a significant element of
e-commerce advancement. However, none of the factors could be considered
competitive if technology adoption is low in the enterprise, which depends on
technology utilization in the country where the enterprise obtains its activity.
The purpose of this paper was to explore if there was a difference among European
countries' e-commerce functionalities. Furthermore, this analysis explored if there are
similarities between the European countries' e-commerce enterprises, which could
divide the countries into homogeneous groups. To accomplish the paper goal, the k
means cluster analysis was conducted on the Eurostat data from 2019. Enterprises from
28 European countries were taken into consideration. Kruskal-Wallis test was used to
explore tests whether the identified clusters are significant. The e-Commerce
functionalities in the European countries were observed thru three dimensions: Website
e-Commerce functionalities, CRM indicator variables, and DESI connectivity
dimension. Every dimension consists of the indicator variables.
The paper is organized as follows: After the Introduction, the Literature review
section presents an overview of the enterprise digital divide, website functionalities,
and CRM as support to e-commerce. The methodology section describes the data
and the methodology used to fulfil the research goal. The results section describes the
descriptive statistic and the cluster analysis results, followed by the discussion where all
research results are displayed. The article’s final section is the conclusion.
Literature review
Enterprise digital divide
Digital technologies transformed the way people live in the past decade dramatically
and they will continue to do it in the future. From the way of communication to training
and education, disruptive technologies transformed the mechanisms and relations
(Shen et al., 2020). There are numerous positive dimensions of the change, and
enterprises intensively invest their efforts to stay up to date with technologies and gain
199
Business Systems Research | Vol. 12 No. 1 |2021
an advantage from technology utilization (Grover et al., 2018). If the company fails to
stay up to date and take advantage of ICT (information and communication
technologies) solutions, they are at risk of becoming irrelevant and therefore less
competitive. The divergence in the ICT utilization between enterprises resulted in the
exclusion, known as the digital divide (Szeles et al., 2018). The concept of the digital
divide was initially proposed at the beginning of 21 century to describe the
disproportion of the people who have and do not have Internet access (Blank et al.,
2018). Over time, the concept became broader, and started to cover diverse aspects
of ICT, and began to be considered globally (Chen et al., 2004).
The term digital divide implies the social consequences of the phenomena, and it
could be distinguished as (i) global, (ii) social, and (ii) democratic digital divide (Norris,
2001). The global digital divide suggests the discrepancies of Internet access between
high and low developed countries, the social digital divide refers to the information
gap between highly and poorly developed countries, and the democratic digital
divide concerns the differences between countries that use or do not use ICT
resources in the public life.
Numerous initiatives seek to reduce the digital divide between countries, for
instance, the European information society had a few initiatives where the focus was
on government actions that could enhance ICT adoption in European countries (Ojo
et al., 2017). The World Economic Forum, OECD, and G20 are the organizations that
also pay special attention to the topic and commence different international
initiatives and recommendations for countries (Ojo et al., 2017).
The most common barriers to disruptive technologies adoption were split into two
distinguished categories: macroeconomic and microeconomic (Giua, 2020).
Macroeconomic aspects included problems such as lack of innovation culture, lack
of flexibility of the production environment, inoperability, and lack of investments. The
microeconomic barriers are concentrated on the lack of customer demand in low-
development countries, the lack of adaptation of the education system, and the lack
of digital content solutions.
The early investigations on the topic concentrated on the socio-economic
dimensions of the digital divide. Newer investigations focused on the digital divide
measurements approaches, such as Brynjolfsson et al. (2019) who investigated
difficulties in comprehending the value of the digital products and services. Bukht et
al (2017) obtained statistical investigations on the topic, and Ahmad et al. (2019)
proposed analytical frameworks for measuring the digital divide. The more recent
investigations conduct their studies on the large sets of variables where they perform
cross-sectional analysis or time-series analysis (Gunn et al., 2019).
Websites functionalities
E-commerce enterprises use their online presence for different reasons such as
marketing, employee recruitment, communication with their partners, etc. (Kremez et
al., 2019). Enterprises nowadays are aware of the significant impact of electronic Word
of Mouth (e-WOM) on their reputation. Internet became the main channel for
enterprises to communicate with their audience, reach their potential customers and,
finally, sell their product (Tsimonis, 2014). Information about customers provided by the
Internet enables e-commerce to create personalized and individual-oriented
products. Digital platforms have become a new phenomenon; today they are one of
the key components of global economic exchange, creating new market
mechanisms (Richardson, 2020). Enterprises often have a presence on social media,
but their website remains the primary focus where customers inform, communicate,
and buy their products or services (Kim et al., 2018). Websites became a distribution
200
Business Systems Research | Vol. 12 No. 1 |2021
channel, which is complementary to the physical stores where customers can
purchase products and services online (Pénard et al., 2017).
There are close to 2 billion digital buyers worldwide and it climbs every year (Statista,
2021). Since the websites became significant revenue drivers, every e-commerce
enterprise should have functional, aesthetic, and relevant websites to stay
competitive (Di Fatta et al., 2018). To check if the website is functional, and the
evaluation process is critical, the e-commerce enterprises' website evaluation can
help the enterprise to modernize their services.
Authors have agreed that some of the website attributes, which should be
evaluated, are precise and complete information, loading speed, website aesthetics
colors, photo, graphics), interactivity, and accessibility (Kwak et al., 2019). Various
techniques are used to evaluate e-commerce website functionalities.
For instance, the authors Albuquerque et al. (2002) created a framework that
evaluates e-commerce website functionalities based on several features such as
usability and reliability. Akhter et al. (2009) use a fuzzy logic system as an instrument
that determines website functionality. Moreover, the authors (Cebi et al, 2013) discuss
the characteristics of e-commerce websites where they investigated the functionality
factor. The paper where authors Al-Qaisi et al. (2015) investigated e-commerce
website functionalities by Mamdani fuzzy system utilization concluded that accuracy
and flexibility are the most important dimensions in website functionality. Furthermore,
the same investigation confirmed website functionality as a key driver of overall
website quality and customer satisfaction.
CRM as support to e-commerce
Customer Relationship Management CRM is considered as a business strategy to
decide and manage potential customers and clients to optimize long-term value
(Chen et al., 2003). It aims to recognize and predict the needs of both enterprises and
potential customers. ICT transformed CRM mechanisms in e-commerce, and without
the Internet, we would not know the CRM we know today (Agnihotri, 2021). It provided
new marketing techniques and strategies which enable enterprises to attract and
retain customers online which enforced them to develop new skills and transform their
CRM capabilities online (Li et al., 2020).
Online enterprises need to attract traffic to their website and web stores by the
usage of various online marketing tools such as social media marketing, e-mail
marketing, SEO optimization, and search engine marketing. Customer relationship
management successfully adopted new operations and established personalization
and customization as key activities that secure customer loyalty and recurrent
shopping of their online products or services (Grover et al., 2020). They use
personalization tools, online analysis systems, recommendation platforms, and
feedback tools to establish long-term customer e-loyalty (Oumar et al., 2017). The
customer data processing evolution is a result of the ICT and online strategies
development (Chen, 2017). CRM built its strategies on data analytics results to develop
competitive strategies (Harrison, 2019).
CRM in the context of e-commerce is continuously evolving. There are two main
impacts of CRM in e-commerce: impact on customers and the impact on suppliers
(Thaichon et al.,2020). There are two main CRM strategies on the impact on customers:
pushing information that impact and collective behavior and investigating in terms of
better customer control over configuration and prices of goods and services and the
wider array of options. CRM's impact on supplies considers creating new demand
chains, effectively communicating, and technology adopting. CRM has an impact
201
Business Systems Research | Vol. 12 No. 1 |2021
on overall e-commerce enterprises; it shapes organizational culture, sales and
marketing functions, marketing strategies, and support functions (Lin et al., 2010).
Because of the interconnection with digital technologies, the success of CRM
depends on ICT adoption. Today, CRM is no longer a competitive strategy, it is a
necessity, it is essential for e-commerce enterprises to stay competitive and attract
online customers. Therefore, for e-commerce enterprises, being up to date and
investing in the technologies is decisive for success.
Methodology
Research variables
The observed research variables were obtained from the Eurostat database for the
year 2019. European Union member countries (28 countries) at the given period were
taken into consideration. We were concentrated on the EU members only, so we did
not include other European countries in the research because of the lack of data and
variables of our focus.
The e-Commerce functionalities in the European countries were observed thru
three dimensions: Website e-Commerce functionalities, CRM indicator variables, and
DESI connectivity dimension.
E-commerce website functionalities were measured for the European countries
enterprises, with ten or more employed persons, with the financial sector excluded:
o WEB_PRICE - website describing goods or services, price-lists (% of enterprises)
o WEB_SHOP - website with online ordering or reservation or booking, e.g. the
shopping cart (% of enterprises)
o WEB_TRACK - website with order tracking available online (% of enterprises)
o WEB_SOCIAL- website with links or references to the social media (% of
enterprises)
o WEB_BOT – website with the chat-bot (% of enterprises)
o WEB_BUY_BOT – website with the chat-bot supporting buying process (% of
enterprises)
The CRM indicator variables were measured also for the European enterprise with
ten or more employees and without the financial sector included:
o CRM- Enterprises using software solutions like Customer Relationship
Management (% of enterprises)
o CRM_ANALYSIS -Enterprises using Customer Relationship Management to
analyze information about clients for marketing purposes (% of enterprises)
o CRM_STORE-Enterprises using Customer Relationship Management to capture,
store and make available client’s information to other business functions (% of
enterprises)
DESI index represents a summary of the relevant indicators on Europe’s digital
performance and tracks the evolution of EU Member States in digital competitiveness
(EU, 2020). For this investigation, five indicator variables were considered:
o DESI_1_CONN – Connectivity
o DESI_2_HC – Human capital
o DESI_3_UI - Use of Internet Services
o DESI_4_IDT - Integration of Digital Technology
o DESI_5_DPS - Digital Public Services
DESI Connectivity Dimension is measured as the weighted average of the four sub-
dimensions: (i) Fixed Broadband take-up, (ii) Fixed broadband coverage, (iii) Mobile
broadband, and (iv) Broadband price index.
202
Business Systems Research | Vol. 12 No. 1 |2021
DESI Human Capital Dimension is calculated as the weighted average of the two
sub-dimensions: (i) Internet User Skills, and (ii) Advanced Skills and Development.
DESI Use of Internet Dimension is calculated as the weighted average of the three
sub-dimensions: (i) Internet Use (ii), Activities Online, and (iii) Transactions.
DESI Integration of Digital Technology Dimension is calculated as the weighted
average of the two sub-dimensions: (i) Business digitization and (ii) e-Commerce.
DESI Digital Public Services Dimension is calculated by taking the score for e-
Government.
K-means cluster analysis
Cluster analysis is a knowledge discovery technique that is utilized for the identification
and classification of similar groups of statistical indicator variables. The variables are
homogenous within the group and heterogeneous among the other groups.
Cluster analysis is a form of unsupervised learning, and the goal of cluster analysis is
to explore hidden patterns or to identify groups of objects with similar traits. Partition
clustering and hierarchical clustering are two prevalent groups of cluster analysis
(Govender et al, 2020). The analysis starts with the research item identification,
followed by the clustering procedure selection. For this investigation, the
nonhierarchical cluster analysis with the K-means algorithm was calculated to
systemize variables into comprehensive groups.
K-means falls under the partition clustering method, where data cluster groups
have no overlapping. K-means technique is utilized to divide n observations into k non-
overlapping groups where each observation belongs to one cluster with the nearest
mean. K-means is often used to process a large of data to be representative data,
called cluster centers (San et al., 2004). For this investigation, the K-means algorithm
was used to group the indicator variables into nested groups, starting with all statistical
units in one group, after which it divides them using the top-down method. A V-fold
approach with 10 folds was used to test the validity of the solution. Euclidean distance
was used to distribute iteratively research data to the cluster with the closest centroid.
The process resulted in the selected three clusters.
Furthermore, the statistical difference between clusters regarding the DESI
indicators was investigated by the usage of the Kruskal-Wallis test, which tests whether
the identified clusters.
Results
Descriptive statistics analysis
Descriptive statistics are presented in Table 1. For the sample of the enterprises without
financial sector (10 persons employed or more) of 28 European countries and 3
encompassed dimensions: Web site e-commerce functionalities, Customer
relationship management, and DESI indicators (The Digital Economy and Society
Index, 2020).
The majority of the European countries' e-Commerce websites had the highest
average grades for the dimension Web site e-Commerce functionalities, especially for
the variable WEB_BOT which was found on 62.11% of websites. The second research
item with the high average grades was the WEB_PRICE item, which indicates that
61.04% of e-Commerce websites in European countries have a price list or descriptions
of the goods and services. On the other hand, the variables DESI_3_UI and DESI_4_ITD
for the European countries had the lowest average grade (8.25%), which suggests that
the integration of digital technologies and digital public services are utilized the least
in e-Commerce between European countries. As for the Customer relationship
203
Business Systems Research | Vol. 12 No. 1 |2021
management, the CRM_ANALYSIS variable has notably lower results than CRM and
CRM_STORE, which puts forward that the e-Commerce enterprises in European
countries should broadly implement systems for CRM analysis to enhance client
relationships.
Table 1
Descriptive statistics of e-Commerce usage indicators and obstacles for selected
European countries
N
Minimum
Maximum
Mean
Std. Dev.
Skewness
Kurtosis
Web site e-commerce functionalities
WEB_PRICE
28
34.00
96.00
61.04
16.585
-0.104
-0.764
WEB_SHOP
28
9.00
34.00
20.50
7.351
0.452
-0.820
WEB_TRACK
28
3.00
14.00
8.36
2.313
0.013
0.858
WEB_SOCIAL
28
15.00
68.00
39.54
13.686
0.286
-0.481
WEB_BOT
28
36.00
86.00
62.11
15.586
-0.295
-1.156
WEB_BUY_BOT
28
9.00
34.00
19.50
6.995
0.595
-0.455
Customer relationship management
CRM
28
12.00
56.00
29.93
10.360
0.437
-0.003
CRM_ANALYSIS
28
7.00
26.00
18.18
5.464
-0.195
-0.945
CRM_STORE
28
11.00
55.00
28.57
10.609
0.428
-0.058
DESI indicators
DESI_1_CONN
28
7.37
15.01
11.66
1.922
-0.029
-0.206
DESI_2_HC
28
7.13
19.38
12.07
3.139
0.428
-0.323
DESI_3_UI
28
5.24
11.29
8.25
1.617
0.336
-0.254
DESI_4_ITD
28
3.38
13.82
8.25
2.775
0.301
-0.671
DESI_5_DPS
28
6.75
12.74
10.12
1.807
-0.306
-1.081
Source: Authors work (Eurostat, 2019)
Cluster analysis
Graph of cost sequence, which displays the error function for the different cluster
numbers, was produced to investigate the best number of clusters for the sample data
presented. The error function could be interpreted as the average distance of
observations of the explored dataset from the cluster centroids to which the
observations were assigned (Sugar et al., 2003).
The objective is to minimize the cluster cost to the preferable value (Amaro et al,
2016). Various methods could be used to identify the preferable number of the cluster.
For this investigation, the Elbow method was chosen as the decision indicator. Figure
1 shows “the elbow” point on the number of the three clusters. The decrease of the
error function is considered to be large to the point of the three clusters, after which it
decreases slightly. Decrease between 3 and 4 number of clusters is less than 5%.
Therefore, the number of clusters selected is the optimal solution and three clusters will
be observed in further analysis.
204
Business Systems Research | Vol. 12 No. 1 |2021
Figure 1
Graph of cost sequence
Source: Authors work
Moreover, the Anova analysis was undertaken for the three selected clusters and
the dimension Website e-Commerce functionalities. The table demonstrates six
variables that exemplify the given dimension. Furthermore, the null hypnotizes was
proposed. All the variables came out as statistically significant at 1% and the null-
hypnotizes were rejected which suggests that the means between the variables
observed statistically differ. Table 2 confirms that the selected number of three clusters
included in the investigation is justified.
Table 2
The Anova analysis
Between SS
df
Within SS
df
F
p-value
WEB_PRICE
5851.254
2
1575.710
25
46.418
0.000***
WEB_SHOP
792.444
2
666.556
25
14.861
0.000***
WEB_TRACK
53.305
2
91.123
25
7.312
0.003***
WEB_SOCIAL
3314.990
2
1741.974
25
23.788
0.000***
WEB_BOT
5301.468
2
1257.210
25
52.711
0.000***
WEB_BUY_BOT
904.162
2
416.838
25
27.114
0.000***
Source: Authors work (Eurostat, 2019) **Note: statistically significant at 1%
The descriptive statistics of the e-Commerce variables of the dimension: Website e-
commerce functionalities was conducted. Cluster 1 includes 7 European countries
and has the highest means and the standard deviations for all the variables included
which suggests that the countries included in Cluster 1 have the highest developed e-
Commerce functionalities among European Union enterprises. As for Cluster 2 and 3,
Cluster 2 which includes the most European countries, a total of 11 have the higher
standard deviation and cluster mean in all observed values, except WEB_TRACK
where cluster three outperform Cluster 2. Cluster 3 consists of a total of ten European
countries. Table 3 shows the cluster means and standard deviations.
205
Business Systems Research | Vol. 12 No. 1 |2021
Table 3
Cluster means and standard deviations
Cluster 1
Cluster 2
Cluster 3
WEB_PRICE
77.29
(9.673)
67.73
(7.431)
42.30
(7.166
WEB_SHOP
29.71
(3.773)
17.45
(5.087)
17.40
(5.985)
WEB_TRACK
10.43
(1.813)
6.91
(2.256)
8.50
(1.509)
WEB_SOCIAL
57.71
(6.576)
36.64
(8.869)
30.00
(8.794)
WEB_BOT
77.29
(6.525)
68.73
(7.377)
44.20
(7.131)
WEB_BUY_BOT
29.29
(3.729)
16.91
(4.460)
15.50
(3.866)
Number of cases
7
11
10
Percentage (%)
25.00
39.29
35.71
Source: Authors work (Eurostat 2019)
Note: Standard deviations in parenthesis
Figure 2 displays the distribution of variables across clusters for the dimension Website
e-commerce functionalities. The distributions can be used to get an insight into how
many variables in a cluster differ according to the observed variable. The narrower
distribution is the smaller difference among the variables across clusters. Additionally,
the taller the distribution is, the differences between variables are larger.
The variable WEB_PRICE that suggests that the e-Commerce websites provided a
description of the goods or services or the price lists, shows the normal distribution for
all three clusters, with similar values with slightly higher probability density peaks in
Cluster 2 and 3 than Cluster 1. The Cluster 2 and 3 distribution are narrower than Cluster
1 distribution. The variable WEB_SHOP shows the European enterprises' website are
provided with online ordering or reservation or booking, e.g. shopping cart. Cluster 1
shows the highest distribution peak and the furthest from the graph origin. Cluster 3
shows the lowest probability density peak and the widest distribution of all three
clusters.
The variable WEB_TRACK portrays the enterprises where the website provided order
tracking available online. The values were the highest for Cluster 3, which correlates
with the results from the Cluster means and the standard deviation table following
Cluster 1. Cluster 2 showed the widest distribution for the given variable with the lowest
peak closest to the origin. The WEB_SOCIAL presents data of the European enterprises
where the website had links or references to the enterprise's social media profiles.
206
Business Systems Research | Vol. 12 No. 1 |2021
Figure 2
Distribution of variables across clusters
Source: Authors work
Discussion
Cluster membership across countries
As mentioned in the previous section, Cluster 1 contains seven countries: Belgium,
Denmark, Ireland, Malta; Netherlands; Finland, and Sweden. All countries in Cluster 1
are highly developed. Cluster 2 is the largest; it consists of eleven European countries:
Germany, Estonia, France, Cyprus, Latvia, Luxembourg, Austria, Poland, Slovenia,
Slovakia, and United Kingdom. The countries' structure for the Cluster 2 is diverse, it
consists of both some of the highly developed countries such as the UK, Germany, and
France, and some of the countries, which struggled hardly thru economic crises, but
recovered, such as Slovakia and Cyprus. Cluster 3 consists of the 10 European
207
Business Systems Research | Vol. 12 No. 1 |2021
countries: Bulgaria, Czechia, Greece, Spain, Croatia, Italy, Lithuania, Hungary, Poland,
and Romania, which are mostly developing countries (e.g. Croatia) and the countries,
struggling with the economic crisis (e.g. Greece).
Table 4
Countries across clusters
Cluster
Country
Cluster 1
Belgium, Denmark, Ireland, Malta, Netherlands, Finland, Sweden
Cluster 2
Germany, Estonia, France, Cyprus, Latvia, Luxembourg, Austria, Poland,
Slovenia, Slovakia, United Kingdom
Cluster 3
Bulgaria, Czechia, Greece, Spain, Croatia, Italy, Lithuania, Hungary,
Portugal, Romania
Source: Authors work
Figure 3 presents the European map according to the three specified clusters. There
can be identified similar socio-economic development similarities as well as the close
geographic position within clusters. All Scandinavian countries are grouped in Cluster
1 (Denmark, Finland, and Sweden) which are considered highly developed. Alongside
Scandinavian countries, Cluster 1 consists of two of three Benelux countries (Belgium
and Netherlands), and, Ireland, and Malta which are all highly developed. Therefore,
Cluster 1 could be considered as highly developed. Cluster 2 consists of most of the
Central European countries, which are presented, in the figure. The countries included
are developed, some are highly developed, and the cluster overall could be defined
as developed. Cluster 3 consists of developing countries and countries, which are
recovering from the economic crises. According to the map, the countries included
in the Cluster three are mostly eastern European countries. Therefore, Cluster 3 could
be defined as developing.
Figure 3
European map according to countries grouped into specific clusters
Source: Authors work using mapchart.net
The k-means analysis shows data for a sample of 28 European countries for indicators
of the dimensions of the webshop e-commerce functionalities (Figure 4). It represents
the mean value for the six observed variables from the given dimension: WEB_PRICE,
WEB_SHOP, WEB_TRACK, WEB_SOCIAL, WEB_BOT, and WEB_BUY_BOT. The mean values
208
Business Systems Research | Vol. 12 No. 1 |2021
were observed within three identified clusters. By comparing result means, interesting
conclusions have emerged, such as knowledge that e-Commerce webshops from
highly developed countries from Cluster 1 outperform the other two clusters when it
comes to all variables (not only webshop). Cluster 1. Also has high means regarding
web_social, web_bot, and web_buy_bot. The Cluster 2 countries focus on building
customer relationships including innovative digital marketing strategies in their
websites. The Cluster 1 values are the furthest from the origin so they show the most
significant values.
Figure 4
Cluster means of indicators of webshop e-commerce functionalities
Source: Authors work
European countries grouped in Cluster 1 (highly developed) outperform Cluster 2
and Cluster 3 for all the selected variables from the dimension webshop e-Commerce
functionalities. The average means of the observed variables are higher than any
variable from Cluster 2 and Cluster 3. This knowledge correlates to the findings of the
countries selected for Cluster 1, which are highly developed countries such as
Scandinavian countries. Most enterprises in Cluster 1 countries have webshops
integrated into their e-Commerce websites, and the least of them have the web track
variable.
The Cluster 2 countries (developed) have the highest average means value for the
variable WEB_BOT. It suggests digital marketing strategies and customer relationship is
essential for the countries from Cluster 2. The average mean for the variable web price
is also high, opposite to the values from the other clusters for the given variable, which
also could be the strategy to intensify customer relationship. The Cluster 2 European
countries have the lowest average means for the variables WEB_SHOP that represents
online shop or booking and WEB_BUY_BOT. This could be explained as the Cluster 2
countries' e-Commerce websites focus their website on digital marketing and building
customer loyalty.
209
Business Systems Research | Vol. 12 No. 1 |2021
The Cluster 3 countries (developing) have the highest mean values from the
webshop e-Commerce functionalities dimension for the variable WEB_TRACK. This
could be explained as the some of the countries from Cluster 3 are the newer
members of the EU or they do not have some of the most developed and reliable
shipping companies as UPS and DHL so the shipment could last longer than in
developed and highly developed countries, so the tracking is essential. The
WEB_BUY_BOT and the WEB_PRICE variables have the lowest mean values, which
mean that Cluster 3 countries do not yet concentrate on disruptive technology
implementation and customer relationships.
The difference between European countries is presented by the cluster analysis. The
difference between cluster countries. There are few indicators of diversity between
clusters identifies (i) development which explains the outperformance of the highly
developed countries related to other clusters, the countries technological structure,
and countries orientation to technology development as the online shopping habits.
Relationship between cluster membership and CRM
implementation
Table 5 displays the comparison of cluster members according to CRM indicator
variables. Descriptive statistic was used to and the Kruskal-Wallis test was used to
explore the statistical differences of the standard deviations. The Kruskal-Wallis test
confirms that all the clusters are statistically significant at the 1% for the CRM and
CRM_ANALYSIS variable, and at the 5% for the CRM_STORE indicator variable.
Table 5
Comparison of cluster members according to CRM indicator variables – Descriptive
analysis and Kruskal-Wallis test
CRM
CRM_ANALYSIS
CRM_STORE
Cluster 1
Mean
39.86
24.43
37.86
N
7
7
7
Std. Deviation
8.80
1.81
9.67
Cluster 2
Mean
29.55
16.82
28.45
N
11
11
11
Std. Deviation
8.96
3.71
9.62
Cluster 3
Mean
23.40
15.30
22.20
N
10
10
10
Std. Deviation
7.55
5.50
7.77
Total
Mean
29.93
18.18
28.57
N
28
28
28
Std. Deviation
10.36
5.46
10.61
Kruskal-Wallis test
Kruskal-Wallis H
10.118
13.504
9.151
df
2
2
2
Asymp. Sig.
0.006***
0.001***
0.010**
Source: Authors work
Note: *** statistically significant at 1%; ** 5%
Figure 5 represent error bars of means of CRM indicator variables across clusters.
Standard deviations are the lowest for the CRM_analysis variable indicator and are
similar for variables CRM and CRM_STORE. Error bars are the largest for the Cluster 1
values and the smallest for the Cluster 3 variables.
210
Business Systems Research | Vol. 12 No. 1 |2021
Figure 5
Error bars (95%) of means of CRM indicator variables across clusters
Source: Authors work
Relationship between cluster membership and DESI index
Table 6 present the comparison of cluster members according to DESI index variables.
Descriptive statistic was used to and the Kruskal-Wallis test was used to explore the
statistical differences of the standard deviations. The Kruskal-Wallis test confirms that
all the differences among the variables included are statistically significant.
DESI_1_CONN and CRM_ANALYSIS variables are significant at 1%, and the CRM_STORE
variable is significant at 5%.
Table 6
Comparison of cluster members according to DESI index variables – Descriptive
analysis and Kruskal-Wallis test
DESI_1_
CONN
DESI_2_
HC
DESI_3_
UI
DESI_4_
ITD
DESI_5_
DPS
Cluster 1
Mean
12.52
15.40
10.06
12.06
11.60
N
7
7
7
7
7
Std. Dev.
2.05
2.49
1.33
1.17
0.86
Cluster 2
Mean
11.69
12.30
8.22
7.23
10.12
N
11
11
11
11
11
Std. Dev.
1.82
2.43
1.04
1.57
1.47
Cluster 3
Mean
11.03
9.50
7.03
6.70
9.08
N
10
10
10
10
10
Std. Dev.
1.89
1.69
1.13
2.06
1.99
Total
Mean
11.66
12.07
8.25
8.25
10.12
N
28
28
28
28
28
Std. Dev.
1.92
3.14
1.62
2.77
1.81
Kruskal-Wallis test
Kruskal-
Wallis H
1.668
14.226
13.575
14.830
8.102
df
2
2
2
2
2
p-value
0.434
0.001***
0.001***
0.001***
0.017**
Source: Authors work
Note: *** statistically significant at 1%; ** 5%
211
Business Systems Research | Vol. 12 No. 1 |2021
Figure 6
Error bars (95%) of means of DESI index variables across clusters
Source: Authors work
The DESI connectivity dimensions mean values are the highest for the European
countries from Cluster 1, which means that the highly developed countries are the
most connected between three Clusters. The Cluster 1 counties have the highest
mean for the human capital connectivity dimension, so the Internet user skills and
advanced skills and development are developed the highest among the given
variables.
Oppositely, the Cluster 1 countries have the lowest mean for the DESI indicator Use
of Internet which indicates that the Internet usage, activities online, and transaction
perform the lowest for the Cluster 1 countries. Cluster 2 have also the highest mean for
the variable Human capital with a mean value of 12.30. Cluster 2 performs the lowest
at the DESI 4 variable: Digital Public Services Dimension, which is the Government
connectivity score. The DESI 4 variable is also the lowest variable for the Cluster 3
European countries, with a mean of 6.70. The Cluster 4 countries perform the best at
the Connectivity indicator with a mean of 11.03.
Figure 6 represent error bars of means of indicator variables DESI index variables
across clusters. Standard deviations are the lowest for the DESI_3_UI variable indicator.
Error bars are the largest for the DESI_2_HC values, which means that human skills are
highly developed in the Cluster 1 countries.
Conclusion
E-Commerce is one of the industries where disruptive technologies and internet
development had the strongest impact on mechanisms and operation. E-Commerce
has transformed during the past decade, and it is now positioned as one of the most
competitive industries.
The digital divide is the consequence of the discrepancy between ICT usages in
various countries. There is a gap between countries, which are ICT high adopters and
low adopters, which affect the global economy
This paper investigated the digital divide between European Union countries
enterprises by performing k means cluster analysis and the Kruskal-Wallace test.
Interesting knowledge useful for both practitioners and academics has emerged. The
212
Business Systems Research | Vol. 12 No. 1 |2021
existence of similarities between the groups of European countries has been
confirmed.
The k-means cluster analysis divided the European country's enterprises into three
separated homogenous groups. The countries in the same clusters showed similarities
between the two factors: geographical position and the e-commerce development
state.
Therefore, the three identified clusters could be determined as highly developed,
developed, and developing. The highly developed cluster countries outperformed
other cluster countries in all observed dimensions: website e-commerce functionalities,
CRM, and DESI index.
The Highly developed cluster countries are mostly Scandinavian and Benelux
countries, which have good technological and internet infrastructure, and high
standards. The developed cluster countries are mostly central European countries,
which mostly perform higher than the developing countries, and lower than the highly
developed cluster countries. The developing countries are mostly eastern European
countries as well as the countries who struggled recently with some kind of economic
crisis such as Greece and Portugal. The Developing countries cluster mostly performs
the lowest on all observed functionalities.
The e-commerce functionalities development across countries are often
interrelated with overall economic development, which indicated that the counties
which show lower economic growth have also lower e-commerce functionalities.
The European e-commerce enterprise countries clustering could be very useful for
further investigations on the topic. The knowledge that e-commerce functionalities
are not evenly distributed across European countries could be useful for both
investigators and practitioners. The clusters could be analyzed separately or the highly
developed cluster practice could serve as a benchmark for other countries. This could
be useful for practitioners as well.
Furthermore, this study is not without limitations, further investigations could
concentrate on the particular sector to get more comprehensive results. Additionally,
more variables could be included, such as more website functionalities, or more CRM
dimensions. Future investigations should also consider newer trends in e-commerce
such as social media and mobile shopping.
References
1. Agnihotri, R. (2021), “From sales force automation to digital transformation: how social
media, social CRM, and artificial intelligence technologies are influencing the sales
process”, In Jaramillo, F., Mulki, J.P. (Ed.), A Research Agenda for Sales, Cheltenham,
Edward Elgar Publishing, pp. 21-47.
2. Ahmad, N. A., Ayub, A. F. M., Khambari, M. N. (2019), “Gender digital divide: digital skills
among Malaysian secondary school“, International Journal of Academic Research in
Progressive Education and Development, Vol. 8 No. 4, pp. 668-687.
3. Akhter, F., Buzzi, M. C., Buzzi, M., Leporini, B. (2009, September), „Conceptual framework:
How to engineer online trust for disabled users“, In 2009 IEEE/WIC/ACM International
Joint Conference on Web Intelligence and Intelligent Agent Technology, Vol. 3, pp.
614-617
4. Al-Qaisi, L., Mesleh, A., Al-Qaisi, A., Sharadqh, A., Zahran, B., Alkasassbeh, J., Olaimat,
M. (2015), „Evaluation of e-commerce website functionality using a Mamdani fuzzy
system“, Engineering, Technology & Applied Science Research, Vol. 5 No. 5, pp. 860-
863.
5. Albuquerque, A. B., Belchior, A. D. (2002), „E-commerce websites: a qualitative
evaluation“, WWW 2002 Poster Session.
213
Business Systems Research | Vol. 12 No. 1 |2021
6. Alnemr, R., König, S., Eymann, T., Meinel, C. (2010, „Enabling usage control through
reputation objects: A discussion on e-commerce and the internet of services
environments“, Journal of theoretical and applied electronic commerce research,
Vol. 5 No. 2, pp.59-76.
7. Amaro, S., Duarte, P., Henriques, C. (2016), “Travelers’ use of social media: A clustering
approach”, Annals of Tourism Research, Vol. 59 No. C, pp. 1-15.
8. Blank, G., Lutz, C. (2018), „Benefits and harms from Internet use: A differentiated analysis
of Great Britain“, New media & society, Vol. 20 No. 2, pp. 618-640.
9. Bongomin, O., Gilibrays Ocen, G., Oyondi Nganyi, E., Musinguzi, A., Omara, T. (2020),
„Exponential disruptive technologies and the required skills of industry 4.0.“, Journal of
Engineering, 2020.
10. Brynjolfsson, E., Collis, A. (2019), „How should we measure the digital economy“,
Harvard Business Review, Vol. 97 No.6, pp. 140-148.
11. Bukht, R., Heeks, R. (2017), „Defining, conceptualising and measuring the digital
economy“, Development Informatics working paper, Vol. 68., Available at
SSRN: https://ssrn.com/abstract=3431732 / (20 December 2020)
12. Cebi, S. (2013), „Determining importance degrees of website design parameters based
on interactions and types of websites“, Decision Support Systems, Vol. 54 No. 2, pp.
1030-1043.
13. Chen, I. J., Popovich, K. (2003), „Understanding customer relationship management
(CRM)“, Business process management journal Vol. 9 No. 5, pp. 672-688.
14. Chen, W., Wellman, B. (2004), „The global digital divide–within and between
countries“, IT & Society, Vol.1 No. 7, pp. 39-45.
15. Chen, Z., Li, Y., Wu, Y., Luo, J. (2017), „The transition from traditional banking to mobile
internet finance: an organizational innovation perspective-a comparative study of
Citibank and ICBC“, Financial Innovation, Vol. 3 No.1, pp. 1-16.
16. Cui, M., Pan, S. L., Newell, S., Cui, L. (2017), „Strategy, resource orchestration and e-
commerce enabled social innovation in Rural China“, The Journal of Strategic
Information Systems, Vol. 26 No. 1, pp. 3-21.
17. Di Fatta, D., Patton, D., Viglia, G. (2018), „The determinants of conversion rates in SME
e-commerce websites“, Journal of Retailing and Consumer Services, Vol. 41, pp. 161-
168.
18. E-commerce (2020), „The e-commerce guide“, available at:
https://ecommerceguide.com/ecommerce-statistics/ (20 December 2020)
19. Giua, C., Materia, V. C., Camanzi, L. (2020), “Management information system
adoption at the farm level: evidence from the literature”, British Food Journal
20. Govender, P., & Sivakumar, V. (2020), “Application of k-means and hierarchical
clustering techniques for analysis of air pollution: a review (1980–2019)”, Atmospheric
Pollution Research, Vol. 11 No. 1, pp. 40-56.
21. Grover, P., Kar, A. K., Dwivedi, Y. K. (2020), „Understanding artificial intelligence
adoption in operations management: insights from the review of academic literature
and social media discussions“ Annals of Operations Research, pp.1-37.
22. Grover, V., Chiang, R. H., Liang, T. P., Zhang, D. (2018), „Creating strategic business
value from big data analytics: A research framework“, Journal of Management
Information Systems, Vol. 2 No. 35, pp. 388-423.
23. Gunn, V., Muntaner, C., Ng, E., Villeneuve, M., Gea-Sanchez, M., Chung, H. (2019),
„Gender equality policies, nursing professionalization, and the nursing workforce: a
cross-sectional, time-series analysis of 22 countries, 2000–2015“, International journal of
nursing studies, pp. 99, 103388.
24. Harrison, D. E., Ajjan, H. (2019), „Customer relationship management technology:
bridging the gap between marketing education and practice“, Journal of Marketing
Analytics, 7(4), pp. 205-219.
25. Kim, N., Kim, W. (2018), “Do your social media lead you to make social deal purchases?
Consumer-generated social referrals for sales via social commerce”, International
Journal of Information Management, Vol. 39 pp. 38-48.
214
Business Systems Research | Vol. 12 No. 1 |2021
26. Kremez, Z., Frazer, L., Thaichon, P. (2019), „The effects of e-commerce on franchising:
Practical implications and models“, Australasian Marketing Journal (AMJ), Vol. 27 No.
3, pp.158-168.
27. Kwak, D. H., Ramamurthy, K. R., & Nazareth, D. L. (2019), “Beautiful is Good and Good
is Reputable: Multiple-Attribute Charity Website Evaluation and Initial Perceptions of
Reputation Under the Halo Effect”, Journal of the Association for Information Systems,
Vol. 20 No.11, pp.3.
28. Latif, Z., Latif, S., Ximei, L., Pathan, Z. H., Salam, S., Jianqiu, Z. (2018), „The dynamics of
ICT, foreign direct investment, globalization and economic growth: Panel estimation
robust to heterogeneity and cross-sectional dependence“, Telematics and
Informatics, Vol. 35 No. 2, pp. 318-328.
29. Li, F., Larimo, J., Leonidou, L. C. (2020), „Social media marketing strategy: definition,
conceptualization, taxonomy, validation, and future agenda“, Journal of the
Academy of Marketing Science, Vol. 49, pp. 1-20.
30. Lin, R. J., Chen, R. H., Chiu, K. K. S. (2010), „Customer relationship management and
innovation capability: an empirical study“, Industrial Management & Data Systems. Vol.
110 No. 1, pp. 111-133.
31. Neirotti, P., Raguseo, E., Paolucci, E. (2018), „How SMEs develop ICT-based capabilities
in response to their environment“, Journal of Enterprise Information Management, 31(2)
32. Nica, E. (2015), „Positive drivers of consumer trust in e-commerce.“, Journal of Self-
Governance and Management Economics, Vol. 3 No. 1, pp. 60-65.
33. Norris, P. (2001), „Digital divide: Civic engagement, information poverty, and the
Internet worldwide“, Cambridge, Cambridge University Press.
34. Ojo, A., Adebayo, S. (2017), „Blockchain as a next generation government information
infrastructure: A review of initiatives in D5 countries“, Government 3.0–Next Generation
Government Technology Infrastructure and Services, pp. 283-298.
35. Oumar, T. K., Mang’Unyi, E. E., Govender, K. K., Rajkaran, S. (2017), „Exploring the e-
CRM–e-customer-e-loyalty nexus: a Kenyan commercial bank case study“,
Management & Marketing. Challenges for the Knowledge Society, Vol. 12 No. 4, pp.
674-696.
36. Pazaitis, A., De Filippi, P., Kostakis, V. (2017), „Blockchain and value systems in the
sharing economy: The illustrative case of Backfeed“, Technological Forecasting and
Social Change, Vol. 125, pp. 105-115.
37. Pénard, T., Perrigot, R. (2017) „Online search–Online purchase in franchising: An
empirical analysis of franchisor website functionality“, Journal of Retailing and
Consumer Services, Vol. 39, pp. 164-172.
38. Richardson, L. (2020), „Coordinating office space: Digital technologies and the
platformization of work“, Environment and Planning D: Society and Space.
doi:10.1177/0263775820959677
39. Rodríguez-Ardura, I., Meseguer-Artola, A., Vilaseca-Requena, J. (2008), „Factors
influencing the evolution of electronic commerce: an empirical analysis in a developed
market economy“, Journal of theoretical and applied electronic commerce
research, Vol. 3 No. 2, pp.18-29.
40. San, O. M., Huynh, V. N., Nakamori, Y. (2004), “An alternative extension of the k-means
algorithm for clustering categorical data”, International Journal of applied
mathematics and computer science, Vol. 14 No. 2, pp. 241-247.
41. Shen, C. W., Ho, J. T. (2020), “Technology-enhanced learning in higher education: A
bibliometric analysis with latent semantic approach”, Computers in Human Behavior,
Vol. 104, pp. 106-177.
42. Statista, (2021), „E-commerce worldwide - statistics & facts“, available at:
https://www.statista.com/topics/871/online-shopping/ (08 January 2021)
43. Sugar, C. A., James, G. M. (2003), “Finding the number of clusters in a dataset: An
information-theoretic approach”, Journal of the American Statistical Association, Vol.
98 No. 463, pp. 750-763.
44. Szeles, M. R. (2018), „New insights from a multilevel approach to the regional digital
divide in the European Union“, Telecommunications Policy, Vol. 42 No. 6, pp.452-463.
215
Business Systems Research | Vol. 12 No. 1 |2021
45. Thaichon, P., Surachartkumtonkun, J., Quach, S., Weaven, S., Palmatier, R. W. (2018),
“Hybrid sales structures in the age of e-commerce”, Journal of Personal Selling & Sales
Management, Vol. 38 No. 3, pp. 277-302.
46. The Digital Economy and Society Index (2020), “DESI index 2020”, available at:
https://ec.europa.eu/digital-single-market/en/digital-economy-and-society-index-
desi (08 January 2021)
47. Tsimonis, G., Dimitriadis, S. (2014), „Brand strategies in social media“, Marketing
Intelligence & Planning. Vol. 32 No. 3, pp. 328-344.
48. Yue, T. (2020), „The Strategic Management of Travel E-Commerce Platform (OTA) in the
“Post-Epidemic” Period.“ In 2020 4th International Seminar on Education, Management
and Social Sciences (ISEMSS 2020), pp. 1018-1020.
About the authors
Božidar Jaković, Ph.D., is an Associate Professor and currently Vice Dean at the Faculty
of Economics & Business, University of Zagreb, Croatia. He received his Ph.D., MSc, and
BSc degrees from the Faculty of Economics and Business, University of Zagreb. In
addition, he is an author of numerous articles in journals on the topic of e-commerce.
His current research interests include e-commerce, web services, mobile technologies
and applications, document management, and e-learning, Knowledge
management, and Information management. He is actively engaged in several
scientific projects. The author can be contacted at bjakovic@efzg.hr.
Tamara Ćurlin is a Teaching Assistant and a Ph.D. student at the Faculty of Economics
and Business, University of Zagreb, Department of Informatics. She received her BSc
and MSc degrees from the Faculty of Economics and Business, University of Zagreb.
She is teaching Informatics and Enterprise Information Systems courses exercises. Her
current research interests include Information Technologies in Tourism, Mobile
Technologies, Knowledge management, and Information management. The author
can be contacted at tcurlin@efzg.hr.
Ivan Miloloža, Ph.D. graduated from the Faculty of Economics and Business in Zagreb
and received a Ph.D. at the Faculty of Economics in Osijek in 2015. He lived and
worked abroad in the period from 1983 to 1986 (Argentina and the Netherlands). Since
1986, he has been employed by Munja, the only Croatian battery manufacturer,
where he has performed virtually all management functions and is currently the CEO
of the Board (since 1999). He is Assistant Professor at the Department of Dental
Medicine and Health, Dean for Institutional Cooperation and Development, and
Chair of the Department of History of Medicine and Social Sciences. He has performed
many social functions in various state bodies, associations, and banks, and was a
participant and guest lecturer at numerous domestic and foreign faculties and
international conferences. The author can be contacted at ivan.miloloza@fdmz.hr.
