IFactor-KM: A Process for Supporting Knowledge Management

Initiatives in Software Organizations Considering Influencing Factors

Jacilane Rabelo and Tayana Conte

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

Keywords: Knowledge Management, Influencing Factor, Process, Software Organization.

Abstract: Knowledge management has become a real need in the software industry. Knowledge management factors

refer to management and organizational factors that need to be addressed effectively in order to increase the

chances of a knowledge management successful implementation. Many organizations have questions about

the approach they should take in their knowledge management initiatives. Literature studies have been

conducted to identify the factors that affect the implementation of a knowledge management, but do not

suggest knowledge management practices for organizations. A process named IFactor-km (influencing

factors on knowledge management) was created to address these needs. The goal of this process is to

support knowledge management initiatives and to suggest knowledge management practices for software

organizations considering the following influencing factors: people, leadership and culture. The IFactor-KM

supports software organizations by: a) identifying the knowledge management objectives; b) checking how

tacit knowledge is shared; c) showing the knowledge experts; d) understanding leadership and people

aspects; e) characterizing the organizational culture profile; and f) suggesting knowledge management

practices. The process is composed of: i) a procedure detailing the steps of the process; and ii) a set of

artifacts detailing how to use the process and examples of completed artifacts to facilitate the use of the

process.

1 INTRODUCTION

Knowledge is considered to be a valuable asset and a

key resource for the permanent competitive

advantage of an organization (Allameh et al., 2011).

Organizations have problems with keeping track of

content, its location, and how to best make use of it

(Rus and Lindvall, 2002). Knowledge Management

(KM) is important in large, medium and small

organizations (Le Dinh et al., 2014).

Knowledge in software development projects is

varied and grows in proportions (Carreteiro et al.,

2016). 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 (Rus

and Lindvall, 2002). Organizations are suggested to

share knowledge of how they believe that this effort

will result in: (a) productivity, (b) performance and

effectiveness, (c) improving efficiency, (d) cost

reduction, (e) reduction of available resources and

(f) quality improvement (Yang, 2009; McAdam and

Reid, 2000).

According to Moffett et al. (2002), many

organizations have questions about the approach

they take in their KM initiatives. Several papers in

the literature have investigated which facilitators

influence KM implementations. 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 and Hassan, 2012). For example, 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) are significant influences on the

implementation of knowledge management systems

in organizations. Allameh et al., (2011) conducted a

study to determine the impact of KM facilitators and

the KM process. The results show that the

information technology and culture facilitators are

related to the KM process. Also, their results suggest

that the organizational structure is not related to

knowledge management processes. Furthermore, the

166

Rabelo, J. and Conte, T.

IFactor-KM: A Process for Supporting Knowledge Management Initiatives in Software Organizations Considering Inﬂuencing Factors.

DOI: 10.5220/0006360701660177

In Proceedings of the 19th International Conference on Enterprise Information Systems (ICEIS 2017) - Volume 2, pages 166-177

ISBN: 978-989-758-248-6

research by Mehta et al., (2014) concludes that the

main factors contributing to effective knowledge

management are human and technical. Human

behavior is the key to success or failure in KM

activities, since KM involves an emphasis on

organizational culture, teamwork, learning

promotion, and sharing of skills and experiences.

This work differs from the previously mentioned

researches, since it intends to list and suggests

practices for KM initiatives in software

organizations considering factors influencing these

initiatives. The main goal of this paper is to present

the IFactor - KM (Influencing Factors on

Knowledge Management initiatives) Process.

The goal of the IFactor KM process is to support

knowledge management initiatives and to suggest

knowledge management practices for software

organizations considering the following influencing

factors: people, leadership and culture. The proposed

process supports software organizations to: a)

identify the knowledge management objectives; b)

check how tacit knowledge is shared; c) show the

knowledge experts; d) understand leadership and

people aspects; e) characterize the profile of the

organizational culture; and f) suggest knowledge

management practices.

Besides this introductory section, the paper is

organized in four more sections. Section 2 presents

the background for this research. The IFactor-KM

process and its details are shown in Section 3.

Section 4 presents some results obtained using the

process. Finally, Section 5 shows the conclusions

and future work.

2 THEORETICAL

BACKGROUND

The knowledge that an organization can hold and its

ability to create and use that knowledge is the central

ability to maintain a competitive advantage and

innovation (Nonaka and Teece, 2001). The main

asset of Software Companies is knowledge (Silva-

Filho et al., 2016). The development practices of

software organizations are based on the knowledge

and experiences of software developers and

stakeholders (Vasconcelos et al., 2009). The

collection, storage and sharing of knowledge is

essential, but hard to do. By managing knowledge,

organizations can better respond to customer and

market demands, delivering faster and better-quality

results (Schneider, 2009).

According to Nonaka and Takeuchi (1995), there

are two types knowledge that need to be managed:

tacit and explicit. Tacit knowledge is based on the

person's experience, which, due to being subjective,

is difficult to express with words, numbers and

sentences (Nonaka and Takeuchi, 1995). This kind

of knowledge cannot be found in documents, but

only in the minds of the collaborators (Dingsoyr et

al., 2009). Therefore, tacit knowledge is usually

shared directly, by face-to-face contact, and is

considered the most valuable type of knowledge

(Patel, 2012). On the other hand, explicit or codified

knowledge is considered transmissible in formal and

systematic language. Nonaka and Teece (2001) state

that, since it is objective, this type of knowledge can

be represented in several ways, such as documents,

reports, databases and others. Also, it can be

processed, transmitted and stored easily. Only

managing the types of knowledge is not enough. It is

necessary that this knowledge is learned at the

organizational level, so that it adds success to the

executed software development activities.

Several authors propose different objectives for

knowledge management (Probst et al., 2000;

Tiwana, 2000; Alavi and Leidner, 2001; Rus and

Lindvall, 2002). For instance, Alavi and Leidner

(2001) did a research of works that used the KM

objectives and carried out a feature analysis. Based

on the comparison of these works, they reached the

following goals: creation, storage/retrieval, transfer

and application. Due to the feature analysis already

performed by these authors, the defined KM

objectives used in our present work consider the

steps defined by Alavi and Leidner (2001).

Knowlede Creation is about creating new

knowledge or replacing existing knowledge. This

creation can be done by individuals, throughout the

organization or acquisition of external sources

(Rodríguez-Elias et al., 2008). Knowledge

Storage/Retrieval is the process of storing the

knowledge after it is created so that other people in

the organization can access it. This process feeds

and seeks to ensure that the organization does not

forget what it has learned or the knowledge that has

been created. Knowledge Transfer focuses on

activities aimed at the dissemination and distribution

of knowledge. This transfer can occur at various

levels, such as: between collaborators, from

employees to explicit bases, from collaborator to

group, within a group, between distinct groups and

from the group to the whole organization (Alavi and

Leidner, 2001). Knowledge Application occurs

when knowledge of a given domain is applied. Thus,

it is possible to generate new knowledge.

Research found in literature has used Social

IFactor-KM: A Process for Supporting Knowledge Management Initiatives in Software Organizations Considering Inﬂuencing Factors

167

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 and Faust,

1994). These actors 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; two people who

modify the same source code of an application;

relationships in the dependencies between

organizations; and so on.

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 in the discovery of opportunities to

improve communication and efficiency processes.

According to Anklam (2003), SNA allows managers

to visualize and understand relationships that can

facilitate or make it difficult to create and share

knowledge.

2.1 Influencing Factors in Knowledge

Management

In order to identify the factors that influence

Knowledge Management initiatives and the ways of

evaluating these influencing factors, a research was

conducted in the literature.

AL-Hakim and Hassan (2012) conducted a

literature review that addressed the factors that

influence knowledge management, increase

innovation, and improve organizational

performance. According to analyses AL-Hakim and

Hassan (2012), most of the explored factors within

the identified works mention:

 human resources management;

 information technology;

 leadership;

 organizational learning;

 organizational strategy;

 organizational structure;

 organizational culture.

The most cited are: organizational culture,

structure and information technology.

We carried out a search for other research papers

in the Scopus library (www.scopus.com). The aim of

that search was to identify the influencing factors in

knowledge management initiatives in software

development companies. In addition, our search

aimed to identify the evaluation questionnaires used

by other researchers. Our search results showed that

the most cited factors are: leadership, people and

information technology.

2.1.1 Organizational Culture

Organizations should establish an appropriate

culture that encourages people to create and share

knowledge within an organization (Holsapple and

Singh, 2001). Organizational Culture (OC) works as

a repository of knowledge, as it determines how

individuals act and behave (Gonzalez and Martins,

2014). Also, Alavi and Leidner (2001) state that OC

is considered a critical factor in the construction and

effort of Knowledge Management, and can act as a

barrier or facilitator of these initiatives.

According to Ribiere and Sitar (2003),

Organizational Culture has been identified as the

main impediment for the occurrence of activities

related to knowledge management. OC affects how

members learn, acquire, and share knowledge

(Gupta and Govindarajan, 2000). Organizational

Culture support to KM in the software development

context can be encouraged, for example, by sharing

knowledge and improving the opinion of post-

mortem analyzes (Aurum et al., 2008).

Several instruments were developed to evaluate

the Organizational Culture. Among these

instruments, we can cite (Giritli et al., 2013): a)

inventory organization culture; b) organization

culture profile; c) six-dimensional model and

concurrent values model; d) organizational profile

questionnaire; and, e) values framework.

In this work, we used the evaluation

questionnaire proposed by Cameron and Quinn

(2006) - the Computing Values Framework (CVF).

CVF is one of the most used models in the research

area of organizational culture due to its reliability

and validity (Giritli et al., 2013).

Based on the identification of the four cultural

types of CVF, Cameron and Quinn (2006)

developed and validated the Organizational Culture

Assessment Instrument (OCAI). This instrument

uses a questionnaire to establish the organizational

culture profile based on the four types of culture. In

other words, the instrument evaluates the relative

importance of the elements of the types of culture

within an organization.

2.1.2 People and Leadership

People are seen as important elements in KM

initiatives (Ndlela and Toit, 2001). Holsapple and

Joshi (2001) argue that people are the key to the

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creation of organizational knowledge. It is the

people who create and share knowledge. People's

attitudes are an important pre-requisite in KM

projects (Naghib, 2003).

People have to feel like sharing and offering their

knowledge to other people inside the organization.

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).

The initiative of a KM program can be a major

change in an organization. Therefore, leadership

involvement is considered fundamental (Más-

Machuca, 2014; Storey e Barnett, 2000). Liu and

Fang (2006) argue that leadership is seen as the

ability to influence the behavior of others to align

their goals with those of the leader. KM leadership

should encourage people to participate in the

decision-making. In addition to identifying success

measures, the inclusion of decision makers is a

critical leadership aspect that should not be

underestimated (Schwarber, 2005).

2.2 Works on Knowledge Management

Practices

The works presented in the previous subsections

describe investigations of the factors that influence

knowledge management in organizations. However,

these works do not suggest KM practices that can be

employed in these organizations. Some papers

suggesting KM practices are shown below.

Viana et al., (2015) and Viana (2015) proposed a

framework to support organizations in the

identification of current Organizational Learning

(OL) and knowledge management practices. Also,

the authors suggest practices that these organizations

can use. This framework consists of a process

describing the steps required to identify current OL

and KM practices and activities, and the suggestion

of new practices that can be applied. The framework

also has a practice catalog that contains a list of

practices that can be used to support the diagnosis of

the current state of the organization, as well as

helping to suggest new practices for the

organization.

Menolli et al. (2015) presented a set of tools and

technologies used by software organizations. The

authors have related these tools and technologies to

theories of knowledge sharing and organizational

learning. The most commonly used tools and the

frequency of use by employees were identified. One

of the results of the authors' work shows that

although organizations have adopted the tools, they

are not often used.

The work by Santos et al. (2013) presents a set of

identified practices aimed at organizations that

execute agile development processes. The identified

practices were evaluated by an industry consultant

who employs aspects of agile methodologies and

knowledge management in their activities. In

addition, the set of practices were validated through

case studies in the industry. These practices aimed to

promote the interaction and knowledge sharing

among agile teams.

3 THE IFACTOR-KM PROCESS

Based on the results of the literature review, two

research categories were identified:

 Papers that show the factors that influence

KM initiatives (Wang and Wang, 2016; AL-

Hakim and Hassan, 2012; Allameh et al.,

2011; Holsapple and Singh, 2001);

 Research that suggests KM practices (Viana et

al., 2015; Viana, 2015; Santos et al., 2013).

However, we did not identify a work that linked

the two categories.

The greatest motivation of this work is to

propose a process for software organizations that

suggests knowledge management practices

considering the characteristics of each organization

through its influencing factors. By doing so, we

expect that organizations can use practices that meet

their needs in KM initiatives, and that they can

succeed in their knowledge management activities.

The IFactor-KM (Influencing Factors on

Knowledge Management initiatives) Process

proposed in this work aims to identify the levels of

KM in the organizations, support the diagnosis of

the state of practice and support the insertion of KM

practices in software organizations considering the

factors influencing the organization (i.e. culture,

people and leadership).

The Process IFactor-KM is composed of: a) a

procedure detailing the steps of the process; b) a set

of artifacts detailing how to use the process and

examples of finished artifacts to facilitate the use of

the process. The Process has activities and artifacts

that help software organizations to: i) identify the

knowledge management objectives; ii) check how

tacit knowledge is shared; iii) show the knowledge

experts; iv) understand leadership and people

aspects; v) characterize the profile of the

organizational culture; and vi) suggest knowledge

management practices.

This process contains the definition of activities

IFactor-KM: A Process for Supporting Knowledge Management Initiatives in Software Organizations Considering Inﬂuencing Factors

169

required to perform the diagnosis and identify

improvements in knowledge management activities

in software organizations. The IFactor-KM process

was specified using the Business Process Model and

Notation (BPMN) (OMG, 2016). The main elements

used for its representation are shown in Table 1.

Figure 1 shows the overview of the IFactor-KM

process.

Table 1: Elements of the BPMN notation used for defining

the IFactor-KM Process.

Start Event

End Event

Activity

Sub-process that contains other activities

Gateway

Link event - to throw

Link event - to catch

Sequence flow

Data object - input

Data object - output

3.1 Collecting Data

The first step of the IFactor-KM Process is to gather

the organization's data according to the following

activities:

 To identify the context of the organization and

the needs related to KM;

 To identify collaborators participating in the

diagnosis;

 To apply the KM Objectives Questionnaire;

 To apply the questionnaire that supports the

knowledge sharing diagnosis - through social

networks analysis;

 To apply the OCAI questionnaire.

The details of these activities will be shown next.

3.1.1 Identifying the Organization's Context

and KM Needs

The activity of identifying the organization context

and the needs related to KM aims to extract the

necessary information from the organization that

will participate in the diagnostic process. Table 2

shows the details of this activity.

The questionnaire used in this activity has fields

to describe the organization’s context, how it works

and the number of employees. Additionally, the

organization is asked to state the main needs

regarding knowledge management, in addition to

which KM objectives the organization wants best:

creation, storage/retrieval, transfer or application of

knowledge.

Table 2: Identifying the context of the organization and

the needs related to KM.

Name 1. To identify the organization context

and related needs

Description This activity is performed to identify the

necessary information that will guide the

diagnosis of the Knowledge

Management in software organizations.

This activity is also useful for suggesting

practices for the organization.

Variability Required

Tasks



Use the <<Organizational Context

Template>> document

 Fill in the document as in the

template

 Transfer these results to the

presentation <<Result Presentation

Template >> document

Participants Project Manager, Senior Management

Input Organizational Context Document

Output Finished Organization Context

Document

Results Presentation Document

3.1.2 Identifying Collaborators

Participating in the Diagnostic Process

The purpose of this activity is to identify who will

be the people participating in the Knowledge

Management diagnosis process. The person

responsible for executing the process in the

organization will request the data of each

participant. This activity has an artifact that helps to

document the data. The fields from the artifact in

this activity ask for the basic data from the

collaborator regarding his/her role, time in the

organization, projects in which (s)he participate and

time spent in these projects.

3.1.3 Applying the KM Objectives

Questionnaire

This activity aims to apply the knowledge

management objectives questionnaire with the

organization's collaborators. Therefore, it seeks to

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Figure 1: Overview of the IFactor-KM Process.

understand the level of KM in the software

organization or software teams. This is done with

respect to the KM objectives. The investigated KM

objectives are in agreement with those defined by

Alavi and Leidner (2001): creation, storage/retrieval,

transfer and application of knowledge.

The KM objectives are evaluated through a

questionnaire. The employed evaluation

questionnaire was based on the one proposed by

Lawson (2003). It is possible to identify, through the

results of the applied questionnaires in the

organization, what the purpose of the KM is being

used and what needs to be improved. By doing so,

one can verify if these characteristics are meeting

the real needs of each organization.

3.1.4 Applying the Questionnaire that

Supports the Knowledge Sharing

Diagnosis – Social Networks Analysis

The goal of applying this questionnaire is to identify

how knowledge sharing occurs among the

organization's collaborators. This questionnaire

helps to identify social networks aspects. In this

questionnaire, the collaborators inform who they

consult inside the organization to obtain knowledge

or who they ask questions to about their daily

activities.

3.1.5 Applying the Questionnaire that

Evaluates the Organizational Culture

Profile

The purpose of this activity is to apply a

questionnaire that evaluates the profile of the

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organizational culture. This questionnaire is called

the Organizational Culture Assessment Instrument

(OCAI) and was proposed by Cameron and Quinn

(2006). Through the results of this questionnaire it is

possible to identify the current and desired culture

profile of the organization. The artifact of this

activity provides an example to facilitate its use by

employees. This questionnaire is answered by all the

collaborators identified in the Identification of the

Collaborators Participating in the Process activity

(see Subsection 3.1.2).

3.2 Data Analysis

The second step of the IFactor-KM Process is to

analyze the data collected in the first step. Three

analyses are carried out at this stage: data analysis of

social networks, data analysis of KM objectives

questionnaires, and data analysis of the

organizational culture.

3.2.1 Data Analysis of Social Networks

This stage includes the identification of how sharing

of the tacit knowledge in the organization or teams

takes place through the analysis of social networks

(SNA). Social network analysis aims to find an

understanding of the relationship between entities, as

well as investigate the patterns and implications of

these relationships (Wasserman and Faust, 1994). It

is important to mention that there are several types

of relationship patterns in social network analysis, as

well as metrics that can be useful for analyzing a

social network (Wasserman and Faust, 1994; Cross

and Parker, 2004). However, this research focused

on the identification of some specific types of

collaborators, which are important for the

recognition of opportunities, challenges related to

knowledge dissemination and identification of the

expert of an organization.

The sub process of analysis of social networks is

composed of activities that help to identify: i) the

most consulted people in the organization; ii)

knowledge flow between the leader and the team;

iii) knowledge flow between novice practitioners

and the team; iv) peripheral people in the team; and

v) centrality of the information.

These activities are presented below.

Transcribing Questionnaires - Social Networks

Worksheet

The purpose of this activity is to transcribe the

questionnaires identifying the knowledge sharing

that was filled out by the collaborators.

Preparing Data to the Social Networks Structure

The purpose of this task is to develop a structure of

social networks. This structure of social networks is

created through the use of social network analysis

tools. Aiming to facilitate the implementation of this

activity was drafted a document explaining the steps

needed to create a social network structure. This

document is part of the artifacts proposed for the use

of the IFactor-KM Process.

Identifying the Most Consulted People in the

Organization

The purpose of this activity is to highlight the most

consulted people in the organization. These people

are the ones who most share tacit knowledge.

That data is obtained using a social network

analysis tool. Also, we describe the steps to facilitate

the presentation of the results to the organization

that has been evaluated. Among, these, we can cite:

i) to highlight the most consulted people: these are

the people who are the closest to the center of the

graph. (we suggest highlighting the four people

closest to center); ii) for each of these specialists,

provide a description of the results, while pointing

out the most consulted knowledge from this person.

Identifying Knowledge Flow between the Leader

and the Team

The purpose of this activity is to show how the

knowledge flow between the leader and the team

occurs. This information helps the organization

members to get to know each other better.

IFactor-KM Process has some guidelines that

help each member identify the flow of their

knowledge exchange. First, based on the SNA

graph, we highlight the team leader and then check

the relationship between the leader and the team.

The results of this analysis allow identifying who are

the collaborators who consulted the team leader

whenever they have a question.

Identifying the Knowledge Flow between Novice

Practitioners and the Team

This activity is proposed in the IFactor-KM Process

to identify how the knowledge flow occurs between

novice practitioners (collaborators who have been in

the team for less than six months) and team

collaborators. This information helps verifying if

novice practitioners have access to the team leader,

as well as verifying if they exchange knowledge

with other collaborators who perform the same role

that they have. It is also possible to identify

relationships between novice practitioners and

experts on certain relevant subjects for the

organization.

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Identifying Peripheral people in the Team

This activity aims to identify the peripheral people

of the team (employees who are poorly consulted by

other employees). These people are the people who

have few connections within the network. In other

words, these people are further away from the center

of the graph.

Centrality of Information - Knowledge Experts

The purpose of this activity is to identify

collaborators who have a large part of the team's

information. The centrality of information shows

how information flows through many different

paths. Therefore, it uses all the paths between the

actors (when they consult and when they are

consulted about a certain subject). For this analysis,

a social network analysis tool is used.

3.2.2 Data Analysis of KM Objectives

Questionnaire

The sub process of the data analysis of the KM

objectives questionnaires is composed of the

following activities: a) transcribing the

questionnaires for identifying KM objectives; b)

generating totalizer graphs for each KM objective; c)

identifying the perception of the leaders and people

in the team.

These activities are detailed below.

Transcribing the KM Objective Identification

Questionnaires

The purpose of this activity is to transcribe the

questionnaires of identification of knowledge

management objectives. Each questionnaire filled

out by collaborates must be transcribed. The artifact

created in the IFactor-KM Process for this activity

has the full details on how it can be used.

Generating totalizer graphs for each KM

Objective

The purpose of this activity is to show the graphs

with results of each of the knowledge management

objectives. These graphs are generated based on the

data recorded in the "Transcribing the KM Objective

Identification Questionnaires" activity.

The graphs show the total degree of agreement

(totally agree and agree) and disagreement (disagree

and totally disagree) with respect to the KM

objectives (creation, storage/retrieval, transfer and

application).

Identifying the Perception of the Leaders and

People in the Team

The purpose of this task is to show the perception of

the team leader with respect to the KM objectives.

The results help the organization to know how the

leader perceives the process of knowledge

management in the team.

The process allows identifying which employee

has a different response when compared to most of

the participants. These aspects are provided to the

evaluated organization so that it can talk to

employees and understand their point of view.

The identification of the perception is done for

each of the KM objectives. First, the median of the

agreement and disagreement answers from all

participants is calculated. This result is compared to

the response of the team leader. The result shows

this difference in responses between the leader and

the team.

3.2.3 Data Analysis of the Organizational

Culture

The Organizational Culture profile is analyzed at

this stage of the IFactor-KM process. The sub

process of the data analysis of the organizational

culture has activities in order to transcribe the

applied questionnaires and to generate the graphs

that show the profile of the culture. These activities

are presented below.

Transcribing the OCAI Questionnaire - Culture

Profile Identification Worksheet

The goal of this activity is to transcribe the

questionnaires identifying the organizational culture

profile that were filled by the collaborators.

Generating the Organizational Culture Profile

Charts

The purpose of this activity is to show the charts that

show the profile of the organizational culture

(current and desired). It is obtained through the

OCAI questionnaire that was applied in the

"Applying the Questionnaire that Evaluates the

Organizational Culture Profile" activity.

The application of the OCAI questionnaire

makes it possible to analyze the organizational

culture profile, as well as the single results from

each of the six dimensions that form the instrument,

which are: (a) dominant characteristics; (b)

organizational leadership; (c) organizational

management; (d) the “glue” that keeps the

organization together; (e) strategic emphasis; (f)

success criteria. In the investigated organization, the

organizational culture profile is analyzed according

to the perception of each team.

The graphs generated in this activity show this

analysis of the organizational culture. That is, the

current and desired profile, as well as the stratified

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analysis.

3.3 Identifying and Listing Practices

In the third stage IFactor-KM Process practices

identification and listing activities are carried out.

The activities from this stage are to:

 Identify practices according to the results of

the social network - strengthening current

practices;

 Identify practices in accordance with the goals

of KM- strengthening current practices;

 List practices for the organization according to

the collected data;

 Identify practices according to the KM

objectives that the organization wants to

achieve.

The detail of these activities is shown below.

3.3.1 Identifying Practices According to the

Social Network Results -

Strengthening Current Practices

This activity of the IFactor-KM process aims to

identify which practices may already be in use in the

organization. This identification is based on the

results of social network analysis. KM practices

related to social networks are selected when most of

the employees (51% or more of the total number of

employees) exchange tacit knowledge.

3.3.2 Identifying Practices According to the

KM Objectives - Strengthening

Current Practices

The purpose of this activity is to identify which

practices may already be employed in the

organization. This identification will be based on the

results of agreement with the KM objectives.

The analysis of this step is done with regards to

the total of agreement with each KM objective

pointed out in the questionnaire. When the response

count is more than 50% of the total responses for

each KM goal, practices that can meet KM

objectives are highlighted in the "KM Practice

Catalog" document. The suggestion of these

practices aims to strengthen or improve what may

already be practiced in the organization.

3.3.3 Listing Practices for the Organization

According to the Collected Data

The list of practices is in accordance with the

analysis of the previous activities: "Identifying

practices according to the results of the social

network - Strengthening current practices" and

"Identifying practices according to the KM

objectives - Strengthening current practices".

Based on the results, the organization's current

situation is presented to the organization – i.e. what

it does (even if it does not know that it does). These

results include a listing of all practices and examples

of application of these practices. In addition,

examples are given to contemplate other KM

objectives and the totalizer graph of each KM

objective.

Table 3 shows an example of a practice listed in

the 'KM Practice Catalog' document. The "Experts

participation in certain activities of the organization"

practice is related to the creation, transfer and

application of knowledge objectives.

Table 3: Part of the Practice Catalog with an Example of a

KM Practice.

Practice: Experts participation in certain activities of

the organization

Definition: This practice supports the dissemination of

the organizational knowledge. Also, it enables problem

solving during project / process execution and in

updating the organizational knowledge base.

Note: The experts on a certain subject are obtained

through the Knowledge Expert Identification activity.

Creation

Knowledge

Create new knowledge for the

organization, so that it can be

used in future projects, such as

components. In addition, the

expert collaborators stimulate the

creation of new knowledge in the

organization, through innovation.

Examples of how to achieve this

result:

 New solutions created by

specialists.

Knowledge

Storage/Retrieval

Knowledge

Transfer

They support the exchange of

specialized knowledge about

some important process,

technology or business rule for

the software development.

Examples of how to achieve this

result:

 Consult experts in case of

questions.

Knowledge

Application

Involving experts in problem

solving assists the application of

knowledge.

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3.3.4 Identifying and Suggesting Practices

According to the KM Objectives that

the Organization Wants to Achieve

The purpose of this activity is to identify and suggest

knowledge management practices according to the

KM objectives that the organization wants to

achieve. The results also show the graph according

to the KM objectives.

In the first activity of the IFactor-KM- Process

(i.e. "Identifying the context of the organization and

the needs related to KM"), the organization indicates

what KM goals it wants to achieve. For example, an

organization may be more focused on knowledge

creation, but wants to be focused on knowledge

storage/retrieval. According to the requirements of

the organization, the practices that meet this

objective are selected in the "KM Practice Catalog"

document. For each suggested Practice, examples

are given of how to apply them in the organization.

3.4 Presenting Results

The last step of the IFactor-KM Process is to show

the results to the organization participating in the

process. The purpose of this activity is to show to

the organization all the results through the IFactor-

KM Process. The collected and analyzed data in the

previous steps are presented to a senior management

of the organization and others authorized by them.

The discussion of the results is carried out in a

meeting and recorded.

4 EXPERIENCE OF USE THE

IFACTOR-KM PROCESS

An experience of using the complete IFactor-KM

Process was performed to verify the proposed

process and to improve the employed artifacts. This

usage experience was made with real data from a

software organization.

The software organization is responsible for

developing and maintaining information systems in

several contexts such as education, human resources,

public safety, administration, planning and health.

The organization has 392 employees, divided into

several software teams. This organization is in the

process of changing its organizational structure and

is interested in understanding how knowledge

management works for later improvements.

The data used for the IFactor-KM Process

experience was collected in one of the sector within

the organization that is responsible for projects

related to public sector management systems. This

sector is divided into two teams. A team is

responsible for a new system that is developed and

deployed in contracted companies. The other team

maintains and implements another system in

companies. The results are based on the data from

17 participants from this organization.

By using the entire IFactor-KM Process with the

actual data of the software organization, it was

possible to identify the applicability of the process.

Some of the obtained results regarding the

organization are:

 The organization's two software teams focus

more on sharing tacit knowledge. The leader

is accessible to the teams (all the collaborators

look for the leaders when they have a

question). The leaders of the two teams also

consult each other when they have questions;

 The identified culture profile in the teams is

more focused on teamwork, participation and

a high degree of commitment. The work

environment is considered to be “an extension

of the family”. This may be evidence of the

identification of tacit knowledge sharing in

these teams;

 The collected data shows that the organization

has little knowledge storage / retrieval. This is

a real need in the organization. KM practices

related to these be suggested to the

organization through the proposed IFactor-

KM Process.

5 CONCLUSIONS

Knowledge in software engineering is diverse and its

proportions are immense and growing (Rus et al.,

2001). (Rus et al., 2001). This causes organizations

to have trouble keeping track of what this

knowledge is, where it is, and who has it. Some

organizations have difficulties regarding which

approach to adopt in their KM initiatives. In order to

diminish these doubts, surveys are being carried out

seeking to create a body of knowledge about the

influencing factors on knowledge management.

People and leadership have also been identified

as factors that influence KM activities. People have

to feel like sharing their knowledge with others in

the organization (Storey and Barnett, 2000). The

team leaders should also create an environment that

encourages knowledge sharing.

The IFactor-KM Process (Influencing Factors on

Knowledge Management) was presented in this

IFactor-KM: A Process for Supporting Knowledge Management Initiatives in Software Organizations Considering Inﬂuencing Factors

175

paper. This process supports software organizations

to: a) identify the knowledge management

objectives; b) check how tacit knowledge is shared;

c) show the knowledge experts; d) understand

leadership and people aspects; e) characterize the

profile of the organizational culture; and f) suggest

knowledge management practices.

An experience using the entire process from the

IFactor-KM was performed. During this use

experience, it was possible to verify the whole

defined process, to identify and to suggest KM

practices for the software development organization.

A careful analysis is being carried out with the

collected data for later publication. Some of the

already identified results are: i) the organization's

two software teams focus more on sharing tacit

knowledge; ii) culture profile in the teams is more

focused on teamwork, participation and a high

degree of commitment; and iii) the collected data

shows that the organization has little knowledge

storage / retrieval.

As future work, we intend to carry out controlled

experiments in software organizations using the

IFactor-KM Process. By doing so, it will be possible

to verify the feasibility of the process and to improve

the KM activities in these organizations. In addition,

we intend to develop tool support for the proposed

process.

ACKNOWLEDGEMENTS

We thank the financial support granted by CAPES to

the first author of this paper (through a doctorate

scholarship); and by FAPEAM through process

062.00578/2014. Finally, we thank all the subjects

from the Software Organization who participated in

this study.

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