Categorical Classification of Factors Effecting Knowledge

Management in Software Crowdsourcing: Hypothetical Framework

Nasir Hussain

College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics (NUAA),

Jiangjun Road no.29, Jiangning District, Jiangsu Province, Nanjing, China

Keywords: Knowledge Management, Software Crowdsourcing, Success Factors, Failure Factors, Quantitative Research.

Abstract: Within Software Crowdsourcing, Knowledge Management has a great significance in both academia and

industry as a valuable tool used to manage knowledge from the crowd. The aim of this research is to ascertain

which success are and which are failure factors of Knowledge Management in Software Crowdsourcing.

Literature review techniques and Quantitative Research techniques were applied in order to establish the

success and failure factors. By utilizing the literature review method a total of twelve success factors were

established of which seven is supported. Eight failure factures were established out of which six are supported.

Subsequent to the analysis, a framework is presented in which the factors are further linked to the

implementation of Knowledge Management in Software Crowdsourcing. This research and its suggested

framework will also prove useful for academics to further gain a comprehensive view of Knowledge

Management factors in Software Crowdsourcing for use in future studies.

1 INTRODUCTION

Crowdsourcing is a tool used by small and medium

enterprises (SMEs) (Khan et al, 2019; Brabham,

2008). Crowdsourcing software development is

different to other platforms. Before any development

starts, organizations would need to first predetermine

their needs and requirements and how this will affect

crowd membership. Software crowdsource-ing is a

web based model that brings together solutions from

a distributed network of individuals. How states that

crowdsourcing represents the process in which an

organization takes a process that was previously

performed by its employees and giving it to a large

network of individuals in order to collaborate.

Organizations would post a problem online to which

a large number of individuals with different ideas on

how to solve the problem will respond. A reward

could be given to the individual with the best idea,

after which the organization can then market it as

their own (Brabham 2008).

Knowledge Management within Software

Crowdsourcing is a challenge to manage. Knowledge

Management has undergone a systematic

development process over the first years of the

twenty-first

century and has been shown to be a much

discussed topic within numerous different industry

groups(Aurum et al, 2008: Khan et al, 2016). The

skills utilized in order to achieve knowledge is

progressing more rapidly and is in line with today’s

knowledge driven economy.

However there are many Knowledge

Management tools available on how to manage

knowledge from within the crowd. We will be

introducing a technique that will enable the

management of knowledge in Software

Crowdsourcing (Alan Frost, 2014). We’ve placed

our focus on Knowledge Management factors in

Software Crowdsourcing as well as to study and test

for any other influences that may the success and

failure factors in Knowledge Management in

Software Crowdsourcing (Alavi and Leidner, 2001).

Knowledge Management is faced with a difficult

situation as to how to manage knowledge from

within the crowd as there are so many different

frameworks. These frameworks are however mostly

developed for the needs of large international

companies (Ergazakis et al, 2006). Therefore the

same needs still exist specifically in using software

in crowdsourcing. This research is therefore aimed

directly towards a Knowledge Management system

specifically focused on Software Crowdsourcing in

line with the needs and necessities required to handle,

regulate the collection and storing as well as the

sharing of information (Akhavan and Fathian 2006).

Based on the discussion we have developed the

following research questions.

578

Hussain, N.

Categorical Classiﬁcation of Factors Effecting Knowledge Management in Software Crowdsourcing: Hypothetical Framework.

DOI: 10.5220/0007811005780585

In Proceedings of the 14th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2019), pages 578-585

ISBN: 978-989-758-375-9

RQ1: Using literature and empirical study, which

factors are identified that would either have a positive

or negative impact on Knowledge Management

within Software Crowdsourcing?

RQ2: What would be the likely framework for the

factors to have either a positive or negative outcome

on Knowledge Management in Software

Crowdsourcing?

RQ3: What would be the most suitable framework

to be introduced within a successful Knowledge

Management in Software Crowdsourcing platform?

2 BACKGROUND STUDY

Knowledge Management is a valuable component

within Software Crowdsourcing platforms. It takes

individual’s knowledge and transforms it into

hierarchical information where it can be shared with

others in the group. In turn other individuals in the

group can then collaborate and add to it (Wong &

Aspinwall, 2006). Although Knowledge

Management has been extensively researched, there

is no single all-fitting method. There is also a variety

of systems, tools and methods.

Many researches have come up with different

views on Knowledge Management (Khan et al,

2018). One of which states that it is the gathering of

techniques view information through association

with end goals to assess its feasibility and

achievement.

Moreover it has characterized Knowledge

Management as the act of specifically utilizing

learning from past encounters with basic leadership.

This is in line with current and future basic leadership

exercises which is specifically the required

motivation behind enhancing the organization.

Knowledge is a kind of stream that can exchange

knowledge between the knowledge provider and the

knowledge demander (Wong and Aspinwall, 2006).

Knowledge Management is getting the correct

information to the correct individuals at the right time

so they can settle on the best choices.

In conclusion, all Knowledge Management

methodologies focuses on the fact that it’s an

important asset and that knowledge should be

presented to users timeously. Therefore it is a process

that assists organizations to capture, select, organize,

distribute and transfer significant information in

order to gain a business advantage.

In this paper we discuss the primary steps towards

developing the proposed framework (Knowledge

Management in Software Crowdsourcing). We

discuss the success and failure factors that could have

a positive or negative impact on Knowledge

Management in Software Crowdsourcing. These

factors will assist in the development of the factors

component of Knowledge Management in Software

Crowdsourcing.

The reported success and failure factors will assist

with the theory of Knowledge Management in

Software Crowdsourcing.

3 RESEAR CH METHODOLOGY

This research study will be performed by utilizing

literature review and has been used as a prelude to the

research report and proposal. A good review can

extract new ideas from others’ work by combining

and summarizing previous sources (Brettle and

Gambling, 2003). On top of that, new theories can be

built from the evidence discussed and new directions

for future research can be suggested. It can also be

used to facilitate evidence used in daily practice, by

supplying answers to clinical questions (Khan et al,

2017; Brettle and Gambling, 2003). Literature

reviews are also ideally used within publishing

through initial research questions including access to

a literature databases and some basic evaluation and

writing skills.

4 PROPOSED FRAMEWORK

The focus of this research is to identify success and

failure factors in Knowledge Management in

Software Crowdsourcing. The contextual factor was

also added to the framework to test the sensitivity of

organizations of different sizes. The proposed

framework

groups the variables based on whether

they are success or failure factors in Knowledge

Management in Software Crowdsourcing. Below is a

brief discussion thereof.

4.1 Success Factors

The following success factors categorized below

includes the variables which produces positives

within Knowledge Management in Software

Crowdsourcing.

4.1.1 Organizational Culture

Organizational culture is a bottoms-up process. It

starts with the junior staff and works its way up to the

top of the organization.

Categorical Classiﬁcation of Factors Effecting Knowledge Management in Software Crowdsourcing: Hypothetical Framework

579

a. Crowd Involvement

Organizational culture is very important to the

successful implementation of activities within

Knowledge Management. Lack of trust amongst co-

workers will block their ability to group together

during discussions. The greater the trust, the more

likeliness there is of openness in discussions that will

lead to data being shared more willingly (Khan et al,

2017). Making errors is a part of learning, individuals

should not be punished when applying new

information. Focusing on errors will deter them from

collaborating with others as there will be a sense of

disappointment.

H1: Crowd Involvement has a positive impact on

Knowledge Management in Software

Crowdsourcing.

b. Cultural Norms and Values

Every culture has its own styles, values and norms

which can potentially cause difficulty in people from

diverse cultural backgrounds when they try to

communicate with each other. (Holmstrom et al.,

2006) Between companies, working norms can also

differ to some extent.

H2: Culture Norms and Values has a positive

impact on Knowledge Management in Software

Crowdsourcing.

c. Cultural Awareness

Cultural awareness involves teams recognizing their

difference in opinion during for example software

development. By adjusting their ways of thinking and

responding differently they can promote mutual

understanding instead (Winkler et al., 2008).

Interpretation can be further promoted by team

members having a sense of cultural awareness. An

example hereof, (Imsland et al.,2003) teams from

Russia and Norway were collaborating using English.

A misunderstanding took place due to diverse

communication style and national cultural

backgrounds within each of the two teams. The

Norwegians would state their intentions explicitly

without much dependence on the context. The

Russians however communicate their intentions

indirect without it being clearly stated. Non-verbal

communication is used mostly in high-context

cultures such as Anglo and Northern European

cultures (Khan et al, 2016). It can therefore be noted

that cultural awareness plays an important part in

Knowledge Management in Software Crowdsourcing.

H3: Culture Awareness has a positive impact on

Knowledge Management in Software Crowdsourcing.

d. Mutual Understanding

Mutual understanding is best achieved through

overcoming cultural, social and political differences

(Espinosa et al., 2007). The level of understanding is

improved between team members through public

relation and in-person meetings. However this could

be challenging for globally distributed teams due to

possible cost saving strategies. Mutual understanding

can be effective if miscommunication and

misinterpretation is avoided. (Azeem and Khan,

2011). Factors that will assist with mutual

understanding is socio-cultural fit, language skills,

conflict handling, cognitive based trust and regularity

in the work place (Jimnez et al., 2009).

H4: Mutual Understanding has a positive impact

on Knowledge Management in Software

Crowdsourcing.

4.1.2 Knowledge Resources

Knowledge resources within an organization could

be realistically tapped into by that organization. It

can therefore reside within individuals and groups, or

exist at an organizational level.

a. Human Capital

Human capital refers to information held by owner-

managers and staff in relation to organizational

knowledge, ideas and skills. The more training is

provided, individuals will be more likely to continue

contributing. Organization information, knowledge

and experience is collated over the years and is

therefore company assets that have developed over

time (Alan Frost, 2014).

H5: Human Capital has a positive impact on

Knowledge Management in Software Crowdsourcing.

b. Knowledge Capital

Knowledge capital is the current information that an

organization has which can be in the form of reports,

records or data which can either be manually filed or

located within a system. Most SMEs do not have

sufficient advanced ICT systems due to constraints.

All information should be kept up to date and staff

should have access to it (Cheng Sheng Lee, 2015).

H6: Knowledge Capital has a positive impact on

Knowledge Management in Software Crowdsourcing.

4.1.3 Knowledge Process

Knowledge process is the allocation of relatively

high-level tasks to an outside organization or a

different group possibly in a different geographic

ENASE 2019 - 14th International Conference on Evaluation of Novel Approaches to Software Engineering

580

location within the same organization.

a. Knowledge Acquisition

Knowledge acquisition activities can be anything

from sending employees on training courses (Cheng

Sheng Lee, 2015) to the attendance of seminars and

exhibitions where knowledge is gathered. Due to the

relaxed organizational culture in SMEs, employees

are able to interact with managers and peers more

easily. Knowledge can also be gained from

customers and suppliers.

H7: Knowledge Acquisition has a positive impact

on Knowledge Management in Software

Crowdsourcing.

b. Knowledge Creation and Generation

Knowledge creation is when new knowledge and

ideas have been generated. During the process of

employees working together, knowledge is created as

it facilitates teamwork and thereby the generation of

more conversation amongst employees (Bergman et

al, 2004; Alan Frost, 2014). Another way for a group

to generate ideas is through brainstorming sessions.

Group discussions stimulates conversation and it is

therefore a preferred interaction method.

H8: Knowledge Creation and Generation has a

positive impact on Knowledge Management in

Software Crowdsourcing.

c. Knowledge Transferring and Sharing

Informal social interaction is mostly used in the

sharing of knowledge (Cheng Sheng Lee, 2015).

Knowledge can also be shared during meetings

where employees hold discussions and thereby share

their ideas and knowledge (Bergman et al, 2004). In

SMEs this process may be of a more informal nature

as in relation to larger organizations. Meetings are an

important way to discuss any current projects

including deadlines that are to be met.

H9: Knowledge Transferring and Sharing has a

positive impact on Knowledge Management in

Software Crowdsourcing.

4.1.4 Organizational Factors

Organizational factors associated with the

occurrence and persistence of operational successes

and failures.

a. Management Leadership and Support

Management in Knowledge Management roles play

a most important part in order to initiate and drive

successful Knowledge Management implementation,

including keeping employees involved in the process

(Cheng Sheng Lee, 2015). Commitment is

encouraged as a way to introduce a system and

environment for Knowledge Management activities

(Khan et al, 2017). Motivation by top management

can be used as a way to establish commitment and

therefore promote a way to keep employees to take

part in even more in Knowledge Management

activities. Motivation can either be in the form of

money or some non-monetary item. This will inspire

employees to keep contributing information.

H10: Management Leadership and Support has a

positive impact on Knowledge Management in

Software Crowdsourcing.

b. Organizational Infrastructure

ICT infrastructure is an important factor in

Knowledge Management within organizations.

Within some organizations there are rarely advanced

ICT systems nor document management systems.

Most of these only have basic infrastructure such as

Internet and Intranet as a way to transfer information.

In some organizations they may still only use

physical filing storage in filing rooms. Venues that

would assists employees to interact and discuss

should be provided (Cheng Sheng Lee, 2015;

Cumming, 2004).

H11: Organizational Infrastructure has a positive

impact on Knowledge Management in Software

Crowdsourcing.

c. Organizational Strategy

Successful Knowledge Management implementation

is a key strategy within an organizations’ overall

business strategy and a key factor for their long-term

competitiveness and success. Owner-managers are

usually responsible for this (Khan et al, 2016; Cheng

Sheng Lee, 2015). An organization would need to

predefine its needs and requirements prior to

considering its Knowledge Management strategy. A

clear strategy will provide focus and direction for

everyone in the organization (Chang Sheng Lee,

2015). Without this, the required result will not be

reachable and resources would have been wasted.

H12: Organizational Strategy has a positive

impact on Knowledge Management in Software

Crowdsourcing.

4.2 Failure Factors

The following failure factors categorized below

includes the variables which produces negatives

within Knowledge Management in Software

Crowdsourcing.

Categorical Classiﬁcation of Factors Effecting Knowledge Management in Software Crowdsourcing: Hypothetical Framework

581

4.2.1 Technology

The practical application of knowledge especially in

the particular area of engineering based on how to use

technology.

a. Lack of Advanced Technological Tools

and Techniques

Due to the ever changing technical advancements

and enhancements in areas such as computers,

networks, television, fiber optics, etc., we have to

adapt the way we interconnect, play and do business

as it changes rapidly. This shift in technology means

that organizations have an increased need to accept

and adjust to the need created in the field of IT

advancements. As the information age further

develops we are faced with larger sources of

information and organizations therefore need to

mechanize themselves (Frost, 2014). Technology is

therefore a key factor within Knowledge

Management as a success factor and as a potential

failure factor.

H13: Lack of Advance Technological Tools and

Techniques has a negative impact on Knowledge

Management in Software Crowdsourcing.

b. Lack of Controlling Knowledge

Management Activities

Knowledge Management has revealed a more often

psychological trend as supposed to a technical view

(Frost, 2014). Thus knowledge has more to do with

individuals, else it is only data or information.

Knowledge can therefore be viewed as an activity

whereas data and information are objects.

H14: Lack of Controlling Knowledge

Management Activities has a negative impact on

Knowledge Management in Software Crowdsourcing.

4.2.2 Planning

A basic management function involving formulation

of one or more detailed plans to achieve optimum

balance of needs or demands within the available

resources.

a. Poor Planning and Design

Successful Knowledge Management implementation

depends upon the integration of many different

aspects of an organization (Frost, 2014). Indicates

that "Knowledge Management provides a strategy

and organizational discipline for the integration of

people, processes and IT into an effective enterprise."

Proper planning and continuous evaluation are

needed to ensure that all aspects of Knowledge

Management are being implemented effectively and

work well together. Moreover, the implementation of

Knowledge Management needs to be focused on the

organization’s strategic business objectives and

critical business problems (Khan et al, 2017). In other

words, the implementation of Knowledge

Management requires a long-term practical outlook.

H15: Poor Planning and Design has a negative

impact on Knowledge Management in Software

Crowdsourcing.

b. Poor Coordination and Evaluation

There is a positive relationship between operation

control and continuous evaluation of a successful

knowledge management project as per Danesh (Alan

Frost, 2014). There needs to be a continuous facility

in order to evaluate progress and to track the

effectiveness of the program. There needs to be a

continuous facility in order to evaluate progress and

to track the effectiveness of the program. There needs

to be proper performance indicators in place in order

to evaluate progress made, else it will be a challenge

for management to evaluate the effectiveness of the

program. In addition there could also be a

shortcoming in the approach followed during the

implementation (Khan et al, 2017; Frost, 2014).

H16: Poor Coordination and Evaluation has a

negative impact on Knowledge Management in

Software Crowdsourcing.

4.2.3 Skills

The attributes required to perform a job which is

generally demonstrated through qualifying service,

education or training.

a. Inadequate Skills of Knowledge

Managers

Knowledge managers and workers fulfil the entire

spectrum of Knowledge Management related

positions, and may include such titles/roles as Chief

Knowledge Officer (CKO), knowledge broker

(Frost, 2014), knowledge analyst, knowledge

systems engineer (Civilian Career Path Guide), etc.

The skills required of knowledge managers and

workers can be broken down into the following broad

categories (KM Skills Map) (Khan et al, 2017). The

skill requirements for a knowledge manager/worker

could change depending on the areas of

responsibility. For instance, a CKO would require

very strong strategic and business skills, as well as

management, learning, and communication (KM

Skills Map). The CKO would not need to be as strong

in IT skills as for example, a systems engineer in

ENASE 2019 - 14th International Conference on Evaluation of Novel Approaches to Software Engineering

582

charge of developing a Knowledge Management

system (Frost, 2014).

H17: Inadequate Skills of Knowledge Managers

has a negative impact on Knowledge Management in

Software Crowdsourcing.

b. Inadequate Skills of Team Members

Lack of availability of relevant skills: The right

business and technical skills must be present to

sustain the project. Skills can also be developed

through training, provided that it is implemented with

clear, measurable goals (Frost, 2014).

Improper selection of knowledge managers. i.e.

the skills must correspond to the role that the

individual knowledge manager or worker will play

within the Knowledge Management initiative (Khan

et al, 2017; Frost, 2014), stress the importance of the

middle manager, highlighting three key qualities:

analytic, intuitive and pragmatic.

H18: Inadequate Skills of Team Members has a

negative impact on Knowledge Management in

Software Crowdsourcing.

4.2.4 Responsibility

This shows the expense at the lowest level of work

for the purpose of managing cost and duration. It is a

charting system that illustrates the task’s goals and

the required action by each person.

a. Lack of Responsibility

Control of shared resources are part of managerial

responsibilities according to Weber. There is a need

for central management responsibilities according to

Pettersson. For Hasanali (Frost, 2014), he views the

need of stewards throughout the organization to be

working below the central Knowledge Management

team as well as the importance of structure and

responsibility to assist with accountability (Bergman

et al, 2004; Frost, 2014).

H19: Lack of Responsibility has a negative

impact on Knowledge Management in Software

Crowdsourcing.

b. Lack of Ownership

Ownership is the classification of when one owns up

to ones mistakes as well as willingly taking up tasks.

Not willingly taking up responsibility is failure of

knowledge management (Frost, 2014), which is likely

because of organizational culture. Culture could

cause a reluctance to admit to ones mistakes due to

the fear of possible consequences.

H20: Lack of Ownership has a negative impact on

Knowledge Management in Software Crowdsourcing.

The Framework of this research is shown below

in Figure 1.

Figure 1: Proposed Framework.

Categorical Classiﬁcation of Factors Effecting Knowledge Management in Software Crowdsourcing: Hypothetical Framework

583

5 CONCLUSION

This research places its focus on developing a

framework for Knowledge Management within

Software Crowdsourcing and it is based on critical

success factors and critical failure factors. Literature

review was performed after which a research

questionnaire was sent to IT professionals in the field.

After all the results were obtained, data analyses was

performed by utilizing SPSS version 19. It was found

that a total of twelve success factors were established

out of which seven is supported. Eight failure factures

were established out of which six is supported. In

conclusion, a framework is presented in which the

factors are further linked to the implementation of

Knowledge Management in Software

Crowdsourcing.

6 FUTURE WORK

During future research, the focus should be placed on

conducting an empirical study in order to produce

factors that will have an effect on Knowledge

Management within Software Crowdsourcing

implementation. This research as well as the

framework it suggests will prove valuable for

academics to further gain a comprehensive view of

the factors that have an influence in Knowledge

Management in Software Crowdsourcing.

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