Adoption of Intelligent Information Systems: An Approach to the

Colombian Context

Sofía Abadía

and Oscar Avila

Department of Systems and Computing Engineering, School of Engineering, Universidad de los Andes, Bogota, Colombia

Keywords: Intelligent Information Systems, Data Analytics, Adoption, Influencing Factors.

Abstract: Enterprise Information Systems (EIS) are widely used to support operational and tactical processes of

companies and have begun, in recent years, to be used at the strategic level to support decision-making

processes. To do so, new systems, known as intelligent EIS, integrate data analytics modules to provide the

necessary information and reports to make informed decisions. There are certain influencing factors for the

adoption of such systems, however, from a first analysis of the academic literature, it was found that research

works in the domain are very scarce and even more, there are no research works on the subject in Colombia.

Consequently, this article aims at identifying the relevant factors for the adoption of intelligent EIS based on

an analysis of the academic literature, and then structuring a focus group activity with 5 experts on the subject

to obtain a first approach to the adoption of this kind of systems for the Colombian context. As a preliminary

result, we found that in the Colombian industry the most important influencing factors include cost and IT

capabilities which differs from main factors identified in the revision of the international scholar literature.

1 INTRODUCTION

There is a wide variety of technological tools that

aims at providing support to the companies' business

processes. Some of the most important support tools

are Enterprise Information Systems (EIS) and data

analytics modules, which have become very popular

in recent years due to the large amount of data

produced by both companies and customers (el Kadiri

et al., 2016).

The use of EIS can bring great benefits to

companies due to the high impact they have on

business processes at both operational and tactical

levels. Usually, systems such as Enterprise Resource

Planning (ERP) or Customer Relationship

Management (CRM) contribute to register

transactional data generated in business processes such

as financial accounting, purchasing, operations or

sales, and in the realization of descriptive reports of the

company's situation based on historical information

(Kenneth C. Laudon & Jane P. Laudon, 2014).

On the other hand, data analytics tools allow a

characterization of both customers and the business

based on historical information (Sharda et al., 2015).

https://orcid.org/0000-0002-9388-5543

https://orcid.org/0000-0001-7586-0353

By analysing the company's historical data, it is viable

to know its current situation, i.e., it is possible to

identify the failures that are occurring, as well as the

processes that are working correctly. In this manner,

the company's historical data can be used to develop

a predictive analysis, with which the company can

have an idea about the possible scenarios that may

arise both internally and in relation to customers, and

thus be able to identify new business opportunities. In

this way, data analytics makes it possible to determine

the changes that should be made in business processes

in order to improve the company's situation. (Sharda

et al., 2015)

In recent years, data analytics components have

begun to be integrated to EIS in order to support

processes at the strategic level, all thanks to advances

in analytics and business intelligence (Kenneth C.

Laudon & Jane P. Laudon, 2014). This allows

companies to profit of transactional data registered in

EIS for several strategic processes such as decision-

making, recommendations and analysis of customer

behaviour. To this end, today analytics can be

integrated to EIS through embedded modules that

collect all the information stored in the EIS and

Abadía, S. and Avila, O.

Adoption of Intelligent Information Systems: An Approach to the Colombian Context.

DOI: 10.5220/0011750300003467

In Proceedings of the 25th International Conference on Enterprise Information Systems (ICEIS 2023) - Volume 1, pages 21-31

ISBN: 978-989-758-648-4; ISSN: 2184-4992

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

perform more accurate business analysis and reports

(Z. Sun et al., 2017). Other possibility is when the

organisation has analytics tools external to the

transactional EIS, in which case, the EIS collects data

from the different processes, and then there is an

external integration with the analytics tool (Sharda et

al., 2015). It is used when the EIS and the analytics

tools were implemented by different vendors or at

different times.

This new configuration of systems are called

intelligent EIS (Jenab et al., 2019), since they take

advantage of the organisation’s information in

internal sources and combine them with external

information to make business analysis using

techniques such as artificial intelligence, analytics

and business intelligence

(Jenab et al., 2019).

Companies can also have analytics tools external to

the transactional enterprise systems, in which case,

the enterprise system collects data from the different

processes, and these are transferred to the analytics

tool to be analysed.

The adoption of intelligent EIS brings great

advantages for businesses, as a correct data analysis

helps to identify new opportunities and,

consequently, create value. However, there is little

research literature found on the factors influencing

the adoption of such systems, and to understand if

they are implemented and adopted through embedded

analytics modules, or by implementing and

integrating external analytics tools. Moreover, in

Colombia, the documentation on the adoption of

technological tools including EIS and analytic

applications is quite scarce and, so far, there are no

academic publications that analyse such aspects.

To fulfil this lack we carry out a review of the

international research literature that aims at

determining the most significant factors in the

adoption of intelligent EIS and the type of impact they

have. Then we undertake a qualitative analysis for the

Colombian context from a focus group from experts

in the domain.

This paper is structured as follows: section 2

analyses the research literature in the domain. Section

3 describes the characteristics of the focus group and

synthesize the main results of this activity. Section 4

compares and discusses the results of both activities

to draw conclusions for the Colombian context.

Finally, section 5 describes conclusions.

2 LITERATURE REVIEW

The process of conducting the literature review is

divided into three main stages: (i) Planning: it focuses

on identifying the articles’ selection criteria and

defining a framework with review questions for the

evaluation of the articles. (ii) Realization: it consists

in searching and selecting academic articles based on

the fulfilment of the selection criteria and the

possibility of answering the review questions. (iii)

Synthesis and analysis: the review questions are

applied to each of the selected academic articles and

answered according to the information obtained.

2.1 Planning

For the selection of the most relevant academic

articles, a set of criteria is defined, which are:

adoption of an intelligent business system or an

analytics tool, explanation of the determining factors

on the adoption decision process and mention of the

factor’s impact type. All the articles that meet the

above criteria are considered potential articles for the

realization of the literature review. The evaluation

framework (see Table 1) is structured in terms of

three types of concepts, which are described below.

1. Category: these are the main key points to be

analysed in the articles.

2. Criterion: each category has a set of criteria that

help us evaluate the articles.

3. Research question: each criterion is associated

with a research question, which is used to

analyse the contribution of the article to the

defined criteria.

Table 1: Literature assessment framework.

Context cate

Size of the

company(ies)

What is the size of the

company(ies) studied in the

article?

Type of study Is it a qualitative or quantitative

stud

Analytics category

Type of EIS On what type of EIS is the adoption

anal

sis done in the article?

Component Is the adoption of an internal or

external analytics component

ein

anal

Adoption category

Method of study What method is used to study

ado

tion?

Factors What are the main factors

influencin

ado

tion?

Impact Do the identified factors positively

or negatively impact adoption?

Significance Do the identified factors have a

nificant effect or not?

ICEIS 2023 - 25th International Conference on Enterprise Information Systems

The context category is to clearly understand the

purpose of the research and the industrial context in

which it was carried out. The following criteria are

proposed: Size of the analysed company(ies), the

industrial sector to which the companies studied

belong and the type of study between qualitative and

quantitative. The analytical category seeks to

understand the type of tool studied in the article and

its relationship with EIS, the purpose is to identify if

the analytics tool is an internal module of the business

system or it is a completely separate tool. Finally, the

adoption category refers to the Study method, e.g.

Technology-Organization-Environment (TOE)

framework, the factors that according to the study

have an impact on the adoption decision, the impact

that each factor has on the decision to adopt the

technology (positive or negative) and the significance

that corresponds to the factors that are relevant to the

adoption process. A significant factor is a

determining factor in the decision to adopt a tool,

while a non-significant factor is not very relevant to

the process.

2.2 Realization

Having the selection criteria for the academic articles

ready, a keyword search is started. Scopus, a database

that indexes academic articles published in different

scientific journals, conference proceedings and book

chapters, among others, is used for this search.

As intelligent EIS can be implemented and

adopted through embedded analytics modules or by

implementing traditional EIS integrated with external

analytics tools, we used keywords aimed at searching

both possibilities. The query used for the search of

academic articles using defined keywords is

described as follows.

TITLE-ABS-KEY ((("intelligent enterprise

information systems" OR "intelligent enterprise

systems" OR "iEIS" OR "intelligent EIS" OR "I-

ERP" OR "intelligent ERP" OR "I-CRM" OR

"enterprise information systems" OR "enterprise

systems" OR "enterprise systems" OR "EIS" OR

"ERP" OR "CRM" ) AND ( "business intelligence"

OR "data analytics" OR "big data analytics" OR

"BDA" ) AND ( "technology adoption" OR

"technological adoption" OR "IT adoption" OR

"TOE" OR "DOI" OR "UTAUT2" OR "TRA" OR

"TAM" )) AND ( LIMIT-TO ( SUBHEARING , 

"COMP" ) ) AND ( LIMIT-TO ( PUBYEAR , 2022 )

OR LIMIT-TO ( PUBYEAR , 2021 ) OR LIMIT-TO

( PUBYEAR , 2020 ) OR LIMIT-TO ( PUBYEAR ,

2019 ) OR LIMIT-TO ( PUBYEAR , 2018 ) OR

LIMIT-TO ( PUBYEAR , 2017 ) OR LIMIT-TO (

PUBYEAR , 2016 ) ) )

At the beginning there were more than 200 results,

these results were downloaded to Excel with the

information of the source, the title and the summary.

Once in Excel, a first elimination was performed

according to the titles, with this filter the number of

articles went down to 46. Then another filter was

performed, this time with the summary of the articles,

which resulted in 24 articles. However, when the

reading and application of the evaluation framework

began, it was found that certain articles were not of

great relevance to the study, so they were eliminated,

thus ending up with 19 articles.

2.3 Synthesis and Analysis

Based on the application of the evaluation framework

to the set of articles, an analysis is presented

indicating the similarities and differences between the

articles and the conclusions reached.

Regarding the criterion Type of EIS and

Components (see Table 1), the conclusions are

summarized in Table 2 and can be divided into 3

groups of papers. The first group of papers studies the

adoption of intelligent EIS, where 4 of them focus on

ERP adoption (Xu et al., 2017), (Nwankpa et al.,

2016), (Mayeh et al., 2016), (Elkhani et al., 2014), 3

of them on CRM adoption (Cruz-Jesus et al., 2019),

(Hasan Salah et al., 2019), (Ahani et al., 2017) and

one on EIS in general, but hosted in the cloud (Şener

et al., 2016). Although these papers refer to the

adoption of intelligent EIS, they do not provide

evidence of how they are integrated with internal or

external analytics modules or components.

The second group of papers ((Maroufkhani et al.,

2020), (Schüll & Maslan, 2018), (Park & Kim, 2021),

(Maroufkhani et al., 2022), (Angwar, 2018), (Khan &

Brock, 2017), (El-Haddadeh et al., 2021), (S. Sun et

al., 2018)) discuss the adoption of data analytics as

an external stand-alone tool that generates

information by using, among others, the

organisation’s internal transactional data sources.

Finally, in the third group, we identified two

subgroups. On the one hand papers dealing with the

adoption of analytics as an external component, but

mentioning explicitly that it is fed by data coming

from one or more EIS (Alaskar et al., 2021),

(Kyriakou et al., 2020) and on the other hand, articles

analysing the adoption of analytics as an internal

embedded module of an EIS (Junior et al., 2019).

Adoption of Intelligent Information Systems: An Approach to the Colombian Context

Table 2: Tool adopted.

Tool T

e Article

Intelligent

EIS

ERP (Xu et al., 2017),

(Nwankpa et al., 2016),

(Mayeh et al., 2016),

(Elkhani et al., 2014)

CRM (Cruz-Jesus et al.,

2019), (Hasan Salah et

al., 2019), (Ahani et al.,

2017

)

EIS on clou

(Şener et al., 2016)

Stand-

alone data

analytics

External

component

(Maroufkhani et al.,

2020), (Schüll &

Maslan, 2018), (Park &

Kim, 2021),

(Maroufkhani et al.,

2022), (Angwar, 2018),

(Khan & Brock, 2017),

(El-Haddadeh et al.,

2021), (S. Sun et al.,

2018

)

EIS with

data

analytics

EIS powered

by an external

analytics

com

onent

(Alaskar et al., 2021),

(Kyriakou et al., 2020)

Internal

(embedded)

analytics

component in

ERP

(Junior et al., 2019)

Regarding the question related to the criterion size

of the company(ies), Table 3 shows the articles for

which size is considered a relevant factor, the

relationship between the type of tool studied in the

article and the size of the companies, and the impact

and significance it has on each one.

As it can be seen in Table 3, for all the articles that

consider size as a factor to be taken into account, its

impact is positive, which means that the larger the

company, the more likely it is that the process of

adopting the tool can be initiated. However, there is

no unanimity regarding its significance, since one out

of 5 articles considers it a non-significant factor (S.

Sun et al., 2018).

When analysing the results by type of tool

adopted, in the case of intelligent EIS, even though in

(Şener et al., 2016) the company’s size is argued to

have a positive impact and be significant, making it a

relevant factor, the size of the companies

participating in this study is not presented. In the

remaining cases, (i.e., standalone data analytics and

EIS with data analytics) the articles study companies

of all size categories, namely, small, medium and

large.

Table 3: Company’s size as relevant factor vs type of tool.

Intelligent

EIS

Stand-alone

data analytics

EIS with

data

anal

tics

Size of

the

company

relevant

(Şener et

al., 2016):

positive

and

significant

(Angwar,

2018): positive

and significant

(Khan & Brock,

2017): positive

and significant

(S. Sun et al.,

2018): positive

and not

significant

(Kyriakou

et al.,

2020):

positive

and

significant

Regarding the criterion type of study, the vast

majority of cases conducted quantitative studies (see

Table 4) which usually began with an analysis of the

academic literature on the subject in order to identify

the significant factors for technology adoption.

Subsequently, a survey was developed and distributed

to the participating companies. Once the results were

obtained, a process of elimination of incomplete

surveys was carried out. Finally, different statistical

techniques, such as linear regression, were used to

determine the most significant factors.

In the case of qualitative studies, the first step was

the same, the study of academic literature and

identification of the most important factors. Then, a

group of experts on the subject were surveyed to get

their opinion on the information collected and,

according to this, the factors were ordered from most

to least significant by means of techniques such as the

Analytic Hierarchy Process (AHP), which is a multi-

criteria decision method (Şener et al., 2016).

However, no statistical analysis was performed at any

time.

Table 4: Type of study.

Type of

stud

Article

Quantitative

study

(Xu et al., 2017), (Cruz-Jesus et al.,

2019), (Kyriakou et al., 2020), (Junior et

al., 2019), (Alaskar et al., 2021),

(Nwankpa et al., 2016), (Maroufkhani et

al., 2020), (Ahani et al., 2017), (Schüll

& Maslan, 2018), (Mayeh et al., 2016),

(Park & Kim, 2021), (Maroufkhani et

al., 2022), (Angwar, 2018), (Khan &

Brock, 2017), (El-Haddadeh et al.,

2021), (Elkhani et al., 2014)

Qualitative

study

(Hasan Salah et al., 2019), (Şener et al.,

2016), (S. Sun et al., 2018)

With respect to the criterion method, as expected,

the Technology-Organization-Environment (TOE)

ICEIS 2023 - 25th International Conference on Enterprise Information Systems

framework is the most commonly used adoption

model, being used in 13 out of 19 articles (see Table

5). In addition, there are two more articles that made

use of variations of the TOE, namely, TOEP (Ahani

et al., 2017) and TOES (Angwar, 2018). Both study

the same three categories of the TOE, which are

Technology, Organization and Environment,

however, they add a new one. TOEP adds the

processes category because of its focus on the

company's business processes. TOES add the security

category which considers factors such as information

security and privacy. Regarding other methods, some

works use different adoption models simultaneously.

For instance, (Junior et al., 2019) uses TOE,

Diffusion of Innovations (DOI) and Inter-

organizational Relations (IOR) theory, (Hasan Salah

et al., 2019) uses TOE, DOI and Resource-based

View (RBV) and (S. Sun et al., 2018) uses TOE, DOI

and Institutional theory. The combination of methods

allowed researchers to have a broader view of

significant factors of adoption.

Table 5: Adoption models.

Ado

tion model Article

TOE (Xu et al., 2017), (Cruz-Jesus et al.,

2019), (Kyriakou et al., 2020),

(Junior et al., 2019), (Alaskar et al.,

2021), (Hasan Salah et al., 2019),

(Maroufkhani et al., 2020), (Schüll

& Maslan, 2018), (Park & Kim,

2021), (Maroufkhani et al., 2022),

(El-Haddadeh et al., 2021), (Şener et

al., 2016), (S. Sun et al., 2018)

DOI (Junior et al., 2019), (Hasan Salah et

al., 2019

)

(

S. Sun et al., 2018

)

Technology

Acceptance

Model (TAM)

(Mayeh et al., 2016), (Khan &

Brock, 2017), (Elkhani et al., 2014)

TOEP (Ahani et al., 2017)

TOES (Angwar, 2018)

IOR

(

Junior et al., 2019

)

Real Options

Theor

(Nwankpa et al., 2016)

RBV (Hasan Salah et al., 2019)

Institutional

theor

(S. Sun et al., 2018)

Concerning the factor criterion, this analysis is

characterized according to the adopted tools within 3

groups, namely, intelligent EIS, stand-alone data

analytics and EIS with data analytics. For this

analysis the information is gathered in Table 6 which

describes for each tool the factors influencing

adoption, as well as the impact (positive or negative)

and the significance level of each factor where S

stands for significant (i.e., it is a determining factor in

the decision to adopt a tool) and NS means non-

significant (i.e., it is not very relevant to the process).

According to Table 6 the most currently found

determining factor in the intelligent EIS group is

management support, which implies that the

management is involved in the EIS adoption process

(Xu et al., 2017), i.e., that it knows the competitive

advantages that can be provided by the technological

tool and is willing to accept its cost. This factor is

mentioned in 7 out of 8 articles and in all cases, it has

a positive impact that is significant.

Other important factors are described as follows.

Relative advantage relates to the increasing of

benefits that the new intelligent EIS can bring (Xu et

al., 2017). It has a positive impact that is considered

significant in all the articles. Competitive market

pressure concerns the pressure level a company feels

to implement a certain EIS due to market competition

(Xu et al., 2017). It is determined to have a positive

impact that is significant. Compatibility refers to the

degree of consistency between the EIS to be adopted

and the values, needs, experiences and practices of

the company (Xu et al., 2017). It also has a positive

impact which is significant. In the case of the factors

complexity and government policies, even though all

research works consider the former has a negative

influence and the later a positive one, there is no

consensus regarding their significance, as in both

cases, the same research work (Şener et al., 2016)

determines that these two factors are not significant.

This may be due to 2 reasons, the context and the type

of study: regarding the context, this work is the only

one that focuses on EIS in the cloud, which may

imply a change in the determinants, since the

complexity of deployment in this type of model does

not make its adoption easier or more difficult, and

possibly in the environment in which the analysis was

made, it is possible that government policies are

neutral regarding the adoption of cloud technologies.

Now, regarding the type of study, this article conducts

a qualitative study unlike articles (Xu et al., 2017),

(Hasan Salah et al., 2019) and (Ahani et al., 2017).

In the second’s group case, it is to say, the articles

dealing with stand-alone data analytics, the

determinants for the adoption of data analytics tools

are practically the same as for intelligent EIS, i.e., all

the EIS factors also appear in the data analytics list,

but 2 more factors are added: cost and organizational

readiness (see Table 6).

Adoption of Intelligent Information Systems: An Approach to the Colombian Context

Table 6: Factors for the three groups of tools.

Tool Factors Im

nific Article

Intelligent EIS

Management support + S

(Xu et al., 2017), (Cruz-Jesus et al., 2019),

(Nwankpa et al., 2016), (Hasan Salah et al., 2019),

(

Ahani et al., 2017

)

(

Elkhani et al., 2014

)

(

Şener et al., 2016

)

Relative advantage + S

(Xu et al., 2017), (Nwankpa et al., 2016),

(Hasan Salah et al., 2019), (Ahani et al., 2017), (Şener et al., 2016)

Competitive market

ressure

+ S

(Xu et al., 2017), (Cruz-Jesus et al., 2019), (Hasan Salah et al., 2019), (Ahani

et al., 2017), (Şener et al., 2016)

Compatibilit

+ S (Xu et al., 2017), (Hasan Salah et al., 2019), (Ahani et al., 2017)

Government policies +

S (Hasan Salah et al., 2019), (Ahani et al., 2017)

(

Şener et al., 2016

)

Complexity

(

Xu et al., 2017

)

(

Hasan Salah et al., 2019

)

NS (Şener et al., 2016)

Expected benefits + S (Nwankpa et al., 2016), (Mayeh et al., 2016), (Elkhani et al., 2014)

IT capabilities + S (Cruz-Jesus et al., 2019), (Ahani et al., 2017)

IT infrastructure

/resources

+ S

(Hasan Salah et al., 2019), (Şener et al., 2016)

Securit

+ S (Hasan Salah et al., 2019), (Şener et al., 2016)

Size + S (Şener et al., 2016)

Stand-alone data analytics

Management support + S

(Maroufkhani et al., 2020), (Schüll & Maslan, 2018), (Park & Kim, 2021),

(Maroufkhani et al., 2022), (Angwar, 2018), (S. Sun et al., 2018)

Competitive market

pressure

(Maroufkhani et al., 2020), (Schüll & Maslan, 2018), (Angwar, 2018), (El-

Haddadeh et al., 2021

)

NS (S. Sun et al., 2018)

Compatibility

S (Maroufkhani et al., 2020), (Maroufkhani et al., 2022), (Angwar, 2018)

NS (Park & Kim, 2021), (S. Sun et al., 2018)

Complexity - S

(Maroufkhani et al., 2020), (Maroufkhani et al., 2022), (Angwar, 2018), (El-

Haddadeh et al., 2021), (S. Sun et al., 2018)

Organizational

readiness

(Maroufkhani et al., 2020), (Maroufkhani et al., 2022), (El-Haddadeh et al.,

2021

)

(

war, 2018

)

Expected benefits + S (Park & Kim, 2021), (Khan & Brock, 2017), (El-Haddadeh et al., 2021)

Relative advantage + S (Maroufkhani et al., 2020), (Angwar, 2018), (S. Sun et al., 2018)

Government policies

(

Park & Kim, 2021

)

(

El-Haddadeh et al., 2021

)

(

S. Sun et al., 2018

)

(

Maroufkhani et al., 2020

)

Size

(

war, 2018

)

(

Khan & Brock, 2017

)

NS (S. Sun et al., 2018)

IT infrastructure /

resources

+ S

(Khan & Brock, 2017), (El-Haddadeh et al., 2021), (S. Sun et al., 2018)

IT ca

abilities + S

(

Schüll & Maslan, 2018

)

(

Park & Kim, 2021

)

Security and privacy

(Angwar, 2018)

- (Park & Kim, 2021), (S. Sun et al., 2018)

Cost - S (Park & Kim, 2021), (S. Sun et al., 2018)

EIS with data

analytics

Mana

ement su

ort + S

(

Junior et al., 2019

)

(

Alaskar et al., 2021

)

Competitive market

ressure

+ S

(Junior et al., 2019), (Alaskar et al., 2021)

Com

atibilit

+ S

(

Junior et al., 2019

)

(

Alaskar et al., 2021

)

IT capabilities

(

riakou et al., 2020

)

(

Junior et al., 2019

)

Expected benefits + S (Alaskar et al., 2021)

Relative advantage + S (Junior et al., 2019)

Size + S (Kyriakou et al., 2020)

Organizational readiness is the ability to make

available the technological, financial and human

resources necessary for the adoption of the analytics

technology (Angwar, 2018). Cost refers to the amount

ICEIS 2023 - 25th International Conference on Enterprise Information Systems

of money that needs to be invested to be able to adopt

an analytics technological tool which includes

acquisition cost, modifications to the company's

infrastructure, employee training and hiring new

personnel. The most mentioned factors for this group

are management support, competitive market

pressure and compatibility. When there is more

consensus in in the management support factor,

which is addressed in 6 out of 8 articles

Regarding negative impact factors, complexity

that is the degree of difficulty perceived by the

company when faced with a technological tool

(Angwar, 2018) is looked as significant and

mentioned in this way in 5 articles. It is worth to note

that, in this case, the size of the company can be

considered a determining factor, since it is mentioned

in 3 articles and is significant in 2 of them. In

addition, the impact is always positive, which means

that the larger the company, the easier the adoption.

Finally, for the third group, i.e., EIS with data

analytics, two papers study the adoption of an

external analytics component fed by EIS (Kyriakou et

al., 2020)(Alaskar et al., 2021), while one research

work studies the adoption of an internal embedded

analytics component in an ERP (Junior et al., 2019).

For this group the analysis shows that the most

determining factor is again management support,

followed by competitive market pressure,

compatibility and IT capabilities (see Table 6).

However, there is a discrepancy regarding the

significance of the latter, since in (Kyriakou et al.,

2020) it is considered a significant factor and in

article (Junior et al., 2019) it is not. It is worth to note

that here complexity is not considered by any of the

articles and size is only mentioned in article

(Kyriakou et al., 2020), so it is not possible to reach a

conclusion of their importance in the process of

adopting enterprise systems with embedded data

analytics modules. On the other hand, management

support, competitive market pressure and

compatibility are once again determining factors, as

in the case of adoption of enterprise systems and

stand-alone data analytics.

3 ADOPTION OF INTELLIGENT

EIS IN COLOMBIA

This section is intended to carry out a first approach

to the identification of the determining factors for the

adoption of intelligent EIS in the context of the

Colombian industry. To collect information, a focus

group is conducted. The results of the literature

review will be used as a basis to guide this activity.

3.1 Focus Group

The objective of the focus group is to determine if the

determinants identified in the literature review are

also determinants in the context of the Colombian

industry or if discrepancies are found.

For the focus group, 5 professionals were selected

to participate in this activity considering their work

experience in the field of data analytics and enterprise

information systems. They have knowledge in the

different EIS and data analytics tools available in the

market. The group is composed of men and women

between 35- and 65-years old working in private

sector companies, in the IT department or as IT

consultants. The information on the participants'

current position, their companies’ size and the

industry sector to which they belong is presented as

follows:

Table 7: General information of the participants.

Participant Current

osition

Company

size

Industrial

secto

P1:

Partici

ant 1

IT Manager Medium Consulting

P2:

Participant 2

BI Architect Large Technology

P3:

Participant 3

Product

Manage

Large Technology

P4:

Partici

ant 4

Expert

inee

Large Technology

P5:

Participant 5

Independent

Consultant

Large Financial

This activity lasts approximately one hour, during

which the participants discussed among themselves

and answered the researcher's questions. The focus

group questions are semi-structured, that is, there are

some basic questions, and the researcher can add

questions according to the evolution of the

discussion. The used questions are presented below:

• Does your company currently use an EIS?

• Does your company currently use a data

analytics tool?

• Is the analytics tool external or is it a module

embedded in the EIS? Who is your supplier?

• During the literature review phase, we identified

factors influencing the adoption of intelligent EIS.

How would you prioritise these factors, from the least

to the most significant?

• Why do you rank them this way? Would you

add any other factors?

Adoption of Intelligent Information Systems: An Approach to the Colombian Context

The first questions were asked with the objective

of gathering general information about the type of

EIS and analytics tool that has been implemented in

the participants' companies, while the subsequent

questions aimed at generating a discussion about the

determining factors for the adoption of data analytics

in companies of the Colombian industry.

The focus group was conducted virtually and

recorded considering confidentiality criteria. During

the session, participants had the right to ask to be

identified by their name or a pseudonym. All

transcribed fragments were anonymized. The

recording of the session is securely stored in a private

folder in the cloud to which only the researchers have

access. All these norms were presented to the

participants in a written informed consent, which was

signed by them prior to the session.

3.2 Synthesis

We present the synthesis of the answers for each of

the questions as follows.

Does your company currently use an EIS?

In most cases, the participants' companies do not

use a single EIS, but a combination of several tools

within which the transactions associated with their

business processes are registered. It means that in

most of the cases they do not have an integrated EIS

for all areas of the business.

In the case of participant 1, his company manages

the different business processes separately and with

different tools, for example, it uses one of the leading

ERP systems in the market for financial processes,

however, the main system in which they run most of

their processes was custom developed. On the other

hand, the organisation of participant 2 works with

multiple internal systems, from which information is

collected. The information is also collected from

different sources such as JSON or CSV files and

applications, and then a data warehouse is assembled.

Does your company currently use a data analytics

tool?

All participants' companies have one or more

analytics tools and different analytics strategies. In

general, companies use third parties' analytics tools

and data science techniques implemented in-house.

The only exception to this is participant 4's company,

which works exclusively with one of the leading

cloud computing services companies in the market.

Regarding techniques implemented in-house, 3 of

the 5 participants (Participant 2, Participant 3 and

Participant 5) mention that in their companies data

science is performed internally, with which they can

obtain a better knowledge of their clients.

Specifically, it is mentioned that in the company of

Participant 2 an information analysis system was

created based on a data warehouse in which the

collected data is stored and, once all the data is there,

a commercial BI tool is used to create reports on the

processes. On the other hand, participant 3's company

has developed its own algorithms for demand

forecasting. Finally, participant 5 indicates that they

develop "in situ" algorithms in Python for predictive

analytics through linear regression models,

classification, decision trees and correlation between

variables. These algorithms are invoked then by a

commercial BI tool for performing business

intelligence processes.

How would you prioritise adoption influencing

factors, from the least significant to the most

significant?

Regarding the relevance of factors identified in

the literature review, it was not possible to reach a

final/total consensus in the group, since viewpoints

were divided between organisational and

technological factors. However, cost is a common

factor among all, being always placed among the first

places. Likewise, management support, complexity,

competitive market pressure and IT capabilities are

important for the participants. The reasoning for such

results is presented below:

• Cost: companies can contract cloud services

specifically for what they need and thus reduce

costs, however, if a company wants accurate

analytics process it must develop robust tools,

which comes with a high cost. Additionally, the

cost of hiring expert personnel must also be

taken into account.

• Management support: it is important for the

management to know exactly the objective of

analytics tools and be aware of the benefits that

can be gained from them. In this way, they will

be willing to assume the cost of adoption and

staff training.

• Complexity: many of the tools available in the

market may be difficult to handle at the

beginning, which would imply a great expense

in staff training. For this reason, it is important

to maintain the level of complexity not too

high.

• Competitive pressure from the market: if a

company starts to offer a better service to

customers, other companies must start to

innovate in order not to be left behind, in the

words of one of the participants "those who do

not move, die".

• IT capabilities: it is important for a company to

have expert staff in data analysis and with the

ICEIS 2023 - 25th International Conference on Enterprise Information Systems

necessary knowledge on the use of the tool

adopted, otherwise data analytics processes

will not be performed correctly and faithful.

For certain participants, the fact that there are

analytics tools available in the cloud makes some

factors not so decisive when looking for their

adoption. These factors are:

• Security and privacy: cloud service providers

guarantee the confidentiality, integrity and

availability of customer information, so

security and privacy take a back seat.

• IT infrastructure: it is not necessary to have a large

number of on-premise servers and facilities

because the data can be stored on cloud servers.

One of the most debated factors by the

participants is the size of the company, as for some

the size is related to the available resources of the

organization, which means that a small company, for

example, could not have the necessary resources to

acquire an analytics tool. On the other hand, other

participants argue that for smaller companies,

analytics may be the only differentiator that allows

them to increase their competitiveness and thus grow.

4 DISCUSSION

From the analysis of the literature review and the

focus group, it is possible to determine that there are

both coincidences and discrepancies regarding the

adoption of data analytics tools and strategies. At this

point it is important to note that the literature review

is a study of international literature, while the focus

group is based on the Colombian context, so it is

reasonable to find certain differences.

First, concerning the integration of EIS and

analytics tools and the parallelism in their adoption

we discuss as follows both perspectives. Regarding

the literature review perspective, the search and

selection process of articles on EIS integrated with

data analytics, only 2 articles were obtained in which

the data analytics components were fed by the

company EIS (Alaskar et al., 2021), (Kyriakou et al.,

2020) and one in which the data analytics component

was embedded in the EIS (Junior et al., 2019) (see

Table 2). In the case of the 8 articles that talk about

the adoption of intelligent EIS, the relationship that

these could have with other systems or analytics

components was not clearly mentioned, and authors

addressed the capabilities of such systems to generate

specific reports using analytics technics.

Concerning Colombian context perspective, all

participants mentioned their companies have multiple

EIS that are not integrated and register data generated

independently for each process. As a consequence,

they do not have centralized information for all the

areas of the business, but only have access to isolated

data for each process. However, once the data has

been successfully stored, the business intelligence

process begins, in which analytical tools are used to

generate knowledge from this data, so that the current

situation of the company as a whole can be

understood, and informed decisions can be made.

According to these conclusions from the literature

and the focus group, we can say that the adoption of

a centralized EIS and the adoption of a data analytics

tool are not necessarily linked. Companies can adopt

an EIS with data analytics modules embedded if this

system is the only one to be used across the entire

enterprise, otherwise the reports generated by the

analytics process would not be completely accurate,

as the enterprise system would not have access to all

enterprise data, but only to some areas of the business.

Otherwise, in most of the cases the process of

adoption of EIS and data analytics tools are

independent so that links between both adoption

processes and integration between both types of tools

are not strictly mentioned nor addressed.

Second, between the literature review and the

focus group, the determinants for the adoption of

independent data analytics tools vary. Table 8 shows

the most frequently identified factors in both the

literature review and the focus group. From this, it can

be seen that 3 of the 5 factors are determinants in both

cases (management support, competitive market

pressure and complexity), however, the other 2 are in

each case completely different.

Table 8: Literature review vs focus group factors.

Factor

Literature

review

Focus group

Management

ort

X X

Com

atibilit

Complexity X X

Competitive

market

ressure

X X

Expected benefits X

IT Capabilities X

Cost X

From the analysis of the literature review, it is

found that the factor with the least relevance when

adopting a data analytics tool is cost, which is only

mentioned in 2 articles, (Park & Kim, 2021) and (S.

Sun et al., 2018). However, it was the most

determining factor in the focus group because the

participants agreed that the cost of a robust analytics

Adoption of Intelligent Information Systems: An Approach to the Colombian Context

tool can be quite high. Regarding IT capabilities and

skills of employees, in the case of the Colombian

context it is a relevant factor because of the shortage

of professional profiles in the country with experience

in this field, even though in the literature review it

was not identified as an influencing factor.

5 CONCLUSIONS

In this document an extensive analysis of the main

factors that can affect the process of adoption of

intelligent EIS in companies is made, as well as a first

approach to this topic focused on the Colombian

industry. In order to carry out this analysis,

techniques such as a literature review and a focus

group are used. Regarding the literature review, an

evaluation framework is created to analyse the

selected set of academic articles. Each article is

evaluated according to 3 categories: research context,

type of data analytics studied and adoption process.

Through the analysis of the academic articles, it is

possible to identify the determining factors for the

adoption of intelligent EIS.

To get a first approach on the subject to the

Colombian context, a focus group is conducted with

5 experts on the subject of EIS and data analytics. The

focus group is intended to identify the main

characteristics of intelligent EIS in Colombian

companies as well as the main factors influencing

their adoption.

Based on the analysis of both the literature review

and the focus group, a comparison is made to

determine the similarities and differences that exist

between them. Through this comparison, it was found

that there are three factors that are determinant in both

cases and two that are different. On the one hand the

common factors are: management support,

competitive market pressure and complexity. On the

other hand, specific factors for the literature review

are compatibility and expected benefits, while

specific factor for the Colombian companies from the

focus group are cost and the company's IT capabilities

and skills.

Concerning the limitation of our study, it is worth

bearing in mind that the results of the focus group is

a first approach to the topic, since the participation of

5 experts is not enough to determine the process of

adoption of intelligent EIS to the whole Colombian

context. As future work, the information collected

through this study can be used to design additional

collection tools such a surveys’ questionnaires in

order to carry out a representative analysis of

quantitative nature.

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Adoption of Intelligent Information Systems: An Approach to the Colombian Context