Influence Factors for Knowledge Management Initiatives

A Systematic Mapping Study

Jacilane Rabelo and Tayana Conte

USES Research Group, Instituto de Computação, Universidade Federal do Amazonas, Manaus, Brazil

Keywords: Knowledge Management, Influence Factors, Software Engineering, Systematic Mapping Study.

Abstract: Context: Knowledge Management (KM) is becoming critical in software organizations due to the increasing

demands of the market. Despite the importance of KM, there is no consensus on which factors can influence

KM initiatives in software organizations. Aim: The goal of this paper is to investigate what are the factors

that influence KM in software organizations. Method: we performed a systematic mapping study on

influencing factors for knowledge management in software organizations. Results: From a set of 1028

publications, 147 publications were analyzed and 10 were selected in this mapping, which helped us identify

the influence factors that were most cited by the authors. Among the selected publications, the following

factors were the most cited: Organizational Culture, Leadership, Information Technology and Social

Network of Knowledge. Conclusion: There is a shortage of papers that address this issue of influencing

factors for software organizations, and how to assess these factors in software organizations. Most studies

show statistical data on the relationship between KM and the factors, but do not show how these factors can

be evaluated in the organization. These aspects need to be addressed in the influence factors in order to

improve knowledge management initiatives in software organizations.

1 INTRODUCTION

Knowledge is essential for knowledge-intensive

companies such as software-development companies

(Menolli et al., 2015). Silva-Filho et al. (2016) state

that knowledge is considered the main asset in

Software Companies. Knowledge in software

development projects is varied and grows in

proportions (Carreteiro et al., 2016).

A successful Knowledge Management (KM)

became a determining factor affecting the efficiency

and performance of an organization (Sharma et al.,

2012). KM in software organizations is seen as an

opportunity to create a common language among

software developers so that they can interact,

negotiate and share knowledge and experiences

(Aurum et al., 2013).

According to Moffett et al. (2002), many

organizations are attempting to begin working with

KM and they are unsure of which approach to

implement. Mehta et al. (2014) argue that the main

factors contributing to effective knowledge

management are human and technical. Human

behavior is the key to the success or failure of KM

activities, since KM involves an emphasis on

organizational culture, teamwork, promotion of

learning as well as sharing of skills and experiences

(Bollinger and Smith, 2001). Several papers in the

literature have reported which facilitators influence

KM implementations (Wang and Wang, 2016;

Mehta et al., 2014; AL-Hakim et al., 2012; Allameh

et al., 2011). In addition, related researches are

focusing on how these factors can contribute to the

successful implementation of KM, and which can

lead to increased innovation and organizational

performance improvement (AL-Hakim et al., 2012).

Although several papers investigate the

relationship between influence factors and

knowledge management, there is still a shortage of

papers that show how these factors influence and

can be exploited to support knowledge management

initiatives in software organizations. Therefore, we

have identified the need for a comprehensive

research on factors influencing KM in software

organizations.

Systematic mapping studies are carried out to

give an overview of a research area through the

classification of published contributions given an

object of study (Oliveira et al., 2017). Our goal in

this study, is to perform a Systematic Mapping on

Rabelo, J. and Conte, T.

Inﬂuence Factors for Knowledge Management Initiatives.

DOI: 10.5220/0006664700170028

In Proceedings of the 20th International Conference on Enter prise Information Systems (ICEIS 2018), pages 17-28

ISBN: 978-989-758-298-1

research related to the factors that influence the

initiatives of knowledge management. In addition,

our intention is to identify ways to assess these

factors in software organizations.

From an initial selection of 147 publications, we

identified 22 different influencing factors. From the

selected publications, the following factors were the

most cited: Organizational Culture, Leadership,

Information Technology and Knowledge Social

Network. With this work, we present conclusions

about the state of the art in this area of research and

contribute to the improvement of the process of

knowledge management in software organizations.

Besides this introductory section, the paper is

organized in four more sections. Section 2 presents

the background for this research. The research

method and its details are shown in Section 3.

Section 4 presents the results obtained in this

research. Section 5 presents the discussions for this

research. Finally, Section 6 presents our conclusions

and future work.

2 RELATED WORK

This section presents literature reviews that have

been conducted on knowledge management or

influence factors for knowledge management.

The main asset from a software organization is

the knowledge held by its employees and the

organization's development culture (Bari and

Ahamad, 2011). Knowledge in software

development projects is varied and grows in

proportions (Carreteiro et al., 2016).

According to Nonaka and Takeuchi (1995), there

are two types of knowledge that need to be

managed: tacit and explicit. Tacit knowledge is

based on the person's experience, which due to its

subjectivity is difficult to express with words,

numbers and sentences (Nonaka and Takeuchi,

1995). Tacit knowledge is usually shared directly, by

face-to-face contact, and is considered the most

valuable type of knowledge (Ruhe, 2001). Explicit

or codified knowledge is considered transmissible in

formal and systematic language. Nonaka and Teece

(2001) also state that, because this type of

knowledge is objective, it can be represented in

several ways (e.g. such as documents, reports and

databases) and can be processed, transmitted and

stored easily.

Due to the importance of KM, literature reviews

on knowledge management and on the influence

factors for knowledge management were carried out

(Menolli et al., 2013; AL-Hakim and Hassan, 2012;

Bjørnson and Dingsøyr, 2008).

Menolli et al. (2013) conducted a literature

review aimed at understanding in which areas of

software engineering the studies related to

knowledge management are focusing, and how the

concepts of knowledge management are being

applied in software engineering work. The authors

show that the publications focus on the software

processes. In addition, the concepts of lessons

learned and experience factory are widely used in

the work of knowledge management in the area of

software engineering.

Bjørnson and Dingsøyr (2008) present a

Systematic Literature Review (SLR) that aimed to

identify the empirical studies of knowledge

management in software engineering. The authors

present the main concepts and research methods

being used, and point out possible gaps of research

in the field that need further investigation. The

authors concluded that: (a) software engineering has

predominantly addressed the storage and retrieval of

knowledge, ignoring other important aspects, such

as the creation, transfer and application of this

knowledge; (b) software development with agility

has focused mainly on tacit knowledge-driven

management activities, while traditional software

development has focused primarily on explicit

knowledge-driven management activities; and, (c)

there is a lack of understanding about the

identification and detailing of influence factors for

knowledge management in software engineering.

AL-Hakim and Hassan (2012) conducted a

literature review to examine the relationship

between the critical success factors of knowledge

management, innovation and organizational

performance, particularly in the Iraqi mobile

telecommunications industry. The goal of their study

was to address the influence factors for KM, to

increase innovation and to improve organizational

performance. According to AL-Hakim and Hassan

(2012), most of the influence factors explored by the

reported works mention: (i) human resource

management, (ii) information technology, (iii)

leadership, (iv) organizational learning, (iv)

organizational strategy, (iv) organizational structure,

and, (v) organizational culture.

We have analyzed the three previously

mentioned literature reviews and identified that:

 The review by Menolli et al. (2013) and

Bjørnson and Dingsøyr (2008) - investigated

knowledge management for software

engineering, but did not verify the influence

factors;

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 The review by AL-Hakim and Hassan (2012)

verified the influence factors of knowledge

management, but did not analyzed them in the

context of software engineering.

Therefore, we identified a need to associate a

research that investigates the influence factors of

KM in the context of software engineering.

In order to identify the factors that influence the

initiatives of Knowledge Management and ways of

evaluating these factors, we carried out a research in

the literature. The next section shows the details of

this literature mapping.

3 RESEARCH METHOD

Systematic Literature Mappings (SLM) are based on

a well-defined research strategy that seeks to detect

as much relevant publications of a research topic as

possible (Kitchenham and Chartes, 2007). The

following subsections detail the activities concerning

the planning and conducting stages of this

systematic mapping defined in our review protocol.

3.1 Research Question

Our main research question was: "What are the

factors that influence knowledge management

initiatives in software development companies?".

The main goal of this systematic mapping was to

identify the factors that can influence knowledge

management initiatives in Software Engineering and

related areas.

3.2 Search Strategy

The search terms were defined based on the

procedures described by Kitchenham and Charters

(2007), who suggested defining the parameters for

PICOC (Population, Intervention, Comparison,

Outcome and Context). The defined population is

the organizations that develop software. The

Intervention was composed of the influence factors

of knowledge management. The Comparison was

not applicable, since our goal was to characterize

these influences factors. The Outcome were the

influence factors and ways of evaluating these

factors in software development organizations.

Finally, the Context was not applicable, since there

is no comparison to determine the context.

The search strategy should have the keyword

sequences (terms) for carrying out the search (search

strings). The choice of terms related to Software

Engineering and Knowledge Management were

based on the systematic review described in the

paper by Menolli et al. (2013). The keyword

sequences for the search were generated from the

combination of terms. The formation of the search

string respected the peculiarities of the search

engine.

The search string is presented in Figure 1 and

was used in the Scopus

, Engineering Village

and

IEEEXplore3 digital libraries. Scopus and

Engineering Village are meta-libraries that index

publications from several well-known publishers

such as ACM, IEEE, Springer and Elsevier, besides

allowing defining filters such as document type,

language and knowledge area. IEEEXplore indexes

various Software Engineering publication venues.

Also, they allow the establishment of filters for

selecting the document type and area of knowledge

which were defined in our search strategy

(http://ieeexplore.ieee.org/Xplore/home.jsp).

(("software engineering" OR "software

process" OR "software learning software

organization")

AND

("learning organization" OR

"organizational learning" OR "knowledge

management")

AND

("influence factor" OR "critical factor" OR

"critical success factor" OR "key factor"

OR "knowledge management factor"))

AND

(LIMIT-TO(SUBJAREA, "COMP"))

Figure 1: Search string used in the systematic mapping.

3.3 Publications Selection Process

The selection of the papers was carried out in two

stages, in order to ensure the inclusion of

publications that are relevant to the research. Not all

publications returned with the use of the search

string are useful in the context of the search, since

search engines are restricted to the syntactic aspect:

 1 St. Step – Selection of relevant publications

(1st filter): in the 1st filter the title and abstract

of the returned publications are read, applying

the inclusion and exclusion criteria (see Table

1). This step was reviewed by another expert.

In case of disagreement on any publication,

this was included;

http://www.scopus.com

http://www.engineeringvillage.com

http://ieeexplore.ieee.org/Xplore/home.jsp

Inﬂuence Factors for Knowledge Management Initiatives

 2nd. Step – selection of the relevant

publications (2nd Filter): all papers included

as results of the previous stage (1st filter) were

reviewed entirely by at least one of the

researchers. This review concluded the

selection of papers to be included in the data

extraction process.

Table 1: Inclusion and exclusion set of criteria.

Inclusion Criteria

Inc1

The publication proposes/describes the factor(s)

that influence KM initiatives

Exclusion Criteria

Exc1

The publication does not meet the inclusion

criterion

Exc2

The full version of the publication is not be

available for free in the selected sources

Exc3

Paper in languages other than Portuguese or

English will be discarded

3.4 Data Extraction Strategy

The extraction process aims to extract relevant data

from the selected publications. Table 2 shows the

information that was extracted from each of the

selected publications.

4 RESULTS

The systematic mapping involved two researchers,

in order to avoid the bias of a single researcher

carrying out the selection, analysis and extraction of

the retrieved papers. One researcher specified the

review protocol, which was reviewed by the second

researcher.

For the first step, the researchers independently

classified a sample of 86 randomly selected

publications based on the selection criteria. The

agreement between the researchers was evaluated by

the Kappa statistical test (Cohen, 1960). The result

of this evaluation showed almost perfect agreement

between the two researchers (Kappa = 0,805)

according to the range described by Landis and

Koch (1977).

Table 2: Data extraction form for publications.

Item

Description

Publication data

Full publication reference

Publication summary

Short publication description

Context of use

Description of the context in

which the influence factor was

applied

Factor of influence /

Bibliographic references

Name of the influence factor

and data of the complete

bibliographic reference of the

cited factor

Does it show a

questionnaire? If yes,

please describe

Description of the questionnaire

used and references used as a

basis for the research

Type of carried out study

Description of the empirical

study, case study, proof of

concept and others

Type of data analysis

Description whether the data

analysis is qualitative or

quantitative

Procedures for collecting

data

Description of how the data

was collected to analyze the

influence factors of knowledge

management

Data analysis procedures

Description of the data analysis

procedure used in the

publication

Identified Results

Description of the results

presented in the publication

4.1 Identified Publications

We started by finding a total of 712 publications in

the Scopus digital library, 326 publications in the

IEEEXplore digital library and 100 publications in

the Engineering Village library. After removing the

duplicated publications, the number of selected

publications to employ the first step selection criteria

was 1.028. Out of these 1.028 publications, 881

were rejected in the first filter, since they did not

meet the inclusion criteria. The remaining 147

publications were fully read and classified in the

second filter, according to the selection criteria. At

the end of the process, 10 publications were

accepted and extracted. The selected publications

were published between 2008 and January 2017.

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Figure 2 summarizes the complete selection and

data extraction process.

4.2 Results Overview

Table 3 shows the relationship of influence factors

cited by each of the selected publications. Next, we

present a summary of the main contributions related

to the influence factors of each publication presented

in Table 3.

The work of Wang and Wang (2016) presents the

results of a study that aimed to develop and test an

integrative model of implementation and knowledge

management systems (KMS) for companies. One

Survey was applied to 291 companies in Taiwan.

The authors show the applied questionnaire and used

confirmatory factor analysis and the logistic

regression technique to test the relationship of the

hypotheses. The results of paper by Wang and Wang

(2016) show that the technological innovation

factors (perceived benefits, complexity and

compatibility), the organizational factors (support to

top management and organization culture), and

environmental factors (competitive constraints) have

significant influence on the implementation of

knowledge management systems in organizations.

Chang and Lin (2015) carried out a study to

clarify the relationship between five types of

organizational culture (which include results-

oriented, tightly controlled, job-oriented, closed

system and professional-oriented cultures) and four

kinds of individual KM processes (creation, storage,

transfer and application). The authors sought to

answer: “How does the organizational culture

influence the KM process of an individual?”. The

results of the study showed that some organizational

culture dimensions (results-oriented, tightly

controlled and job-oriented) indeed have a

significant effect on the KM process intention of the

individual, whereas a tghtly culture negative effects.

With regards to practitioners, the management can

modify their organizational culture to improve the

performance of KM process.

The work by Chen et al. (2015) sought to analyze

the main factors that influence the sharing of

knowledge in open source software projects. These

authors analyzed data from four real projects (no

evaluation questionnaire was applied) and created a

conceptual framework. The authors concluded,

based on the results of the 4 projects that

participative motivation, social network and the

organizational culture from the developers' side are

important factors influencing knowledge sharing.

From the point of view of users, user innovation was

the most important factor. Participative motivation

includes intrinsic motivation and extrinsic

motivation. Social networks include the cognitive

dimension, relational dimension and structural

dimension. The organizational culture includes

openness, collaborative sharing and the geek spirit.

Rabelo et al. (2015) present the results of a case

study that aimed to compare, in practice, the

relationship of the Knowledge Management cycle

(SECI) with Organizational Culture through the

Competing Values Framework (CVF). The authors

show that the organization's KM practices are more

focused on the internalization stage of the SECI

model, that is, on the transformation of explicit

knowledge into tacit knowledge. Regarding the

cultural type, it was identified that the predominant

type in the organization is Market. This type is

characterized by results oriented and aggressively

focused organizational leadership. The authors

sought to identify the relationship between

knowledge management (SECI) and organizational

culture (CVF) based on a theoretical model of the

literature. However, the authors conclude that they

did not identify this relationship between the models

(SECI and CVF) in the published research.

Yang et al. (2014) empirically investigated a

sample of research and development (R&D)

research projects. The goals were: (a) to evaluate the

associations between application of Information

Figure 2: Publications selection process/results.

Inﬂuence Factors for Knowledge Management Initiatives

Table 3: Influence factors found in systematic mapping.

Cognitive Capital

Complexity

Competitive Pressure

Compatibility

Ethical

Information Technology

Leadership

Learning Practices

Measurement

Motivational Participation

Organizat

ional Culture

Organizational Learning

Organizational Performance

Perceived Benefits

Project Success

Project Uncertainty

Relational Capital

R&D Project performance

Social Network

Team Process

Top Management Support

User Innovation

(Wang and

Wang (2016)

•

(Chang and

Lin, 2015)

•

(Chen et

al.,2015)

•

(Rabelo et al.,

2015)

•

(Yang et al.,

2014)

•

(Akhavan et

al., 2014)

•

(McKay et

al., 2014)

•

(Mehta et al.,

2014)

•

(Anantatmula,

2008)

•

(Aurum et al.,

2008)

•

Total

Instances

Technology (IT), Knowledge Management (KM),

Team Process (TP), and R & D Project Performance;

(b) to determine whether TP may mediate the effect

of KM on R & D project performance; (c) to

examine the moderating role of project

characteristics in the relationship between TP and R

& D project performance. The authors’ analysis

suggests that KM may influence the performance of

R&D projects through TP. Project managers,

particularly for consumer electronics projects,

should employ KM practice and encourage team

members to share their knowledge to enhance team

competency. The results also show that industry and

team size have a moderating effect on the

relationship between TP and R & D project

performance.

Akhavan et al. (2014) investigated the

relationship between the following factors: ethics,

knowledge creation and organizational performance.

The authors assessed the factors by applying a

questionnaire in an organization. The research

results showed that there is a strong and positive

correlation between ethics and organizational

performance. The relationship between ethics and

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the knowledge creation processes is also positive

and significant, but no significant relationship was

observed between processes of knowledge creation

and organizational performance.

McKay et al. (2014) presented the results of a

research that explored the flow of knowledge

transfer within an organization: (1) identifying

factors at the organizational level that influence

knowledge transfer; (2) identifying the factors at the

unit or project team level that influence the transfer

of knowledge; and (3) establishing the impact of

these factors on a tangible measure of successful

knowledge transfer (in this case, project success).

The authors described a theoretical model that shows

the relationship between OLFs (Organizational

Learning Factors), PLPs (Project Learning Practices)

and PSVs (Project Success Variables). The authors

investigated three issues: (a) What constitutes OLFs,

PLPs, and PSVs?; (b) What relationships exist in IT

organizations among OLFs, PLPs, and PSVs; and,

to OLFs and PLPs? The results of their research

demonstrated a positive and significant correlation

between organizational learning, project learning

and project success in IT organizations. Factors

related to organizational learning are important for

learning. For example, if an organization does not

have the confidence, leadership and incentives,

project teams are less likely to implement project

learning practices.

Mehta et al. (2014) report the results of the study

that verified the effects of information technology,

transfer and combination of knowledge and

uncertainty of software projects. The study

considered the three dimensions of social capital: (a)

structural dimension (links between people or units),

(b) relational dimension (trust through interpersonal

relations), and (c) cognitive dimension (sharing of

understanding and interpretations). The results of the

study indicated that both the transfer and

combination of knowledge are necessary to fully

explain the relationships and that the consideration

of the outcome of a project is also important.

Furthermore, while project uncertainty confounds

the knowledge-sharing processes regardless of

technology, the frequency of routinely technology

use increases knowledge transfer and combination in

a software team.

The work of Anantatmula (2008) shows the

challenges of KM from the point of view of

leadership. The author sought to answer the

following questions: (a) “How does an organization

manage knowledge resources to gain and sustain

competitive advantage?” and (b) “What is the role

of KM leadership in making effective use of KM?”.

The author conducted a literature review to

understand the role of leadership and the relationship

between KM and organizational performance. In

addition, two research studies using interpretive

structural modeling (ISM) were used to answer the

two research questions. The author shows that

effective leadership is a prerequisite for

implementing a KM initiative, and the organization

can achieve better results by choosing a leader

before starting and developing the implementation

of a KM plan.

Aurum et al. (2008) show the results of a study

that investigated the current practices of Knowledge

Management in Software Engineering (ES) in two

Australian software development organizations. The

authors also examine the facilitators of the KM

process for Software Engineering (leadership,

technology, culture, measurement and social

networking). The results showed that among the five

KM facilitators, leadership was considered the most

significant factor. Technology was also considered

to be an obvious mechanism for KM, despite some

of its current KM systems being unsuitable or

inaccessible. In addition, the role that informal

personal networks played in accessing tacit

knowledge was seen as one of the main reasons for

fostering a culture that encourages participants to

share their knowledge with others. Informal

networks were also cited, such as knowledge

management systems (informal networks and

personal networks).

5 DISCUSSION

The results of the systematic mapping show that

there is no consensus on the most used influence

factors in knowledge management initiatives.

Different authors use several factors. The papers

show the relationship between influence factors and

knowledge management based on statistical data or

based on case studies / project analysis.

Based on our results (see Table 3), four factors

stand out: Organizational Culture, Leadership,

Information Technology and Knowledge Social

Network. In the following subsections, we will

describe the results found in literature with regards

to these factors of influence.

5.1 Organizational Culture

Culture is a basic element for knowledge

management (Choi and Lee, 2003). Organizational

Inﬂuence Factors for Knowledge Management Initiatives

culture is composed of practices, symbols, habits,

behaviors, ethical and moral values, as well as

principles, beliefs, formalities, internal and external

policies, systems and organizational climate (Ajmal

and Koskinen, 2008). Organizational culture can act

as a barrier or facilitator to success in KM initiatives

(Kayworth and Leidner, 2004; Ajmal and Koskinen,

2008).

Organizational Culture (OC) affects how

members learn, acquire, and share knowledge

(Gupta and Govindarajan, 2000). According to Boh

et al. (2013), a positive organizational culture is

needed to promote learning and sharing of skills and

knowledge. OC supports KM in the software

development context which can be encouraged, for

example, by sharing knowledge and improving the

opinion of post-mortem analyzes (Aurum et al.,

2008).

Wang and Wang (2016) argue that an

organizational culture with a positive orientation

towards knowledge demonstrates that: (1) people are

willing and free to explore; (2) senior management

encourages employees to create, share and apply

knowledge; (3) people are not inhibited to share

knowledge; and, (4) people are rewarded for

innovation and learning.

Several instruments were developed to evaluate

the Organizational Culture, for example: a)

inventory organizational culture; b) organizational

culture profile; c) six-dimensional model and

concurrent values model; d) organizational profile

questionnaire; and, e) values framework (Giritli et.

al., 2013).

The Competing Values Framework (CVF),

created by Cameron and Quinn (2008), is one of the

most frequently used instruments in the literature

(Paro and Gerolamo, 2017). Based on the

identification of the four cultural types of CVF,

Cameron and Quinn (2008) developed and validated

the Organizational Culture Assessment Instrument

(OCAI). This instrument uses a questionnaire to

establish an Organizational Culture profile based on

the four types of culture, i.e. the instrument

evaluates the relative importance of the elements

from each type of culture in an organization.

We have identified in the literature that authors

propose some relationships between organizational

culture and knowledge management, but these

relationships have not yet been proven. For example,

in the paper by Rabelo et al. (2015), the authors

sought to relate the CVF model (Cameron and

Quinn, 2008) and SECI (Nonaka and Takeuchi,

1995) based on a theoretical model of the literature

(Rai, 2011; Gray and Densten, 2006). However,

when comparing our research results with the results

found by Rai (2011) and Gray and Densten (2006),

we did not find evidences that there is a relationship

between quadrants of the SECI model and CVF in

the way literature proposed. Therefore, future

research should be conducted in order to answer the

following research question: “Is the relationship

between the SECI model and the CVF model similar

in other software organizations?”.

Chang and Lin (2015) made the relationship

between five types of organizational culture and four

kinds of KM process. Nevertheless, the relationships

need more studies to be proven.

The results of the papers related to organizational

culture show that more research should be carried

out seeking to understand: "(i) how can the

organizational culture influence knowledge

management initiatives in different software

organizations; (ii) What is the relationship between

organizational culture and knowledge management

in different software organizations. Is there a model

that can be used in different organizations? Using

such a model, will the result be the same in all

organizations? "

5.2 Leadership

In the context of this work, we identified that

leadership can be of three types: a) leader: person

who can influence other people; b) organizational

leadership: person who performs the role/function of

team leader or team manager; and, c) top

management: person who is responsible for the

highest level of the hierarchy of an organization, for

example: a director, president, manager or

coordinator.

5.2.1 Leader

Leadership is seen as the ability to influence the

behavior of others to align their goals with the ones

of the leader (Liu and Fang, 2006). Team leadership

should create an environment that encourages

knowledge sharing, so that people feel secure in

contributing and that these contributions are

recognized by all (Storey and Barnett, 2000).

Team leaders are responsible for how the

business must address and deal with the knowledge

management processes. Leaders are important

because they are examples and set standards to be

followed by people (Holsapple & Joshi, 2000).

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5.2.2 Organizational Leadership

Leadership has a critical role in developing and

managing KM systems (Anantatmula, 2008). Merat

and Bo (2013) state that the organization should

choose the leader who will manage the aspects

related to knowledge management before starting

and developing the implementation of a QA plan.

The initiative of a KM program can be a major

change in the organization, so the leadership

involvement is considered fundamental (Davenport

et al., 1998).

The organization's leadership should encourage

people to take part in the decision-making, and share

knowledge. Collaborative decision-making often

leads to innovation (Aurum et al., 2008). In addition

to identifying success measures, the inclusion of

decision makers is a critical aspect of leadership that

should not be underestimated (Schwarber, 2005).

Aurum et al. (2008) show that in the studied

company, a variety of roles were responsible for

leadership. In one of their projects, the participant

cited quality role, business analyst, project manager,

or team leader acting as team leaders. In other

project, a participant stated that each participant in

the team was responsible for leading their own

knowledge. These results may be indications for

future research: "What is best for a software

organization? (1) to define a single person as

responsible for leadership or (2) that each team

member is responsible for managing his/her own

knowledge?"

5.2.3 Top Management

Sharma and Yetton (2007) argue that top

management support can reduce resistance, resolve

conflicts, improve communications, persuade

employees, and overcome barriers to KM

implementation. Top management should provide

sufficient resources and create a positive

organizational climate for the implementation of

knowledge management systems.

5.3 Information Technology

Information technology helps remove

communication boundaries that often hinder the

interaction between different parts of the

organization (Allameh et al., 2011). It is important to

invest in IT to expand knowledge management

projects (Lee and Choi, 2003). Information

technology should be used to assist in the specific

business needs and projects of the organization.

The results of Metha et al. (2014) indicate that

the use of information technology increases the level

of knowledge exchange under conditions of high

uncertainty in the projects. Nouri et al. (2013) claim

that information technology is the most important

factor when coding (knowledge management

strategy) is the main focus of company strategy.

A variety of IT tools are required to develop

Knowledge Management Systems (KMS).

According to Wang and Wang (2016), when a

company recognizes that a knowledge management

system can contribute to the efficiency and

effectiveness of its knowledge management

practices, then they are more likely to implement

KMS.

5.4 Social Network

Social networks are made up of connections between

individuals seeking knowledge from each other. A

social network can also be called knowledge

network, network of ties or informal networks.

Social network are effective because they show who

has the knowledge (Alavi and Tiwana, 2002).

A social network in which employees share

knowledge is an important factor for an organization

to gain the value of knowledge sharing from person

to person (independent of a knowledge management

system) (Jennex, 2008). These networks are used by

people to exchange resources and services (Aurum

et al., 2008).

Research found in literature has used Social

Network Analysis (SNA) to verify knowledge

management (Helms et al., 2010; Müller-Prothmann

et al., 2005; Anklam, 2003). SNA focuses on the

relationships between nodes, since these

relationships influence the nodes themselves.

Basically, a social network represents a set of

relationships of a group (Wasserman & Faust, 1994).

The actors within a social network can be

individuals, groups, entities or organizations. The

relationships between the actors can be any

connection they have, such as: people who consult in

order to ask a question related to their activities at

their job; people who modify the same source code

of an application; or relationships in the

dependencies between organizations.

According to Müller-Prothmann et al. (2005),

social network analysis can assist: the identification

of personal and knowledge skills; the research on the

transfer and sustainable conservation of tacit

knowledge; and the discovery of opportunities to

improve communication and efficiency processes.

According to Anklam (2003), SNA allows managers

Inﬂuence Factors for Knowledge Management Initiatives

to visualize and understand relationships that can

facilitate or make it difficult to create and share

knowledge.

6 CONCLUSIONS

In this systematic mapping, we investigated the

influence factors for knowledge management

initiatives in software organizations. From the initial

set of 1028 publications, we identified 22

influencing factors. There is no consensus on the

most commonly used influence factors in knowledge

management initiatives. Among the selected

publications, the following factors were the most

cited by different authors: Organizational Culture,

Leadership, Information Technology and Social

Network.

Every study has threats that could affect the

validity of its results (Wohlin et al., 2012). In this

work, some threats can be identified, such as: (a) the

researcher's bias regarding the analysis of the

primary studies - to minimize this bias, all activities

were reviewed by another researcher and we

performed the statistical Kappa de Kohen test (see

section 4.1); (b) limited university access to some

scientific databases, which may prevent some

publications from being accessed - we requested the

full publication of the authors whenever possible and

included those that have been made available; and

two selected databases - although the research has

been conducted in only three databases, these

databases index publications from a large number of

well-known venues, journals and conferences; which

may reduce the number of publications that were not

addressed by this literature review.

Although several papers investigate the

relationship between influence factors and

knowledge management, there is still a shortage of

papers that show how these factors influence and

how they can be used to support knowledge

management initiatives in software organizations.

We also identified that there is no single way to

assess these factors. Many surveys state that they use

evaluation questionnaires, but do not show them, or

provide details on where they were taken from or

how they were created. In addition, there is also a

gap with regards to which actions an organization

can take regarding these factors.

As future work, one can investigate how

addressing one or more of these influencing factors

can improve knowledge management initiatives in

software organizations. Therefore, due to the

differences between software development

companies, one can include their type as influencing

factor. Other sources and knowledge artifacts can

also be considered in software development

companies themselves in the research. In addition to

the results presented in this paper, as part of this

research, a catalog containing actions related to the

most cited factors of influence is being developed.

The purpose of this catalog is to encourage software

organizations with regards to the state of practice

based on the findings of the state of the art. This

catalog of actions will map the knowledge

management practices that the software organization

can apply in their KM initiatives. The actions

catalog will be part of the IFactor-KM Process

proposed by Rabelo and Conte (2017). The IFactor-

KM Process supports software organizations to: a)

identify the knowledge management objectives; b)

check how tacit knowledge is shared; c) indicate the

knowledge experts; d) understand leadership

aspects; e) characterize the profile of the

organizational culture; and f) suggest knowledge

management practices and action.

Finally, we hope that our results can contribute

to the evolution and improvement of the research

field of influence factors for knowledge

management in software organizations.

ACKNOWLEDGEMENTS

We would like to thank the financial support granted

by CNPq through process number 423149/2016-4,

and CAPES through process number 175956/2013.

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