The Application of Knowledge Management to Overcome Barriers to

Enterprise Architecture Adoption: A South African Motor Vehicle

and Asset Finance Case Study

Innocent Gumede, J. P. van Deventer

, Hanlie Smuts

and Joyce Jordaan

Department of Informatics, University of Pretoria, Lynnewood Road, Hatfield, South Africa

Keywords: Enterprise Architecture, Knowledge Management, Adoption Barriers.

Abstract: Information Technology (IT) enables an organisation to gain competitive advantage by exploiting new

opportunities and capabilities offered by evolving technologies. Therefore, it is required to holistically align

IT strategy with organisational strategy, and Enterprise Architecture (EA) is considered as a means to achieve

such alignment. However, EA adoption is impacted by many organizational barriers and in particular

organisational culture factors. Knowledge Management (KM) is a candidate to address these organisational

culture issues. Therefore, the purpose of this study, was to understand the barriers to EA adoption, as well as

the KM interventions likely to increase the success of EA initiatives. The study was conducted in the South

African motor vehicle and asset finance industry and the lack of understanding the purpose of EA, as well as

employees not actively participating in the development of EA, were identified as major barriers. The KM

interventions identified to be effective in overcoming the barriers pointed to the promotion of knowledge

sharing between employees and the EA team, and the increased involvement of EA stakeholders and users in

EA development. By considering the research findings, organisations may apply KM, in overcoming barriers

that prevent the successful implementation of EA initiatives.

1 INTRODUCTION

Enterprise Architecture (EA) is considered to be a

means to achieve and maintain alignment between the

shifts in organisational strategy, business processes

and an increasingly complex Information Technology

(IT) landscape (Löhe and Legner, 2014, Bente et al.,

2012). According to Pham, et al. (2013), EA

comprises of a set of processes and artefacts applied

to transform an organisation’s business strategy into

an IT roadmap with the aim to implement an

organisation’s business strategy. EA is therefore

positioned to support strategic enterprise planning by

establishing the best use of available information,

processes and technology in fulfilling business and IT

strategies (Pham et al., 2013).

However, EA is perceived to be an invasive

endeavour that involves interactions with all the

dimensions of an organisation encountering many

implementation issues and barriers such as resistance

https://orcid.org/0000-0002-3598-0921

https://orcid.org/0000-0001-7120-7787

to change or a general lack of understanding of the

purpose or role of an EA endeavour (Syynimaa,

2015). The wide scope of EA further compounds the

situation as multiple dimensions such as culture,

technology, structure and procedures are essential

contributors to successful EA implementation (Jahani

et al., 2010). The role of employees is also

emphasised as they are responsible for the

management of the business and information

exchange operations of the organisation, to the extent

that modern enterprises are perceived as “human-

driven” (Gilliland et al., 2015: 43). Strategic

initiatives such as EA therefore also depend on

effective human involvement. In the context of EA

initiatives, the introduction and compliance to EA

may limit the design freedom of employees and such

constraints have a potential to lead to significant

resistance within the organisation (Aier and Weiss,

2012, Aier, 2014). There is a requirement for

solutions to overcome the difficulties encountered

Gumede, I., van Deventer, J., Smuts, H. and Jordaan, J.

The Application of Knowledge Management to Overcome Barriers to Enterprise Architecture Adoption: A South African Motor Vehicle and Asset Finance Case Study.

DOI: 10.5220/0008053500450056

In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019), pages 45-56

ISBN: 978-989-758-382-7

during EA implementations in order to conduct EA

successfully (Aier, 2014).

One enabler of the collective intelligence in an

organisation supporting the organisation’s strategic

objectives, is Knowledge Management (KM). KM

ensures that knowledge is created, communicated and

applied to achieve business goals (Wang and Yang,

2016). It enables an organisation to become more

competitive by facilitating the processes of

identifying, managing and leveraging individual and

collective knowledge (Liao, 2003). Furthermore, KM

is able to address culture, people issues, technical,

structural and procedural elements of an organisation

as it provides appropriate practices, tools, and

methods to effect changes to the culture of an

organisation (Corfield and Paton, 2016). In addition,

the implementation of KM processes infuses changes

in organisations, which affects employees and the

organisation’s operation (Smuts and Juleka, 2018,

Rusly et al., 2015). Therefore, the research question

that this paper aims to address is: “how can KM be

applied to overcome the barriers experienced in EA

implementations?”. By addressing this question,

organisations are able to apply KM and its associated

processes to successfully adopt EA.

In Section 2 we present the background to the

study followed by the research approach in Section 3.

Section 4 details the data analysis and findings, while

Section 5 concludes the paper.

2 BACKGROUND

Information collection, -communication, and -

exchange are important constituents of EA (Buckl

and Schweda, 2009). In addition, the dynamic nature

of EA is also about transformation and modelling for

change (Trinskjær, 2009). Successful EA

implementation offers a wide range of benefits to an

organisation, including reduced complexity, cost

savings, more effective decision-making processes,

successful delivery of transformation projects and the

strategic capability arising from the better digital

business platform built during the transformation

(Tamm et al., 2015).

EA adoption is a resource intensive undertaking

requiring significant investments in costs, time and

effort. As a result, a poorly executed EA

implementation results in significant losses and

problems for the organisation (Smuts and Juleka,

2018).

However, KM has the potential capacity of

supporting EA adoption by providing access to

existing resources within the organisation that may

support adoption. This would then have the effect of

reducing the acquisition of additional resources,

already present, to enable and support EA adoption

(Wang and Yang, 2016).

In the next sections we consider EA adoption and

acceptance, and highlight the role of KM

interventions to increase the possibility of success for

EA initiatives.

2.1 EA Adoption and Acceptance

EA provides the support required to enable an

organisation to achieve effectiveness (Ross et al.,

2006), agility (Bente et al., 2012), durability

(Hausman, 2011) and overall efficiency

(Schekkerman, 2004). Moreover, it has also been

found that EA makes it possible for organisations to

coordinate the various organisational initiatives that

are aimed at eliminating the existence of information

islands as well as those initiatives that align the

business and IT domains (Tamm et al., 2015,

Bricknall et al., 2006).

Adoption and acceptance are terms that are

regularly used interchangeably in both literature and

industry to articulate the decision to use or to

introduce and use new technologies or organisational

strategies (Gilliland et al., 2015). However, there is

an important difference between adopting and

accepting new technology or strategy (e.g. EA) by the

organisation (Gilliland et al., 2015). Adoption implies

that members of the organisation have decided to use

the organisation’s new technologies or strategy. This

decision is then followed up with the necessary

planning, acquisition and implementation of such

strategy or technology within the organisation (Smuts

and Juleka, 2018, Gilliland et al., 2015). Meanwhile,

acceptance is specifically about the acceptability of

the organisation’s strategy or technology to the

organisation’s people (Gilliland et al., 2015). For the

purposes of this study, we considered adoption in the

context of EA.

The challenge with EA adoption is that changes

to the organisational culture are inevitable (McNabb

and Barnowe, 2009). For EA adoption to be

successful, it is important that members of the

organisation consider EA adoption to be necessary,

achievable, valuable to the organisation, beneficial to

the individual, and supported by top-management

(Syynimaa, 2015). In this context, at the initiation of

EA adoption, the knowledge and understanding of

EA may be considered as low, emphasising the

requirement for effective communication during EA

adoption (Syynimaa, 2015, Lemmetti and Pekkola,

2012).

KEOD 2019 - 11th International Conference on Knowledge Engineering and Ontology Development

The challenges faced by EA are the hurdles that

have to be overcome by an organisation in the pursuit

of attaining long-term success during the

implementation of enterprise initiatives (Wißotzki et

al., 2013). Some of the difficulties encountered

during EA implementation are (Löhe and Legner,

2014, Bente et al., 2012, Wißotzki et al., 2013):

 The initial gathering of information requires a

great effort

 Outdated EA artefacts as well as low quality of

EA artefacts.

 Existing EA artefacts are not regularly used in

day-to-day work as well as in decision-making.

 Lack of EA acceptance in the IT organisation

and difficulties in enforcing EA policies and

standards.

 Lack of coordination between the EA life cycle

processes and the existing established IT

processes.

 Having emerged from Information Systems

Engineering which is a technical domain, there

is insufficient general business awareness of

 Delivering tangible EA value proposition

remains one of the major challenges for

organisations.

 Organisations often lack the ability to articulate

their information needs, thereby hindering

efforts aimed at designing fit-for-purpose

solutions.

 For every enterprise a specific EA has to be

developed based on the practice of that

organisation.

 EA tasks often entails complex approaches

which are typically difficult to teach and

sometimes even harder to depict graphically.

 Lack of common language / glossary inside IT

and between Business and IT to achieve

consensus on a common terminology (common

understanding) to be used within the

organisation.

 Issues pertaining to data quality and data

consolidation are some of the biggest obstacles

due to continuous changes to the business

requirements.

 The increase in compliance requirements as

well as the promulgation of new regulations are

challenging particularly for organisations

operating in the banking, telecommunication,

insurance, and utilities sectors.

 Unlike in other industries like automotive, IT

systems are regularly enhanced and altered

while in use. This makes EA an open-ended

initiative whose end product has to keep

changing. As a result, the effectiveness and

tangible benefits of EA are not readily

identified.

Notwithstanding the great benefits of EA as

discussed in this paper, its adoption is fraught with

challenges which must be overcome for EA adoption

to succeed.

In the next section we present an overview of the

KM lifecycle and interventions.

2.2 KM Lifecycle and Interventions

There are numerous definitions of the knowledge life

cycle, also referred to as KM phases or activities. The

KM lifecycle comprises of four phases (Lech, 2014:

554):

 acquisition / creation / generation,

 retention / storage / capture,

 share / transfer / disseminate, and

 application / utilization / use.

In this context, knowledge sharing is regarded as

the transfer of knowledge among individuals

(members of an organisation), groups (e.g. teams),

departments and organisations leading to an

improvement in the performance of the organisation

as a whole (Zhang and Jiang, 2015, Oztekin et al.,

2015). In order to facilitate knowledge sharing, there

are three main approaches to knowledge sharing /

transfer (Alshurah et al., 2018). The first approach

places greater emphasis on the importance of

technology in the dissemination of knowledge.

Technological advances make knowledge sharing

between individuals, teams, departments and

organisations effective and geographically feasible

(Oztekin et al., 2015, Alshurah et al., 2018). The

second approach emphasises the importance of social

interactions and cultural aspects within the

organisation. Various scholars have emphasised that

formal or informal social processes and cultural

issues are as important as the technology systems in

knowledge sharing and transfer (Peng et al., 2019).

Scholars caution that technological systems are not a

guarantee that people will share knowledge in the

organisation. It is the quality and frequency of social

interactions and the structure of organisational culture

that encourage people to share knowledge (Peng et

al., 2019, Nonaka and Takeuchi, 1995). The third

approach combines the technology and socio-cultural

aspects of KM (Oztekin et al., 2015, Peng et al.,

2019).

Organisations should therefore seek to adopt an

approach to knowledge sharing that codifies explicit

knowledge (knowledge that has been articulated e.g.

text, diagrams or product specifications) in tangible

The Application of Knowledge Management to Overcome Barriers to Enterprise Architecture Adoption: A South African Motor Vehicle

and Asset Finance Case Study

forms, while tacit knowledge (personal and context-

specific) is shared through (1) strengthening weak

ties, and (2) creation of an environment where sharing

tacit knowledge results in personal benefit (Smuts and

Juleka, 2018, Joe et al., 2013, Nonaka and Takeuchi,

1995). This combination of approaches will ensure

that the project team deploying EA can access both

explicit and tacit knowledge that is necessary to

ensure that the resulting EA products are appropriate

and sufficiently cater for the different stakeholders’

needs.

Furthermore, some of the significant knowledge

transfers pertinent during EA projects are (Smuts and

Juleka, 2018, Joe et al., 2013):

 Transferring of organisational knowledge from

members of the organisation such as key users

to the entire project team (which includes

external consultants).

 Transfer of project knowledge between the

project manager, the project team and key

users. Typically, the project team provides

feedback on their progress, as well as any

potential risks and changes in the scope of the

project.

 Project managers share with the team the plans

to manage the proposed changes as well as

plans to mitigate the project risks. The project

managers also provide the team (and other

stakeholders) with updates regarding the status

of the overall project.

 Transfer of solution knowledge from EA

experts to all the team members.

These are aligned with Lech’s (2014)

observations with regards to knowledge transfers

during Enterprise Resource Planning (ERP)

implementations.

In the next section, we consider EA in the context

of KM.

2.3 EA and KM in Context

According to Gøtze (2013: 321), “much of what

enterprise architects do is transmit, translate and

transform knowledge across boundaries, whether the

boundary is between customer and vendor, between

business silos, or between classic business and IT”. In

a similar manner, the importance of the knowledge

created and shared during EA planning and

development is key as such knowledge is an

important organisational resource which must be

properly managed (McGinnis and Huang, 2007).

Similarly, the introduction of change to the

organisation requires an analysis of possible impacts

triggered by this change and EA can support this

analysis as EA provides a comprehensive view of the

entire organisation (Azevedo et al., 2015).

In an instance where EA is adopted and accepted,

an organisation has the advantage to also obtain

knowledge about how employees involved in EA

operate (Gilliland et al., 2015). Capturing and

retaining such useful “human” knowledge may result

in reusable information that will enable EA to

facilitate effective flow of information thus

promoting KM within an organisation (Gilliland et

al., 2015).

It is therefore appropriate for the relation between

EA and KM to be studied and made explicit. With

such a relation, whenever there are KM-induced

organisational capabilities such as changes to

organisational processes or changes to the IT

landscape as a result of KM, the EA would

accordingly require updating to continue supporting

the provision of the organisation’s products and

services (Smuts and Juleka, 2018).

In order to consider this relation between EA and

KM, we present the research approach followed for

this study in the next section.

3 RESEARCH APPROACH

The overall objective with this paper was to

investigate the application of KM and EA for the

success of EA in the context of a bank operating in

the South African (SA) motor vehicle and asset

finance industry. This industry in SA is highly

competitive and is dominated by four leading finance

houses that hold 92% of the market share

(Competition Tribunal of South Africa, 2013).

The outcome of this study provided insight into

the extent to which EA stakeholders believe that EA

efforts might benefit from the introduction of KM

activities. In addition, it considered the barriers

encountered during EA adoption. In order to achieve

this outcome, we conducted quantitative research and

employed a survey research strategy with the

selection of a large sample of participants from a

predetermined population of interest (Leedy and

Ormrod, 2014). We utilised an online questionnaire

for data collection as it enabled us to obtain the same

kind of data from a large group of people, in a

standardised format (De Villiers, 2012). Non-

probability purposive sampling was used to identify

potential research participants and specific criteria

guided their identification i.e. experts in the fields of

EA, project management, business analysis, systems

analysis, IT management, software development,

KEOD 2019 - 11th International Conference on Knowledge Engineering and Ontology Development

software testing and IT infrastructure across all levels

in the case study organisation.

The design of the online questionnaire consisted

of main topic areas: understanding the barriers and

facilitators to EA adoption, and recognising the

application of KM practices to overcome barriers to

EA adoption.

Table 1: Research participant profile.

Response

Count

Response

Percent

I did not know Enterprise

Architecture exists

5 6.0%

I know what EA does, but do

not believe it is important

19 22.6%

I know what EA does and I

believe it is important

60 71.4%

Research participants provided their opinion by

using a 5-point Likert scale. A web link to the online

questionnaire was emailed to 110 prospective

participants of which 84 participated in the research

yielding a response rate of 76%. The EA engagement

of respondents are shown in Table 1.

Most of the respondents, 94% indicated that they

were aware of EA, while 71% believed having an EA

capability was important. Although 22% of

respondents were aware of EA, they indicated that

they did not believe it added value.

In the next section, we discuss the quantitative

analysis of the data collected in order to understand

the barriers to EA adoption, as well as the KM

interventions to increase the possibility of success for

EA initiatives.

4 DATA ANALYSIS AND

FINDINGS

Inferential statistical analysis was performed to

determine whether a relationship exists between the

respondents that perceived EA to be yielding the

expected benefits and the other nine factors pertinent

presented in the questionnaire. Simple descriptive

statistics, as well as frequency tables were analysed in

order to derive suggestions for intervention. Pearson

chi-square tests were used to examine the

associations/ relationships between the responses to

the statement “EA is yielding the expected benefits”

and responses to the other statements posed in the

questionnaire. The detailed outcome is depicted in

Table 2, and in the next sections, a detailed analysis

of each of the factors are presented. In Figure 1, the

frequencies are presented. The number of Likert scale

options were combined: “Agree” and “Strongly

Agree” to “Agree”, and “Disagree” and “Strongly

Disagree” were combined to “Disagree”.

4.1 The Organisation Has an

Open-minded Approach to New

Ways of Working and the Changes

Necessitated by EA

Of the 79 respondents that perceived the organisation

to have an open-minded approach to new ways of

working, the majority (46) perceive EA to be yielding

the expected benefits. Since the chi-square value is

equal to 6.436 with 1 degree of freedom and a p-value

= 0.011, there is a significant association between the

stated perceptions at the 5% level of significance. In

this case, there is strong evidence of a significant

relationship between those who perceived EA to be

yielding the expected benefits and those who perceive

the organisation to have an open-minded approach to

new ways of working and the changes necessitated by

EA.

4.2 The Purpose and Goals of EA Are

Well Understood in the

Organisation

Of the 56 respondents that perceived that the purpose

and goals of EA are well understood in the organi-

sation, 29 perceived EA to be yielding the expected

benefits while almost the same number (27) did not

agree that EA initiatives are yielding the expected

benefits. Since the chi-square value is equal to 0.601

with 1 degree of freedom and a p-value = 0.438

A total of 17 respondents reported that EA

activities are yielding the expected benefits while

reporting that the purpose and goals of EA are not

well understood in the organisation. Therefore there

is an alignment between the expected and recorded

number of respondents that perceived that EA

activities are yielding expected results while

perceiving that the purpose and goals of EA are not

well understood in the organisation.

Moreover, 11 respondents reported that EA

activities are not yielding expected results and that the

purpose and goals of EA are not well understood in

the organisation. This further highlights the minimal

difference between the respondents’ expected and

reported views with regards to the two perceptions.

While there is no statistically significant

association between the perceptions analysed, it is a

meaningful metric to note that 63% of the

respondents perceived that EA is yielding the

The Application of Knowledge Management to Overcome Barriers to Enterprise Architecture Adoption: A South African Motor Vehicle

and Asset Finance Case Study

Figure 1: Frequency count for factors affecting EA adoption.

expected benefits while also perceiving that the

purpose and goals of EA are well understood in the

organisation. The remaining 37% perceived EA to be

yielding the expected benefits while perceiving that

the purpose and goals of EA are not well understood

in the organisation. This suggests that the possibility

of success for EA initiatives (i.e. EA is perceived as

yielding the expected benefits) is increased when the

purpose and goals of EA are well understood in the

organisation.

4.3 EA Has Been Fully Accepted in the

Organisation

Of the 67 respondents that perceived EA to have been

fully accepted in the organisation, the majority (46)

perceived EA to be yielding the expected benefits.

The chi-square value is equal to 25.800 with 1

degree of freedom and a p-value < 0.001. We can

therefore infer that there is a significant association

between the stated perceptions at the 5% level of

significance. In this case, since the p-value <0.001,

there is convincing evidence of a significant

relationship between those who perceived EA to be

yielding the expected benefits and those who

perceived EA to have been fully accepted in the

organisation.

Since all respondents that perceived EA activities

to be yielding expected results also perceived that EA

has been fully accepted in the organisation, the

organisation should therefore direct their efforts to

ensuring that EA is accepted in the organisation.

4.4 EA Adoption Has Been Supported

by a Transformation and Culture

Change Program

Of the 56 respondents that perceived EA adoption to

have been supported by a transformation and culture

change program, 29 perceived EA to be yielding the

expected benefits while 27 did not perceived EA to be

yielding the expected benefits. The chi-square value

is equal to 0.601 with 1 degree of freedom and a p-

value = 0.438.

Some 17 respondents reported that EA activities

are yielding the expected benefits while reporting that

EA has not been supported by a transformation and

culture change program. There is therefore minimal

difference between the expected (15.3) and recorded

(17) number of respondents that perceived that EA

KEOD 2019 - 11th International Conference on Knowledge Engineering and Ontology Development

activities are yielding expected results but that EA has

not been supported by a transformation and culture

change program.

While there is no statistically significant

association between the perceptions analysed, it is

worth noting that 63% of the respondents perceived

that EA is yielding the expected benefits and EA

adoption has been supported by a transformation and

culture change program. Compared to 37% that

perceived EA to be yielding the expected benefits and

that EA adoption has not been supported by a

transformation and culture change program suggests

that it is beneficial to support EA adoption by a

transformation and culture change program.

4.5 Requirements from EA Users /

Stakeholders Are Understood and

Reflected in the EA Artefacts

Of the 62 respondents that perceived that the

requirements from EA users/ stakeholders are

understood and reflected in the EA artefacts, 35

perceived EA to be yielding the expected benefits

while 27 did not perceived EA to be yielding the

expected benefits. The chi-square value is equal to

0.273 with 1 degree of freedom and a p-value = 0.601.

We can therefore not conclude that there is an

association between the stated perceptions.

Table 2: Cross Tabulation Results between the KM attributes and the perception that EA activities are yielding the expected

benefits.

Cross tabulation Results –

KM attribute compare to “EA

activities are yielding the expected

benefits”

KM attributes

Pearson Chi-

Square

Continuity

Correction

P-value

Note: N=84

Pearson Chi-Square NOTE

My organisation has an open-

minded approach to new ways of

working and the changes

necessitated by EA

6,436 4,3

0.011

[N/A]

2 cells (50.0%) have expected count less

than 5. The minimum expected count is

2.26.

The purpose and goals of EA are

well understood in the organisation

0,601 0,294 0.438

0 cells (0.0%) have expected count less

than 5. The minimum expected count is

12.67.

EA has been fully accepted in the

organisation

25,80 23,10 <0.001

0 cells (0.0%) have expected count less

than 5. The minimum expected count is

7.69.

EA adoption has been supported

by a transformation and culture

change program

0,60 0,29 0.4388

0 cells (0.0%) have expected count less

than 5. The minimum expected count is

12.67.

Requirements from EA users/

stakeholders (such as yourself) are

understood and reflected in the EA

artefacts

0,27 0,08 0.601

0 cells (0.0%) have expected count less

than 5. The minimum expected count is

9.95.

The organisation has the practical

skills required in EA development.

2,16 1,43 0.142

0 cells (0.0%) have expected count less

than 5. The minimum expected count is

7.69.

EA has the necessary management

support. 15,32 12,88 <0.001

1 cells (25.0%) have expected count less

than 5. The minimum expected count is

4.98.

Employees actively participate in

the development of EA.

49,57 46,48 <0.001

0 cells (0.0%) have expected count less

than 5. The minimum expected count is

15.83.

There is a good level of knowledge

sharing between employees and

the EA team

17,61 15,39 <0.001

0 cells (0.0%) have expected count less

than 5. The minimum expected count is

7.69.

The Application of Knowledge Management to Overcome Barriers to Enterprise Architecture Adoption: A South African Motor Vehicle

and Asset Finance Case Study

Some 11 respondents reported that EA activities

are yielding the expected benefits while reporting that

requirements from EA users / stakeholders are not

understood or reflected in the EA artefacts. Moreover,

11 respondents reported that EA activities are not

yielding expected results and that the requirements

from EA users / stakeholders are not understood or

reflected in the EA artefacts.

While there is no statistically significant

association between the perceptions analysed, 76.1%

perceived that EA is yielding the expected benefits

and that requirements from EA users / stakeholders

are understood and reflected in the EA artefacts. Only

23.9% perceived EA to be yielding the expected

benefits and that requirements from EA users /

stakeholders are not understood or reflected in the EA

artefacts. This suggests that it is beneficial to ensure

that requirements from EA users / stakeholders are

understood and reflected in the EA artefacts.

4.6 The Organisation Has the Practical

Skills Required in EA Development

Of the 67 respondents that perceived that the

organisation has the practical skills required in EA

development, 34 perceived EA to be yielding the

expected benefits while 33 did not perceived EA to be

yielding the expected benefits. The chi-square value

is equal to 2.155 with 1 degree of freedom and a p-

value = 0.142. We cannot then conclude that there is

an association between the stated perceptions. Twelve

respondents reported that EA activities are yielding

the expected benefits while reporting that the

organisation does not have the practical skills

required in EA development.

While there is no statistically significant associa-

tion between the perceptions analysed, it is noteworthy

that 73.9% of the respondents perceived that EA is

yielding the expected benefits and that the organisation

has the practical skills required in EA development.

This figure is almost 3 times higher than the 26.1% that

perceived EA to be yielding the expected benefits and

that the organisation does not have the practical skills

required in EA development. This suggests that

ensuring the organisation has the practical skills

required in EA development increases the possibility

of successful EA initiatives by a factor of almost three.

4.7 EA Has the Necessary Management

Support

Out of a total of 73 respondents that perceived EA to

have the necessary management support, the majority

(46) perceive EA to be yielding the expected benefits.

The chi-square value is equal to 15.322 with 1

degree of freedom and a p-value < 0.001. We can

therefore infer that there is a significant association

between the stated perceptions at the 5% level of

significance. In this case, since the p-value <0.001,

there is convincing evidence of a significant

relationship between those who perceive EA to be

yielding the expected benefits and those who perceive

EA to have the necessary management support.

The standardised residuals were used to determine

which cells in the cross tabulation contributed most to

the significant overall association. A standardised

residual value smaller than -2 or greater than 2 is an

indication that the particular cell in the cross

tabulation made a large contribution to the overall

association. In this case the standardised residuals are

2.7 and -2.5.

None of the respondents reported that EA

activities are yielding the expected benefits while

reporting that EA does not have the necessary

management support. In addition, 11 respondents

highlighted that EA activities are not yielding

expected results and that EA does not have the

necessary management support.

Since all respondents that perceived EA activities

to be yielding expected results also perceive that EA

has the necessary management support, securing

management support is therefore essential in

increasing the possibility of success for EA

initiatives.

4.8 Employees Actively Participate in

the Development of EA

Of the 35 respondents that perceive that employees

actively participate in the development of EA, all of

them perceive EA to be yielding the expected

benefits. The chi-square value is equal to 49.565 with

1 degree of freedom and a p-value < 0.001. We can

therefore infer that there is a significant association

between the stated perceptions at the 5% level of

significance. In this case, since the p-value <0.001,

there is convincing evidence of a significant

relationship between those who perceive EA to be

yielding the expected benefits and those who perceive

that employees actively participate in the

development of EA.

The standardised residuals were used to determine

which cells in the cross tabulation contributed most to

the significant overall association. A standardised

residual value smaller than -2 or greater than 2 is an

indication that the particular cell in the cross

tabulation made a large contribution to the overall

KEOD 2019 - 11th International Conference on Knowledge Engineering and Ontology Development

association. In this case the standardised residuals are

3.4 and -3.1.

A total of 11 respondents reported that EA

activities are yielding the expected benefits while

reporting that employees did not actively participate

in the development of EA.

Additionally, 38 respondents indicated that EA

activities are not yielding expected results and that

employees did not actively participate in the

development of EA. Since all respondents that

perceived employees to be actively participating in

EA development also perceived that EA activities to

be yielding expected results, the organisation should

therefore direct its efforts to promote employee

participation in the development of EA.

4.9 There Is a Good Level of

Knowledge Sharing between the

EA Team and Other Members of

the Organisation

Of the 17 respondents that perceived that there is a

good level of knowledge sharing between the EA

team and other members of the organisation, all of

them perceive EA to be yielding the expected

benefits.

The chi-square value is equal to 17.607 with 1

degree of freedom and a p-value < 0.001. We can

therefore infer that there is a significant association

between the stated perceptions at the 5% level of

significance. In this case, since the p-value <0.001,

there is convincing evidence of a significant

relationship between those who perceived EA to be

yielding the expected benefits and those who

perceived that there is a good level of knowledge

sharing between the EA team and other members of

the organisation.

The standardised residuals were used to determine

which cells in the cross tabulation contributed most to

the significant overall association. A standardised

residual value smaller than -2 or greater than 2 is an

indication that the particular cell in the cross

tabulation made a large contribution to the overall

association. In this case the standardised residuals are

2.5 and -2.8.

A total of 29 respondents reported that EA

activities are yielding the expected benefits while

reporting that there is a lack of a good level of

knowledge sharing between the EA team and other

members of the organisation. Additionally, 38

respondents reported that EA activities are not

yielding expected results and that there is a lack of a

good level of knowledge sharing between the EA

team and other members of the organisation.

Since all respondents that perceive that there is a

good level of knowledge sharing between the EA

team and other members of the organisation also

perceived that EA activities to be yielding expected

results, the organisation should therefore direct its

efforts to promote knowledge sharing.

5 KM PRACTICES THAT

OVERCOME BARRIERS TO EA

ADOPTION

In the previous sections we focused on analysing and

presenting the results of the empirical investigation of

this research. The main findings i.e. barriers to EA

adoption was the fact that the purpose and goals of

EA are not well understood in the organisation and

that employees are not actively participating in the

development of EA. The main objective pursued in

this research was to determine how KM can be used

to overcome these barriers towards successfully

adopting EA.

The challenge with EA adoption is that changes to

the organisational culture are inevitable and for EA

adoption to be successful, it is important that

members of the organisation consider EA adoption to

be necessary, achievable, valuable to the

organisation, beneficial to the individual, and

supported by top-management.

In order to establish which KM interventions are

best suited to support and enable EA initiatives,

research participants also rated such initiatives

by indicating “no improvement”, “slight

improvement” and “significant improvement” as

depicted in Table 3.

The empirical evidence reveals that promoting

knowledge sharing between employees and the EA

team would make a significant contribution in

supporting EA initiatives. Empirical evidence also

showed that increasing involvement of EA users/

stakeholders in EA development would also greatly

benefit EA initiatives. Empirical evidence also

showed that regularly communicating EA-related

issues and success stories, continuously

communicating the purpose and goals of EA as well

as increasing the level of user involvement in EA

would be highly effective in overcoming the reported

barriers. Empirical evidence presented revealed that

KM activities such as knowledge sharing are

perceived to hold great potential to support EA

adoption.

The Application of Knowledge Management to Overcome Barriers to Enterprise Architecture Adoption: A South African Motor Vehicle

and Asset Finance Case Study

Table 3: KM interventions to increase the possibility of

success for EA initiatives.

Intervention

Improvement

None Slight

Signi-

ficant

Increase management

involvement in EA.

7% 47% 46%

Continuous

communication pertaining

to the purpose and goals

of enterprise architecture.

8% 22% 70%

Regularly communicating

EA-related issues and

success stories.

26% 22% 52%

Increase involvement of

EA users/ stakeholders in

EA development

27% 0% 73%

Promoting knowledge

sharing between

employees and the EA

team

38% 0% 62%

Some of the significant discoveries made in this

research are that EA is well recognised and perceived

to be important. Some of the barriers to successful EA

adoption are that (i) the purpose and goals of EA are

not well understood in the organisation; and that (ii)

employees are not actively participating in the

development of EA. Some of the KM interventions

that are believed to be effective in overcoming the

barriers are: (i) promoting knowledge sharing

between employees and the EA team; (ii) increase

involvement of EA users / stakeholders in EA

development; and (iii) increased management

involvement in EA.

6 CONCLUSION

IT is a key enabler for business as it facilitates

organisations to exploit new opportunities and

capabilities offered by new technologies in order to

gain competitive advantage in their markets. Since

EA is increasingly being used to align organisational

strategy with IT strategy, its successful adoption is

important to business.

However, EA adoption is fraught with difficulties,

particularly human and organisational culture factors.

KM promises to address and improve these human

and organisational culture issues. Therefore, the

purpose of this study in the South African motor

vehicle and asset finance industry, was to better

understand the barriers to EA adoption, as well as the

KM interventions to increase the possibility of

success for EA initiatives. KM is a field that provides

effective tools and methods to influence human and

organisational culture issues. This research study

indicated that there is a clear possibility in the usage

and application of KM, more specifically knowledge

sharing, in overcoming barriers that prevent the

successful implementation of EA initiatives. Multiple

barriers to successful EA adoption were highlighted

and the lack of understanding of the purpose and

goals of EA in the organisation, as well as employees

that are not actively participating in the development

of EA, were identified as major barriers in the case

study organisation. Some of the KM interventions

that were identified to be effective in overcoming the

barriers pointed to the promotion of knowledge

sharing between employees and the EA team, the

increased involvement of EA stakeholders in EA

development, and the increased management

involvement in EA.

Conducting this study has contributed in two

ways. This study has added to the existing EA and

KM body of knowledge, thus contributing to the

academic literature on both EA and KM as well as the

relation between the two. This study has also offered

practical steps of incorporating KM activities during

EA adoption thus contributing to the domain of EA

practitioners.

REFERENCES

Aier, S. 2014. The Role of Organizational Culture for

Grounding, Management, Guidance and Effectiveness

of Enterprise Architecture Principles. Information

Systems And E-Business Management, 12, 43–70.

Aier, S. & Weiss, S. An Institutional Framework for

Analyzing Organizational Responses to the

Establishment of Architectural Transformation.

Proceedings of the 20th European Conference on

Information Systems, 2012 Barcelona.

Alshurah, M. S., Zabadi, A. M., Dammas, A. H. &

Dammas, D. H. 2018. Impact of Organizational context

& Information Technology on Employee Knowledge

Sharing. International Journal of Business and

Management, 13.

Azevedo, C., Van Sinderen, M., Pires, L. & Almeida, J.

2015. Aligning enterprise architecture with strategic

planning. International Conference on Advanced

Information Systems Engineering, 426-437.

Bente, S., Bombosch, U. & Langate, S. 2012. Collaborative

Enterprise Architecture - Enriching EA with Lean,

Agile, and Enterprise 2.0 practices, Waltham, MA,

Morgan Kaufmann.

KEOD 2019 - 11th International Conference on Knowledge Engineering and Ontology Development

Bricknall, R., Darrell, G., Nilsson, H. & Pessi, K. Enterprise

architecture: critical factors affecting modelling and

management. European Conference on Information

Systems, 2006 Göteborg, Sweden.

Buckl, S. & Schweda, C. Future Research Topics in

Enterprise Architecture Management - A Knowledge

Management Perspective. Workshop Trends in

Enterprise Architecture Research, 2009 Stockholm.

Competition Tribunal Of South Africa. 2013. Volkswagen

Financial Services South Africa (Pty) Ltd and

Volkswagen Financial Services South Africa, a division

of Wesbank, a division of FirstRand Bank [Online].

Available: https://www.comptrib.co.za/ [Accessed

April 2019].

Corfield, A. & Paton, R. 2016. Investigating Knowledge

Management: Can KM Really Change Organisational

Culture? Journal of Knowledge Management, 20, 88-

103.

De Villiers, M. R. 2012. Models for interpretive

information systems research, part 2: Design research,

development research, design-science research, and

design-based research - a meta-study and examples.

Research Methodologies, Innovations and

Philosophies in Software Systems Engineering and

Information Systems, 238-255.

Gilliland, S., Kotzé, P. & Van Der Merwe, A. Work Level

Related Human Factors for Enterprise Architecture as

Organisational Strategy. Third International

Conference on Enterprise Systems, 2015. IEEE

Computer Society, 43-54.

Gøtze, J. The Changing Role of the Enterprise Architect.

17th IEEE International Enterprise Distributed Object

Computing Conference Workshops, 2013 Vancouver,

British Columbia, Canada.

Hausman, K. 2011. Sustainable enterprise architecture,

Boca Raton, Fla, CRC Press.

Jahani, B., Javadein, S. & Jafari, H. 2010. Measurement of

enterprise architecture readiness within organizations.

Business Strategy Series, 11, 177-191.

Joe, C., Yoong, P. & Patel, K. 2013. Knowledge loss when

older experts leave knowledge-intensive organisations.

Journal of Knowledge Management, 17, 913-927.

Lech, P. 2014. Managing knowledge in IT projects: a

framework for enterprise system implementation.

Journal of Knowledge Management, 18, 551-573.

Leedy, P. D. & Ormrod, J. E. 2014. Practical Research:

Planning and Design, New Jersey, Pearson Education

Limited.

Lemmetti, J. & Pekkola, S. 2012. Understanding Enterprise

Architecture: Perceptions by the Finnish Public Sector.

In: SCHOLL, H. J., JANSSEN, M., WIMMER, M.,

MOE, C. & FLAK, L. (eds.) Electronic Government:

Proceedings of the 11th IFIP WG 8.5 International

Conference. Kristiansand, Norway.

Liao, S. 2003. Knowledge management technologies and

applications—literature review from 1995 to 2002.

Expert Systems with Applications, 25, 155-164.

Löhe, J. & Legner, C. 2014. Overcoming implementation

challenges in enterprise architecture management: a

design theory for architecture-driven IT Management

(ADRIMA). Information Systems and E-Business

Management, 12, 101-137.

Mcginnis, T. & Huang, Z. 2007. Rethinking ERP success:

A new perspective from knowledge management and

continuous improvement. Information & Management

Journal, 44, 626-634.

Mcnabb, D. & Barnowe, J. 2009. Trends Shaping Public

Sector Transformation: Knowledge Management, E-

Government and Enterprise Architecture. Journal of

Information & Knowledge Management, 8, 25-34.

Nonaka, I. & Takeuchi, H. 1995. The Knowledge Creating

Company, Oxford University Press.

Oztekin, A., Delen, D., Zaim, H., Turkyilmaz, A. & Zaim,

S. 2015. The Infuence of Knowledge Management on

Financial and Non-Financial Performance. Journal of

Information & Knowledge Management, 14, 1-14.

Peng, J., Quan, J., Zhang, G. & Dubinsky, A. J. 2019.

Knowledge Sharing From Employee's Perspective:

Social Relationship, Contextual Performance, and IT

Competence. In: HABIB, M. (ed.) Handbook of

Research on the Evolution of IT and the Rise of E-

Society. IGI Global.

Pham, T., Pham, D. & Pham, A. 2013. From business

strategy to information technology roadmap: A

practical guide for executives and board members,

Boca Raton, FL, CRC Press.

Ross, J., Weill, P. & Robertson, D. 2006. Enterprise

architecture as strategy: creating a foundation for

business execution, Boston, Massachusetts, Harvard

Business School.

Rusly, F., Sun, P. & Corner, J. 2015. Change readiness:

creating understanding and capability for the

knowledge acquisition process. Journal of Knowledge

Management, 19, 1204-1223.

Schekkerman, J. 2004. How to survive in the jungle of

enterprise architecture frameworks: Creating or

choosing an enterprise architecture framework,

Victoria, B.C, Trafford.

Smuts, H. & Juleka, B. 2018. Enabling Business

transformation through enterprise architecture and the

knowing cycle. International Journal of Engineering &

Technology, 7, 95-102.

Syynimaa, N. 2015. Enterprise Architecture Adoption

Method for Higher Education Institutions. Doctor of

Philosophy Dissertation, Henley Business School

University of Reading.

Tamm, T., Seddon, P., Shanks, G., Reynolds, P. &

Frampton, K. M. 2015. How an Australian Retailer

Enabled Business Transformation Through Enterprise

Architecture. MIS Quarterly Executive, 14, 181-193.

Trinskjær, J. 2009. Combining Enterprise Architecture and

ERP Systems. Doctoral dissertation, IT University of

Copenhagen.

Wang, M. & Yang, T. 2016. Investigating the success of

knowledge management: An empirical study of small

and medium-sized enterprises. Asia Pacific

Management Review, 12, 79-91.

Wißotzki, M., KOÇ, H., WEICHERT, T. & SANDKUHL,

K. 2013. Development of an enterprise architecture

management capability catalog. International

The Application of Knowledge Management to Overcome Barriers to Enterprise Architecture Adoption: A South African Motor Vehicle

and Asset Finance Case Study

Conference on Business Informatics Research. Berlin

Heidelberg: Springer.

Zhang, X. & Jiang, J. 2015. With whom shall I share my

knowledge? A recipient perspective of knowledge

sharing. Journal of Knowledge Management, 19, 277-

295.

KEOD 2019 - 11th International Conference on Knowledge Engineering and Ontology Development