Knowledge
management in the software industry:

how Scrum activities support a knowledge management cycle

Gestão do
conhecimento na indústria de software: como as atividades do Scrum
dão suporte um ciclo da gestão do conhecimento

Nelson Tenório

Doutor em Ciência da Computação.
Pontifícia Universidade Católica do Rio Grande do Sul –
Brasil. [email-masked]

Danieli
Pinto

Mestra em Gestão do Conhecimento nas
Organizações. UniCesumar – Centro Universitário de
Maringá – Brasil. [email-masked]

Marcio
José Silva

Mestre em Gestão do Conhecimento nas
Maringá – Brasil. [email-masked]

Iara Carnevale de Almeida

Doutora em Informática. Universidade de
Évora – Portugal. [email-masked]

Flávio
Bortolozzi

Doutor em Engenharia de Computação. Université
de Technologie de Compiègne – França. [email-masked]

ABSTRACT

Knowledge is the primary asset of the software
industry which increases the complexity of the software development activities.
Knowledge Management (KM) is an important practice that offers processes namely
KM cycles and addressed to knowledge capture, creation, storage, dissemination,
sharing, and use. Thereby, understanding different means to consolidate
individual’s knowledge within the software industry is relevant to
software development practitioners and researchers. Therefore, this article discusses
how Scrum activities support KM cycle to convert the individual’s
knowledge into common knowledge within software development teams. Our analysis
points out that KM cycle combined with Scrum practices is a potential tool to
disseminate individual knowledge across software development team members.

Keywords:
Knowledge processes; Software development; Agile methods.

RESUMO

O
conhecimento é o principal ativo da indústria de software
aumentando o que aumenta a complexidade das atividades desenvolvidas. A Gestão
do Conhecimento (GC) é uma prática importante composta por
processos, que forma os ciclos da GC e direcionam a captura,
criação, armazenamento, disseminação,
compartilhamento e uso do conhecimento. Desse modo, entender os diferentes
meios para consolidar o conhecimento do indivíduo dentro da
indústria de software é relevante para os profissionais e
pesquisadores de desenvolvimento de software. Portanto, este artigo discute
como as atividades do Scrum suportam o ciclo KM para converter o conhecimento
do indivíduo em conhecimento comum dentro das equipes de desenvolvimento
de software. Nossa análise aponta que o ciclo de GC combinado com as
práticas do Scrum é uma ferramenta potencial para disseminar o
conhecimento individual entre os membros da equipe de desenvolvimento de
software.

Palavras-chave:  Processos
do conhecimento; Desenvolvimento de software; Métodos ágeis.

Recebido em 28/02/2019. Aprovado em
14/04/2019.  Avaliado pelo sistema double blind peer review. Publicado conforme normas da APA.

http://dx.doi.org/10.22279/navus.2020.v10.p01-13.928

1
INTRODUCTION

Software
development is essentially performed using agile methods. Agile methods aim to
accelerate the development and maintenance of software products by establishing
shorter deadlines, better quality results, risk mitigation and adding value to
the final product (Dyba & Dingsoyr,
2008; Cámara et al., 2016). The use of agile
practices allows software development teams to adapt themselves to the client's
changing needs through the interaction and collaboration among stakeholders,
which results in better project outcomes (Rola, Kuchta, & Kopczyk, 2016). The
agile method offers an incremental and evolutionary process for the development
of products (Pinoa, Pedreira,
García, Luaces, & Piattini,
2010). Beck et al. (2001) emphasize that Scrum is one of the easiest agile
methods to deploy, because it is simple and also because it has few control
practices, ensuring transparency, inspection, and adaptation of the project
(Lei, Ganjeizadeh, Jayachandran, & Ozcan, 2017; Schwaber &
Sutherland, 2016). However, Scrum does not include technical practices, it
emphasizes values and management practices only (Pinoa
et al., 2010).

The
software industry presents peculiar characteristics regarding other segments of
the market. They differentiate themselves by developing intensive activities on
knowledge and generating high added-value products (Bjornson & Dingsoyr, 2008; Chen, Ragsdell,
& O’Brien, 2014). The globalization of markets and the intensive use
of knowledge in the development of new products have contributed to the
software industry becoming a high technology sector (Binuyo,
Oyebisi, Olayinka, & Afolabi, 2015) making it
complex (Nawinna, 2011). Thus, it is necessary and
fundamental that people’s knowledge is incorporated into the processes
and activities of the organization, that is, one of KM’s roles (Meihami & Meihami, 2014;
Aurum, Daneshgar, & Ward, 2008). In this sense, for knowledge to be useful
to the organization, it must be externalized and socialized among the
individuals that compose it (Wiig, 1997). Therefore, efficiently managing
knowledge resources in a software development environment is critical to
maintaining organizational knowledge and promoting innovation (Aurum et al.,
2008).

The KM
literature presents cycles consisting of distinct processes of capture,
creation, dissemination, sharing, storage, and use of knowledge (Dalkir, 2011). The cycles allow KM to be systematized,
which contributes to both the implementation of organizational learning and to
the use of knowledge, creating value and enabling organizations to become
innovative and sustainable in the market in which they operate. However,
companies that use Scrum for software development simplify their processes and
amplify the focus on individual’s tacit knowledge (Nerur,
Mahapatra, & Mangalaraj, 2005).  Thus, KM performed with Scrum can
support knowledge transferring processes in software development outsourcing
(Brito, Figueiredo, Venson,
Canedo, & Ribeiro Junior, 2017).

Therefore,
based on the results of our previous work, shown in Tenório et al.
(2017), we present the following research question: ‘How are Scrum
practices is integrated to the KM cycle?’ We understand that the Scrum
methodology supports the incorporation of knowledge in the organizations’
processes and it is, this way, directly related to the KM cycles’
establishment activities and the execution of improvements. To answer our
research question, we carried out an exploratory study through the literature
regarding KM and the Scrum and comparing Scrum practices with the KM cycles.

This paper
is important once it shows how Scrum practices can be used to systematize and
incorporate the knowledge into the software teams, this way, not being used o
only as a method of control. In addition, this research provides support (both
to researchers and workers of the software industry) for deploying new
practices and processes and creating new knowledge. This review will also help
build a common understanding of the Scrum method and KM processes. Our findings
point out that Scrum enables knowledge to flow among software development team
members through a knowledge cycle based on the capture, creation, storage,
dissemination, sharing, and use of knowledge. The results of such research will
be relevant to the software industry.

The
article is organized as follows: in Section 2, we give an overview of Scrum, we
present the KM cycle and show the integration between KM and Scrum. Section 3
describes the methods used for this review. Section 4 reports the relation
between Scrum events and KM cycles. Section 5 concludes and provides
recommendations for further research on agile software development.

2 THEORETICAL BACKGROUND

2.1 Scrum

One of the
major innovations in software development was the introduction of the agile
methods that fostered process adaptation in a constantly changing environment (Vlaanderen, Jansen, Brinkkemper,
& Jaspers, 2011; Highsmith & Consortium, 2002).

The
"Agile Manifesto",established in 2001, allowed
the introduction of important concepts based on (a) individuals and
interactions rather than processes and tools; (b) running software rather than
extensive documentation; (c) client collaboration rather than contract
negotiation; (d) responding to changes rather than following a plan (Beck et
al., 2016). These concepts incorporated new methods, tools, techniques and the
use of the best practices in the software development processes. As a result,
agile methods have introduced a new vision on how to develop software (Dingsøyr, Nerur, Balijepally, & Moe, 2012). This has differentiated them
from traditional methods by having a set of values, principles, and practices
with a major focus on people rather than processes (Cristal, Wildt, & Prikladnicki, 2008).
In addition, agile methods can enhance productivity growth in software
development teams (Melo, Cruzes, Kon, & Conradi,
2013), causing a positive impact on both productivity and quality, e.g.,
knowledge sharing, stakeholders’ active participation, self-organizing
teams, and reduced documentation (Ahmed, Ahmad, Ehsan, Mirza, & Sarwar,
2010).

Zhang and
Dorn (2011) indicate that agile methods are more adaptive and flexible than the
traditional ones because they are suitable for scenarios where requirements
suffer constant changes and where results must be delivered in short time
frames. It should be noted that agile methods are characterized by the division
of the software development process into shorter cycles, with deliveries to the
client (internal and external) at the end of each cycle, thus enabling the
monitoring of changes to the requirements by the developers at the beginning of
each cycle and therefore reducing project risks (Eloranta,
Koskimies, Mikkonen, 2016).

Among
agile methods, Scrum is one of the most used in the software industry. One of
the main characteristics of this method is that it enables people to solve
complex and adaptive problems, delivering quality products in a productive and
creative way (Schwaber & Sutherland, 2016).
According to Nerur et al. (2005), Scrum focuses on
how team members should work in order to produce a flexible software system in
a constantly changing environment. Its structure revolves around product
management and development practices, dividing the software development process
into equal phases (called Sprints) which include monitoring and feedback
activities. These activities are carried out at daily and quick meetings with
the whole team, which aims to identify and correct deficiencies and/or
impediments during the execution of processes (Dönmez,
Grote, & Brusoni, 2016).  Therefore, Scrum is characterized by
being an iterative, incremental and team-based method (Cervone,
2011).

Scrum is
based on the collaboration among team members to incorporate value into the
final product (Zhang & Dorn, 2011). According to Schwaber
and Sutherland (2016), Scrum is based on three main cornerstones: transparency,
inspection, and adaptation. Transparency aims to make the team aware of all
project objectives in a uniform, clear and concise manner. Inspection is a way
to ensure constant revisions in the process to avoid misuse of the scope.
Finally, adaptation allows for emerging changes throughout the project to be
analyzed and adopted.

Planning,
preparation, work development, and delivery are the phases that guide the Scrum
cycle (Schwaber, 2004). The planning phase situates
the project and expectations among stakeholders; the preparation phase is
responsible for establishing and prioritizing requirements; the work
development phase implements the requirements established in Sprints, with the
addition of functionalities at the end of each Sprint and the delivery phase
executes the implantation of the system. Scrum also establishes fundamental
practices regarding team members’ roles, events, and artifacts necessary
to accomplish what is called "Scrum Cycle", presented in Figure 1.

The roles
define the necessary competencies for the project team, which consists of the
Product Owner, Scrum Team, and Scrum Master. The Product Owner represents the
interest of the team. Their function is to express with transparency what
should be done, determining requirements, functionalities, objectives and the
delivery plan (Marçal, Freitas, Soares, & Belchior, 2007). The Scrum Team is comprised of developers,
designers, testers, and architects. It has the authority and responsibility for
many aspects of the software development, including planning, scheduling,
assigning tasks to stakeholders and decision-making to achieve the proposed
goals (Moe, Dingsøyr, & Dybá, 2010). The Scrum Master plays the role of the
facilitating leader of the process. It clarifies the progress of the project
and propagates Scrum values to the entire team, ensuring the practice of daily
meetings and maintaining team performance (Lei et al., 2017).

Figure 1.  Scrum cycle

Source: The authors.

The events
include the execution of Sprints, a key element of Scrum. Sprints allow for
transparency and judicious inspection of the project. While the artifacts
comprise project objectives and the design of Sprints, consisting of the
Product Backlog, Sprint Backlog and product increment (Schwaber, 2016).

2.2 Knowledge Management Cycles

The KM
cycles comprise a set of processes and practices that seek to create, collect,
share and promote the dissemination of knowledge in the organizational
environment in order to help people carry out their work and learn from one
another (Ortegón, Lasso, & Steil, 2016). However, these processes
cannot be performed without concrete goals, and they must be directed towards
the organization. Thus, KM proposes a systematic process that takes place
through the development of interrelated and interdependent stages. In this
sense, it is considered that information in transit represents valuable
knowledge which is called the KM cycle (Ortegón et al., 2016).

A KM cycle
makes it possible to identify the route through which knowledge travels within
an organization. This way, it becomes clear when a KM process is being executed
and deserves more attention. A cycle can also follow an action that is related
to knowledge, locating the extension of one or more activities through stages.
It also enables processes or stages to be verified from the moment the
knowledge is created until it is made available or used by the organization
(Dalkir, 2011; Evans, Dalkir, & Bidian, 2014). According to Dalkir (2011),
KM objectives are as follows: i) to describe how knowledge is used; ii) to
raise awareness about the necessary KM activities; (iii) to reduce the
complexity of processes; iv) to design a KM solution.

According
to the Europe Committee for Standardization (Europe Committee for
Standardization) (CEN, 2004), there are over one hundred and fifty models of KM
cycles with similar objectives. Therefore, it is necessary to understand some
of the KM cycles proposals, thus delimiting the processes contained in some of
these approaches.

Goldoni
and Oliveira (2010) propose a KM cycle based on the creation, storage,
dissemination, utilization, and mediation of knowledge. The creation process
consists of acquiring and adding new knowledge to the existing one. In the
storage process knowledge is encoded and stored in knowledge bases. The
dissemination process is responsible for the diffusion of knowledge within the
organization when the use of the knowledge acquired is necessary. Finally,
measurement is the process of evaluating the results achieved with the use of
KM.

Table 1
shows the main KM cycles. It is observed that KM processes, carry out a cycle
which enables the creation of new knowledge, available from the last stage. The
processes of the cycles presented in Table 1 are similar to one another and
very similar to the proposal of Goldoni and Oliveira (2010). However, each
author considers a different situation when designing their cycle model.
According to the analysis presented by Dalkir (2011) and Evans et al. (2014),
the Wiig cycle predicts the existence in a business environment composed of
three basic elements: business, resources and the ability to act. The authors
also point out that the processes, contained in the Meyer and Zack cycle, are
derived from the development of information products. Moreover, the Bukowitz
and Williams cycle sketches its stages from the idea of how organizations
generate and maintain a minimum pool of knowledge for value creation. Finally,
the McElroy cycle is formed by processes formatted from a series of feedbacks,
having greater participation of the individuals involved in the processes.

Wiig (1993)

Meyer and Zack (1997)

Bukowitz and Williams
(1999)

McElroy (2003)

Build

Acquire

Get

Knowledge production

Hold

Refine

Use

Organizational knowledge

Pool

Store

Learn

Knowledge integration

Use

Distribute

Contribute

Distributed organizational knowledge
base

Present

Assess

Business processing environment

Build/Sustain
or Divest

Table
1. Main KM models and their stages

Source:
Adapted from Dalkir (2011)

Tenório
et al. (2017) presents a grouping of KM processes in three stages: i) capture
and creation, ii) dissemination and sharing; iii) acquisition and use. The
authors propose that after knowledge has gone through the capture and creation
process, it advances to the dissemination and sharing process. In this process,
the evaluation of the knowledge function and usefulness to the organization is
carried out. Moving on to the acquisition and use process, knowledge is
incorporated into the organizational environment through a language which is
common to all the ones involved. The authors indicate that when knowledge is
used to create new knowledge, there is a KM cycle to it, and that cycle starts
again. It is observed that although the cycles shown in Table 1 comprise
different phases, all seek to demonstrate knowledge in an organized and
systematized way. The purpose of it is to identify and make organizational
knowledge common to all so that the benefits from KM are effective.

2.3 Integrating Knowledge Management Cycle and Scrum Activities

Agile
methods influence communication, knowledge management, and the organizational
structure, emphasizing feedback cycles, small development iterations,
continuous planning and team self-organization, providing process transparency
among stakeholders (Dönmez et al., 2016). Furthermore, the use of agile
methods allows for the change of requirements throughout software development
which shows the continuous interaction between clients and developers
(Bannerman, Hossain, & Jeffery, 2012).

In a
systematic literature review on the use of agile methodologies in software
development identified that KM is one of the most popular topics among agile
researchers. According to the authors, this is because agile methodologies
promote a continuous learning environment (Dingsøyr et al., 2012).

Dorairaj,
Noble and Malik (2012) emphasize that Scrum activities encompass four distinct
KM processes: creation, coding, transfer, and application of knowledge. In
addition, the authors state that agile teams must be multifunctional and have
the skills necessary to perform software development activities.

Sungkur
and Ramaswmy (2014) worried about the inefficiency and delays that can occur
when exchanging knowledge among the members of the agile distributed team. They
have proposed a tool namely Knowledg4Scrum, to mediate and storing all the
information exchanged among the members of the team. They used data mining
techniques to promote the creation of new knowledge. According to the authors,
the proposed tool supports the processes of creation, storage, dissemination
and application of knowledge.

Miklosik,
Hvizdová, and Zák (2012) state that Scrum bases itself on the KM
principles in order to manage projects efficiently. The authors compare the
Scrum methodology and the KM principles. They conclude that Scrum helps
implement KM practices in organizations. Takpuie and Tanner (2016) have
proposed a theoretical model to identify the characteristics necessary for the
transfer of tacit knowledge among Scrum team members.

Morampudi
and Raj (2013) created an evaluation matrix among Scrum stages, containing the
phases and subphases of the KM framework stages and the model of knowledge
transfer framework. They suggested as the main contribution the identification
of the weaknesses and the choice of the best practices that fit the
methodology.

Singh,
Singh, and Sharma (2014) conducted an empirical study on the relationship
between KM practices and Scrum in software engineering organizations of India
which utilize agile methodologies in their work. The authors came to the
conclusion that most of the investigated firms use agile methodologies and that
they help manage knowledge by providing the right technologies to support KM
processes as a whole.

Eloranta
and Koskimies (2012) proposed several models to align knowledge architecture
management with Scrum. According to the authors, the way that KM is integrated
into Scrum depends on the organization culture, that is, it depends on how
organizations work while using Scrum.

3 METHOD

Knowledge
is the fundamental raw material of the software industry. The intention of
sharing and incorporating new knowledge, monthly, is what makes representatives
of companies of this sector, from a certain region located in South of Brazil,
meet. On April 17th, 2016 we were invited to attend one of these meetings. On
that day 52 representatives from 11 different companies were present. The
participants play different roles within the software development companies in
which they work, such as director, business analyst and project manager.
Throughout the meeting, we were given the time to ask questions to all the
participants about the practices used to maintain knowledge in their
organizations. One of the questions addressed the methodologies used for
developing software, and the use of the Agile Scrum method was unanimous among
the participants.

As the
agile Scrum method follows a cycle, just like the KM, then we tried to understand
how the two of them relate. For this, an exploratory study was carried and out
shows, in general, how the knowledge of the individuals involved in the
implementation and validation of software, behaves in the face of Scrum
practices.

3.1 Knowledge Management Cycle Adopted

Due to the
lack of a single KM cycle present in the literature, in this paper, we adopted
the KM cycle from Tenório et al. (2017), Evans et al. (2014), and
Goldoni and Oliveira (2010) as shown in Figure 2.

Figure 2. The adopted KM cycle

Source: The authors

Capture
process corresponds to the identification of the explicit knowledge that was
generated from the existence of tacit knowledge. Capturing consists of
searching either within the organization itself or in an external environment,
for information or knowledge which already exists and can be applied to a given
situation (Evans et al., 2014). Tenório et al. (2017) suggest that in
the capture phase there are implicit processes that lead to the production or
creation of new knowledge. The capture phase takes place after there is a
necessity to create new knowledge.

Creation
process consists in the continuous updating of the organizational knowledge,
promoting the interaction between individuals’ tacit and explicit
knowledge (Lyles & Schwenk., 2007). Evans et al. (2014) state that it is at
this stage that new knowledge is created. Tenório et al. (2017) argue
that the creation process is directly related to the capture process. Moreover,
the creation process may also happen based on information stored by the
organization.

Store
process consists of organizing the knowledge by using the information that can
be accessed and transferred, making it available to everyone in a structured
way (Milton, Shadbolt, Cottam, & Hammersley, 1999). This stage is after the
capture and creation processes, thus making acquired or created knowledge part
of the intangible assets of the organization. The storage structure should
provide a quick and categorized query, in a way that subjects are not randomly
stored, but rather by type or nature of problem (Evans et al., 2014).

Dissemination
process is characterized by the moment in which codified knowledge has the
potential to be shared (Evans et al., 2014). This helps ensure that important
information is not lost because it has already been evaluated and can be
disseminated more quickly and stored later. Krogh and Roos (1995) state that
the dissemination of knowledge is related to individual competencies. In this
sense, it is believed that the dissemination phase differs from the sharing
phase because it is an existing possibility on a certain subject and will only
be stored after they are validated in some way. Tenório et al. (2017)
also point out that the dissemination process evaluates the possibility of
incorporating knowledge into the organizational environment.

Sharing
process comprises the sharing of existing knowledge with individuals, groups,
teams, departments, and organizations. Thus, this phase can be carried out
immediately after the storage phase or depending on the needs (Ipe, 2003; Evans
et al., 2014). The knowledge-sharing stage poses a challenge to organizations
due to the unstructured nature of tacit knowledge and the numerous barriers
that prevent knowledge from flowing in the organizational environment
(Asrar-Ul-Haq & Anwar, 2016). In this sense, Evans et al. (2014) suggest
that a combination of technologies and tools support the right time to make
this knowledge explicit. For Tenório et al. (2017) this process has an
intimate relationship with the existing knowledge flow within the organization.

Finally,
the Use process is linked directly to the effective application of knowledge in
the organization. As explicit as knowledge may be available, at this stage, in
order to use it, tacit knowledge will be applied by the individuals. There are
many ways in which knowledge can be used, such as the creation of communities
of practice (Evans et al., 2014). Tenório et al. (2017) found out that
the use process is according to how knowledge will be applied.

4 RESULTS

The Scrum
framework consists of a set of activities such as Product Backlog, Sprint
Backlog, Increment, Sprint Planning, Development work, Daily Scrum, Sprint
Review, Sprint Retrospective, and Sprint Burndown. All these elements are part
of the KM cycle since they support all the knowledge of the organization. The
results are presented according to the activities of the Scrum method. Based on
the description presented in the previous section, an integration between KM
cycle and Scrum activities is shown in Table 2.

KM Cycle

Scrum
activities

Capture

Creation

Store

Dissemination

Sharing

Use

Product Backlog

●

●

●

●

●

Sprint Backlog

●

●

Increment

●

●

Sprint Planning

●

●

Development Work

●

●

●

Daily Scrum

●

●

●

●

●

Sprint Review

●

●

Sprint Retrospective

●

●

●

●

Sprint Burndown

●

●

Table
2.  Integration between Scrum
activities and KM processes

Source:
The authors

For the
creation of the Product Backlog artifact, a discussion of the project to be
developed is required. At this stage, the Product Owner must capture the
information in the client's environment (using printed documents, previously
used forms, monitoring routine activities in the company, etc.), get them
stored and disseminate them to the team. When discussing requirements, team
members share their experiences through information exchange and determine
which items are relevant to product development. In addition, it is at this
stage that team members determine which paths to follow. Previous experiences
help trace those paths. This way, the sharing and use of pre-existing knowledge
among individuals occur.

The
storage and dissemination of knowledge take place in the next phase, that is,
the Sprint Backlog, since that artifact is responsible for defining the list of
tasks that must be executed by the Scrum Team within a Sprint.

The
increment artifact is responsible for the sharing of the results obtained at
the end of a Sprint, and the new features that will be implemented into the
client’s environment. Therefore, this artifact shows the new
functionalities that have been captured and used.

In the
Sprint Planning event, the Product Owner will choose which Product Backlog
functionalities will be developed in the current Sprint; in this case,
knowledge dissemination takes place among team members. Thus, the sharing of
knowledge takes place within the Scrum Team, during the Sprint Backlog.

The
development work phase is intended to implement the features set out in the
Sprint Backlog. The capture, creation, and use of knowledge by the different
members of the Scrum Team take place at this stage because during this phase a
new product will be created and tested.

Daily
Scrum meetings allow all team members to know what activities are being done by
each one of members. By making this report, team members disseminate knowledge
within the whole team, providing the listener with the information that may
help them in their work, so that knowledge can be captured, created, shared and
used.

At the
Sprint Review meeting, a comparison between the delivered increment and the
goals established in the Sprint Planning, involving the dissemination and
sharing of knowledge, is performed. The Sprint Retrospective phase promotes the
dissemination of the best practices and the sharing and creation of new
knowledge, that is, the capture of the information that can be used to develop
new projects and create new knowledge. Finally, the Sprint Burndown, through a
graphic record, disseminates and shares what has already been done and what
remains to be done.

5 DISCUSSION

Following
the main question of this article which investigates how Scrum practices are
related to the KM cycle, we observed in Table 2 while Scrum is carried out, KM
cycles are directly related to Scrum activities. Thus, Figure 3 presented the
integration between the KM cycle and Scrum activities.

Software
development teams carry out knowledge-intensive activities. Therefore, they
need highly valuable knowledge (Dorairaj et al., 2012). Dingsøyr et al.
(2012) state that software development is a knowledge-building activity and
therefore knowledge management should be an attractive prospect when exploring
the creation of knowledge within software development teams, in general, and within
agile teams. Effective knowledge management can be achieved by the
implementation of diverse knowledge management processes, e.g., knowledge
creation, storage, dissemination, and application (Sungkur & Ramasawmy,
2014).

The use of
agile methodologies in software development influences team members'
communication (Dönmez et al., 2016). Generally, agile methods allow for
close interaction among team members by enabling efficient and effective
sharing of information among team members (Bannerman et al., 2012). Looking at
Figure 3, we note that Scrum provides this interaction among team members
through their activities. Also, the knowledge required for the development of
agile software is very context-dependent, and it is often difficult to transfer
context to different projects, even within the same organization (Andrej et
al., 2012). According to Sungkur and Ramasawmy (2014), Scrum activities provide
conditions for the KM cycle to take place within software development teams, as
they seek to incorporate pre-existing knowledge into the running activities,
helping in the decision-making process. When we relate the activities of Scrum
to the KM cycle, we observe a constantly learning environment, since KM
promotes that. In this sense, it is expected that the software development team
is open to change (Dingsøyr et al., 2012).

According
to Dorairaj et al. (2012), agile teams are multifunctional and should promote
the sharing of specific knowledge through frequent interactions and
collaboration with the client. This can also be seen in Figure 3. While Scrum
activities are being executed, the KM cycle is also in constant execution.
However, feedback cycles and small iterations only occur when the individuals
involved have some knowledge to share or to put into execution (Andrej et al.,
2012). Therefore, Scrum can be considered as an important KM tool, since the
activities performed enable the sharing and incorporation of knowledge to the
whole process.

Figure 3. KM Cycle related to Scrum activities

Source: The
authors

6 CONCLUSION

This
article suggested that Scrum activities can support KM cycle in order to
convert tacit knowledge to explicit knowledge within software development
teams. In this sense, we have shown how Scrum activities are related to the
processes of a KM cycle which support knowledge capture, creation, storage,
dissemination, sharing, and use. We observed that Scrum activities could be
directly associated with the KM cycles once its practices allow to incorporate
tacit knowledge into the organization, thereby increasing knowledge transfer
among individuals. Our results are means to stimulate new research in software
industry addressed to systematize the knowledge of team members to creation of
new ideas and, consequently, increase the innovation of software products.
However, we limited our exploratory research to the literature, and we did not
identify whether the KM cycle really completes itself at the software
development teams accomplishing Scrum practices in a real environment. Thus,
the challenge is to carry on in-depth research to assess our empirical results
in a practical context. Finally, we encourage Scrum researchers and
practitioners to investigate Scrum activities supported by different KM
practices and tools in order to clarify the comprising about the benefits of KM
processes for the software industry.

REFERENCES

Ahmed, A., Ahmad, S., Ehsan, N.,
Mirza, E., & Sarwar, S. Z. (2010). Agile Software Development: Impact on
Productivity and Quality. In Proceedings
of the 2010 IEEE ICMIT, Singapore.

Andrej, M., Hvizdová,
E., & Zák, S. (2012). Knowledge management as a significant
determinant of the competitive advantage sustainability. Journal of Economics. 60(10), 1041-1058.

Asrar-Ul-Haq, M. & Anwar, S. (2016). A
systematic review of knowledge management and knowledge sharing: trends,
issues, and challenges. Cogent Business & Management,
3.

Aurum, A., Daneshgar, F., Ward, J. (2008).
Investigating Knowledge Management practices in software development
organisations - an Australian experience. Information and Software Technology,
50(6), 511–533.

Bannerman, P. L., Hossain, E., & Jeffery, R.
(2012). Scrum practice mitigation of global software development coordination
challenges: A distinctive advantage? In Proceedings
of Hawaii International Conference on System Sciences.

Beck, K., Beedle, M., Bennekum, A. V.,
Cockburn, A., Cunningham, W., Fowler, M.,… Thomas, D. (2001). Manifesto
for Agile Software Development. Retrieved from http://www.agilemanifesto.org/

Binuyo, G. O., Oyebisi,
T. O.,  Olayinka, A.,  Afolabi, B. S. (2015). Evaluation of the
Factors influencing the Indigenous Software Products Development in Nigeria. International
Journal on Advances in ICT for Emerging Regions (ICTer), 7(3),
1–8.

Bjornson, F. O. & Dingsoyr, T. (2008). Knowledge management in software engineering:
a systematic review of studied concepts, findings and research methods used. Information
and Software Technology, 50(11), 1055–1068.

Brito, M. F., Figueiredo, R. C., Venson,
E., Canedo, E. D., & Ribeiro Junior, L. C. M.  (2017). Knowledge Transfer in a
Management Process for Outsourced Agile Software Development. In Proceedings of 50th Hawaii International
Conference on System Sciences.

Cámara, J., Correia, P., Lemos, R., Garlan, D., Gomes, P.,
Schmerl, B., & Ventura, R. (2016). Incorporating Architecture-Based
Self-Adaptation into an Adaptive Industrial Software System. Journal
of Systems and Software, 122, 507-523.

CEN. (2004). European Guide to good
Practice in Knowledge Management. Management Science, 50(3),
352–364.

Cervone, H. F. (2011). Understanding agile
Project management methods using Scrum. International Digital Library Perspectives,
27(1).

Chen, H., Ragsdell, G., &
O’Brien, A. (2014). Identification
of Tacit Knowledge Associated with Experience: a Chinese Software Industry
Study. In Proceedings of 15th European
Conference on Knowledge Management. Portugal.

Cristal, M., Wildt, D., &
Prikladnicki, R. (2008). Usage of SCRUM
Practices within a Global Company. In Proceedings of International Conference on Global Software Engineering.

Dalkir, K. (2011). Knowledge Management in Theory
and Practice. London: The MIT Press.

Dingsøyr, T., Nerur, S.,
Balijepally, V., & Moe, N. B. (2012). A decade of agile methodologies:
towards explaining agile software development. Journal System Software,
85(6), 1213–1221.

Dönmez, D., Grote, G., & Brusoni,
S. (2016). Routine interdependencies as a source of stability and flexibility.
A study of agile software development teams. Information and Organization,
26, 63-83.

Dorairaj,
S., Noble, J., & Malik, P. (2012). Knowledge management in distributed agile software
development. In Agile Conference (AGILE).

Dyba, T., & Dingsøyr, T. (2008). Empirical
studies of agile software development: A
systematic review. Information and Software Technology, 50, 833–859.

Eloranta, V. P. & Koskimies,
K. (2012). Aligning architecture knowledge
management with Scrum. In Proceedings of the WICSA/ECSA 2012 Companion.
Helsinki.

Eloranta, V. P., Koskimies, K., &
Mikkonen, T. (2016). Exploring ScrumBut – an empirical study of Scrum
anti-patterns. Information and Software, 74, 194-203.

Evans, M., Dalkir, K., & Bidian, C. A.
(2014). Holistic View of the Knowledge Life Cycle: The Knowledge Management
Cycle (KMC) Model. Electronic Journal of Knowledge Management,
12(2), 85-97.

Goldoni, V. & Oliveira, M. (2010).
Knowledge management metrics in software development companies in Brazil. Journal
of Knowledge Management,
14(2), 301-313.

Highsmith, J. & Consortium, C. (2002).
What is agile software development? The Journal of Defense Software Engineering,
15, 4-9.

Ipe, M. (2003). Knowledge Sharing in
Organizations: a conceptual framework. Human Resource Development Review,
2(4), 337-359.

Krogh, G. V. & Roos, J. (1995). A
perspective on knowledge, competence and strategy. Personnel Review,
24(3), 56-76.

Lei, H., Ganjeizadeh, F., Jayachandran, P.
K., Ozcan, P. (2017). A statistical analysis
of the effects of Scrum and Kanban on software development projects. Robotics
and Computer-Integrated Manufacturing, 43, 59-67.

Lyles, M. & Schwenk, C. R. (2007). Top
management, strategy and organizational knowledge structures. Journal
of Management Studies, 29(2), 155–174, https://doi.org/10.1111/j.1467-6486.1992.tb00658.x

Marçal, A., Freitas, B. C. C.,
Soares, F. S. F., & Belchior, A. D. (2007). Mapping CMMI project management
process areas to SCRUM practices. In Proceedings
of the Software Engineering Workshop.

Meihami, B. & Meihami, H. (2014).
Knowledge management a way to gain a competitive advantage in firms (evidence
of manufacturing companies). International Letters of Social and
Humanistic Sciences, 14, 80-91.

Melo, C.O., Cruzes, D. S., Kon, F., &
Conradi, R. (2013). Interpretative case studies on agile team productivity and
management. Information and Software Technology, 55(2), 412-427.

Milton, N., Shadbolt, N., Cottam, H.,
& Hammersley, M. (1999). Towards a knowledge technology for knowledge
management. International Journal Human-Computer Studies, 51, 615-641.

Moe, N. B., Dingsøyr, T., &
Dybá, T. A. (2010). A teamwork model for understanding an agile team: a
case study of a Scrum Project. Information and Software Technology,
52, 480-491.

Morampudi, N. S. & Raj, G. (2013). Evaluating
Strengths and Weaknesses of Agile Scrum Framework using Knowledge Management. International Journal of Computer
Applications, 65 (23).

Nawinna, D. P. (2011). A model of knowledge management: delivering
competitive advantage to small & medium scale software et al industry in
Sri Lanka. In Proceedings of 6th
International Conference on Industrial and Information Systems, 2011.

Nerur, S., Mahapatra, R., & Mangalaraj, G. (2005). Challenges of Migrating to Agile
Methodologies. Communications of the ACM, 48(5), 73-78.

Ortegón, A. M., Lasso, A. L.,
& Steil, A. V. (2016). Organizational Strategy and the Knowledge Management
Cycle: Bukowitz and William´s Model in Practice. Espacios, 37(7), 11.

Pinoa, F.J., Pedreira, O.,
García, F., Luaces, M. R., Piattini,
M. (2010). Using Scrum to guide the execution of software process improvement
in small organizations. The Journal of Systems and Software, 83,
1662–1677.

Rola, P.,
Kuchta, D., & Kopczyk, D. (2016). Conceptual model of working space for
Agile (Scrum) project team. Journal of
Systems and Software, 118, 49-63.

Schwaber, K. & Sutherland, J. (2016). The
Scrum GuideTM -
The definitive guide to Scrum: the rules of the game. Retrieved from
http://www.scrumguides.org/docs/scrumguide/v2016/2016-Scrum-Guide-US.pdf#zoom=100

Schwaber, K. (2004). Agile Project Management with
Scrum. EUA: Microsoft Press.

Singh, A., Singh,
K., & Sharma, N. (2014). Agile knowledge management: a survey of Indian
perceptions.  Innovations in Systems and Software Engineering, 10, 297.

Sungkur, R. & Ramasawmy, M. (2014).
Knowledge4Scrum, a novel knowledge management tool for agile distributed teams,
VINE, 44(3), 394-419.

Takpuie, D. & Tanner, M. (2016). Investigating the
Characteristics Needed by Scrum Team Members to Successfully Transfer Tacit
Knowledge During Agile Software Projects. The
Electronic Journal Information Systems Evaluation, 19(1), 36-54.

Tenório, N., Pinto, D., Vidotti, A.
F., Santos, M. O., Urbano, G. C., & Bortolozzi, F. (2017). Tool Based on
Knowledge Management Process: An Interview Protocol to Gather Functional
Requirements from Software Industry Experts. Matter: International Journal of Science and Technology, 3(1),
45-54.

Vlaanderen, K., Jansen, S., Brinkkemper,
S., & Jaspers, E. (2011). The agile requirements refinery: applying SCRUM
principles to software product management. Information and Software Technology,
53.

Wiig, K. M. (1997). Knowledge management:
an introduction and perspective. Journal of Knowledge Management,
6–14.

Zhang, X. & Dorn, B. (2011). Agile Practices in a Small-Scale,
Time-Intensive web development Project. In Eight International
Conference on Information Technology, 2011.