Effects of Geographical, Socio-cultural and Temporal Distances
on Communication in Global Software Development
during Requirements Change Management
A Pilot Study
Arif Ali Khan, Jacky Keung, Shahid Hussain
and Kwabena Ebo Bennin
Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong
Keywords: Geographical, Socio-cultural, Temporal, Communication, Framework, Global Software Development,
Requirements Change Management.
Abstract: Trend of software development is changing rapidly most of the software development organizations are
trying to globalize their activities throughout the world. This trend leads towards a phenomenon called
Global Software Development (GSD).The main reason behind the software globalization is its various
benefits. Besides these benefits, software organizations are facing various challenges. One of these
challenges is communication which is considered a big challenge in GSD and it becomes more complicated
during the Requirements Change Management (RCM) process due to three factors, they are Geographical,
Socio-cultural and Temporal distances. This paper presents a framework which shows the effect of these
factors on communication during RCM process in GSD. Communication is the core function of
collaboration which allows information to be exchanged between the team members. A pilot study has been
conducted in three GSD organizations. A quantitative research method has been used to collect data. The
findings from the survey data show that these three factors have a strong negative impact on communication
process in GSD.
1 INTRODUCTION
Global Software Development (GSD) or Global
Software Engineering (GSE) is the development of
software projects consisting of teams working
together to accomplish project goals from various
geographical locations (Biffl and Halling, 2003;
Helena et al., 2006; Šmite et al., 2008). About a
decade ago, different experiments have done in
order to develop the software projects at
geographically distributed locations. Most of the
organizations try to find solutions around the world
and GSD appears to be a good option in such an
environment (Rafael et al., 2006). The acceptance of
the GSD process is to get the business benefits and
competitive advantages. Software companies are
distributing their work globally in order to get
benefits such as low cost, good productivity, access
to skilled work forces and access to market (Khan et
al., 2012).
Along with benefits, most of the studies have
discussed some issues related to the distribution of
work and the constraints associated with it (Helena
et al., 2006; Khan et al., 2012; Khan et al., 2011). In
these studies, constraints such as geographical
distance, socio-cultural distance and temporal
distance are recognized, and they indeed increase
the scope of an organizational operation (Sahay,
2003), but there is a little doubt that these
constraints challenge communication, coordination
and control in GSD (Da Silva et al., 2010; Herbsleb
and Mockus, 2003).
In GSD, requirements continuously change
during the software development life cycle. It is very
difficult to manage the changed requirements due to
certain communication and coordination challenges.
Communication is one of the major issues during
requirements change management in GSD due to
geographical, socio-cultural and temporal distances
(Casey and Richardson, 2008; Huang and Trauth,
2007).
The objective of this study is to identify the
negative effect of factors geographical distance,
socio-cultural distance and temporal distance on
159
Khan A., Keung J., Hussain S. and Ebo Bennin K..
Effects of Geographical, Socio-cultural and Temporal Distances on Communication in Global Software Development during Requirements Change
Management - A Pilot Study.
DOI: 10.5220/0005331401590168
In Proceedings of the 10th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE-2015), pages 159-168
ISBN: 978-989-758-100-7
Copyright
c
2015 SCITEPRESS (Science and Technology Publications, Lda.)
communication. During literature review it is
observed that no study has been done to explore the
effect of factors on communication during RCM
process. In this study a self-questionnaire pilot
survey in the Pakistani GSD industry is conducted.
Based on survey in three GSD organizations, the
effect of geographical, socio-cultural and temporal
distances on communication is explored in this
study.
2 BACKGROUND STUDY
Software development is generally defined as “any
software development lifecycle activity”
(Holmstrom et al., 2006). The term “activity” refers
to a human action taken specifically for a purpose
either individually or collectively at any stage during
the production of software life cycle. A
“development activity” refers to either separate or
combined action that brings a meaningful change in
software system’s lifecycle (Cottmeyer, 2008;
Herbsleb, 2007). The development of a software
system may involve multiple teams coordinating at
various degrees in order to produce the resultant
product. Based on the involvement of development
teams, the software development is divided into
types of Collocated and Global Software
Development (GSD) (Conchuir, 2009). In
Collocated software development the development
activities take place on single site. In this type of
software development, the single sites do not
distribute the development activities on multiple
sites (Damian, 2007). However the development
process GSD OR Geographically Distributed
Software Development is restricted by geographical,
cultural and temporal limits (Smite et al., 2008). In
geographically distributed software development,
missions are accomplished by the joint efforts of the
group of people that belong to different geographical
locations. Software development companies are
looking forward to GSD because of its well-
recognized and known benefits that comprise low
cost, high ratio of productivity, access to skilful
work force and access to market etc. (Herbsleb and
Mockus, 2003).
However, GSD also faces different challenges
(Da Silva et al., 2010 ; Herbsleb and Mockus, 2003).
Some of the researchers found the poor
communication is the key issue that cause global
software projects to fail (Bass et al., 2009; Herbsleb,
2007). Communication is the core function used to
exchange the information between the team
members (Shujian, 2012). In GSD the
communication challenge generally occur when
team members are geographically, socio-culturally
and temporally apart from each other (Hofner and
Mani, 2007; Korkala and Abrahamsson, 2007).
The key objective of this research work is to
observe the negative effects of geographical, socio-
cultural and temporal distances on communication
process in the context of GSD. For this purpose a
framework as shown in Figure.1 is proposed that
could explain the effect of the above three factors on
communication. The proposed framework is
empirically evaluated in GSD industry using the
questionnaire survey approach.
The major terms used in this research study is
explained in the following sections:
2.1 Requirements Change
Management
The software product requirements in software
engineering are considered to be fixed which gives a
wrong idea to the management team and make them
to stop the requirements before the project is
initiated (Zhu et al., 2008). Contrary to this
traditional principle software, requirements
continuously change from requirements gathering to
the maintenance phase of development life cycle.
The factors like change in user needs, government
policies and technologies cause the requirements to
change (Ramzan and Ikram, 2006). According to
(Zhu et al., 2008), change in requirements behaves
as a main driver for software maintenance and re-
engineering activities.
The process used to manage those changes is
called Requirements Change Management (RCM)
and is one of the most thoughtful happening which
produces many problems in distributed environment
(Hussain and Clear, 2012). Requirements Change
Management (RCM) is a process which decides
whether a requested change should be implemented
or not. It faces hindrances when performed globally
due to different challenges faces by communication
process (Hussain and Clear, 2012).
These challenges are mainly distributed as
geographical distance, socio-cultural distance and
temporal distance. These factors make
communication a major issue during RCM process
(Casey and Richardson, 2008; Huang and Trauth,
2007; Moe and Šmite, 2008).
The main reason to focus on RCM is that
requirements change during all phases of software
development life cycle and various communication
issues make it even harder to manage (Hussain and
Clear, 2012). As discussed before, requirements
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change from start to the end of development life
cycle and the management of these requirements is
crucial job in GSD (Ramzan and Ikram, 2006).
Therefore it’s become important to address the
communication issue during RCM process.
In this article a framework has been propose to
exhibit the effect of geographical, socio-cultural and
temporal distances on communication during RCM.
The proposed framework is based on the available
literature that has been categorized into geographical
distance, socio-cultural distance and temporal
distance as shown in Figure.1.
2.2 Geographical Distance
Geographical distance is the effort required for one
team member to visit another and it shows the
physical separation between the system development
team members (Holmström et al., 2006).
Geographical distance causes hindrance during
communication of one team member with another
(Holmstrom et al., 2006). Two sites within the same
country with regular flights can be considered close
even if separated by a huge distance, but different
sites which have little transportation and perhaps
intervening borders cannot be geographically close.
Furthermore, even two actors within the same
building but separated by long corridors and many
levels will be impacted by geographical distance
(Agerfalk et al., 2005).
Geographical distance has a direct effect on
communication in GSD. When the geographical
distance increases communication decreases and a
huge geographical distance leads to
miscommunication ((Da Silva et al., 2010). So,
based on the relationship among geographical
distance and communication, we propose the
following hypothesis.
H1: Geographical distance has a negative effect on
communication.
2.3 Socio-cultural Distance
In GSD, socio-cultural distance is a measure of an
actor understanding of another actor’s values and
normative practices (Winkler et al., 2008). Culture
can have a strong effect on how people discuss
certain issues, and how they react to them (Winkler
et al., 2008). Cultural distance involves national
culture, organizational background, language,
policies, and moral principles (Helena et al., 2006)
In GSD, people from different national and
organizational backgrounds are involved which may
lead to misunderstanding and miscommunication
(Conchuir, 2009). Generally socio-cultural distance
relates to the geographical distance between the
actors. Due to the small geographical distance the
actors may also experience cultural distance which
can negatively affect the communication process
(Damian, 2006). Based on the above discussion we
propose the following hypothesis
H2: Socio-cultural distance has a negative effect on
communication.
2.4 Temporal Distance
Temporal distance is the measure of the time
difference experienced by two actors wishing to
communicate (Holmstrom et al., 2006). Temporal
distance is the result of different factors including
two actors located at two different time zones
((Agerfalk et al., 2005). Geographical distance
causes the temporal distance among the different
actors who want to communicate. Temporal distance
interrupts the communication process among team
members due to less time overlapping (Shahzad,
2011). Due to the relationship between temporal
distance and communication, we propose the
following hypothesis
H3: Temporal distance has a negative effect on
communication.
Figure 1: Proposed Framework for Factors Affecting
Communication in GSD.
3 RESEARCH METHODOLOGY
In this study the influence of geographical, socio-
cultural and temporal distances on communication in
GSD during RCM have been investigated. The
survey questionnaire used for this research focused
on the following information:
Demographic profile (in terms of respondent
gender, position, education, experience,
organization and nature of projects developed).
Effect of geographical, socio-cultural and
temporal distances on communication during
RCM in GSD.
EffectsofGeographical,Socio-culturalandTemporalDistancesonCommunicationinGlobalSoftwareDevelopment
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The steps used for this research study were a
background study, survey instrument development,
conducting of a survey and data analysis (Liao and
Shi, 2009). The objectives were based on existing
literature discussed in section 2. For survey study the
questionnaire was designed and the piloting was
performed in order to refine the survey instrument.
The survey questionnaire sample is exhibited on the
given link (http://goo.gl/forms/e7f8NfBpdV). The
questionnaire was distributed among team members
of three GSD organizations in Islamabad (Pakistan)
and data was collected using a self-administrated
questioner (Gunasekaran and Ngai, 2008). These
three organizations were randomly selected from the
website of Pakistan Software Export Board
(www.pseb.org.pk). The selected GSD organizations
were visited with an approval letter declaring the
objectives of our research for conducting the survey.
A total of 53 responses were collected, 12 responses
were incomplete and other 41 responses were
analyzed. For the data analysis, statistical package
for social sciences (SPSS version-19) is used.
4 RESULTS
4.1 Demographic Profile of
Respondents
Brislin in (Brislin et al., 1973) discussed the
significance of detailed information on a sample of
descriptive statistics, in which the information could
provide close view of respondents and companies
which are deemed to interpret more significant
results.
From the selected three organizations a total of
53 responses were collected in which 41 responses
were usable. The respondents shown in Table 1
include 65.8% male and 34.2% female.
Table 1 also discussed the job position and
education level of the respondents. According to the
analysis most of the respondents were developers
followed by analysts with 36.5% and 19.5%
respectively. Other job positions were designer,
team manager, tester and CEO representing 14.8%,
12.3%, 7.3% and 4.9% of overall respondents
respectively.
The knowledge of the respondents can be
determined by analyzing their education level. In
this research out of 41 respondents, 23 have bachelor
degree which presents 56.1%; subsequently 12 and 5
respondents have diploma and postgraduate,
presenting 29.2% and 12.2% respectively. Just one
respondent has been a high school certificate holder,
representing 2.5% as shown in Table 1. Results from
the analysis show that the respondents were well
educated and well positioned. This shows the
significance of the collected survey data.
The context of this research is GSD industry. It
is important to investigate the working experience of
employees in GSD. In this study the highest working
experience in GSD industries ranged from 1 to 5
years representing 75.6% and the second highest
value is less than year representing 12.1%. The
remaining respondents have working experience
ranged from 6 to 10 years and 11 to 15 years
presenting 9.8% and 2.5% respectively. The overall
statistics show that most of the respondents have
experience in the range from 1-5. This shows that
generally the respondents were well experienced in
the GSD industry.
Table 1: Summary on Respondent‘s Demographics.
Respondents Frequency %
Gender
Male 27 65.8
Female 14 34.2
Total 41 100.0
Position
Developer 15 36.5
Analyst 8 19.5
Designer 6 14.8
Team Manager 7 12.3
Tester 3 7.3
CEO 2 4.9
Total 41 100.0
Education
Bachelor 23 56.1
Diploma 12 29.2
Postgraduate 5 12.2
High School 1 2.5
Total 41 100.0
Working
Experience
1-5 31 75.6
Less than year 5 12.1
6-10 4 9.8
11-15 1 2.5
Total 41 100.0
4.2 Organizations Profile
It is important to inspect the complete background of
companies where research survey was conducted
(Rea and Parker, 2012). It is vital to investigate the
geographical nature of selected GSD organizations.
In this study the GSD industries were the focus
of research. For confidential reasons we are not
permitted to discuss the names of the selected
organizations. The selected three organizations are
tagged as Companies X,Y,Z.
Company X is a leading IT distributer company
provding end to end business solutions to the
enterprise and mid market sector.The main office of
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Table 2: Organizations Profile.
Company X
Distributed Sites 3
Development Methodology Agile
Type of Global Software Development
Offshore
Insourcing
Time difference between Pakistan and UAE 1 hour
Time difference between India and UAE 1.5 hours
Time difference between India and Pakistan ½ Hour
National languages Arabic, Hindi, Urdu
Company Y
Distributed Sites 2
Development Methodology Agile
Type of Global Software Development Offshore Outsourcing
Time difference between Pakistan and US 12 hours
National languages English, Urdu
Company Z
Distributed Sites 2
Development Methodology Scrum
Type of Global Software Development Offshore Outsourcing
Time difference between Pakistan and Denmark 8 Hours
National languages Danish, Urdu
company A is located in UAE. The two site
branches are in India and Pakistan. The company is
working in GSD environment since 2007. The
complete detail of Company X is discussed in Table
2.
Company Y is US based distributed software
development organization and working on system
development for law firms, management and
marketing firms and information technology
consultants since 2006. Company Y consist of US
headquarter office and other site branch located in
Pakistan. Table 2 gives the thorough detail of
Company Y.
The headquarter of Company Z is located in
Denmark and started distributed software
development for the last six years. The other branch
of Company C is in Pakistan. The main focus of
Company C is on digital signage systems. Table 2
listed the distributed characteristics of Company C.
4.3 Reliability Analysis of Survey Data
Reliability analysis technique is used to check the
reliability of the data collected from respondents.
According to (Ahire et al., 2007) there are four
methods for testing the reliability, namely the test-
retest method, alternative form method, split-halves
method, and the internal consistency method. The
internal consistency method is utilized in this study
for estimating the reliability between the observed
variables of interest. The internal consistency
method is chosen because it requires only one
administration of the survey instrument and
is commonly used in empirical research. It shows the
internal consistency of data items as a group and is
used in various forms to test empirical data (Santos,
1999). In this research the Cronbach Alpha test has
been used to analyze the reliability of the data
(Santos, 1999). According to (Joreskog et al., 1989),
0.70 is an acceptable Alpha reliability value. Hence
Alpha reliability was set to 0.70 as an acceptable
reliability. The results of the Alpha Reliability are
shown in Table 3.
The first variable was the geographical distance
having four items and the reliability score 0.766
Alpha. The second variable was the socio-cultural
distance with four items and the Alpha score was
0.777. The last independent variable was the
temporal distance having four items and the Alpha
score was 0.823. Alpha value for dependent variable
(communication) is 0.783. The alpha values of the
variables was greater (>0.70) (Joreskog et al., 1989),
so all of these variables were reliable for the data
analysis.
Table 3: Reliability Analysis.
Variables Number of Items
Cronbach’s
Alpha
Geographical
Distance
4 0.766
Socio-Cultural
Distance
4 0.777
Temporal
Distance
4 0.823
Communication 3 0.783
4.4 Hypothesis Testing
In this section we have shown the hypotheses results
EffectsofGeographical,Socio-culturalandTemporalDistancesonCommunicationinGlobalSoftwareDevelopment
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and also discuss the analysis of the results. A major
tools used for hypothesis testing is Regression
analysis (Weisberg, 2014). Regression analysis has
been used to analyse the relationship between at
least two variables (i.e., At least one independent
and one dependent variable) (Weisberg, 2014).
Before presenting the results of regression
analysis it is important to present the interpretation
for various correlation and regression coefficients,
based on which the strength, direction and impact of
a relationship can be determined. The values of R,
R-square and P (significance) value have been used
to analyse the results.
The value of R showed the strength of the
relationship. It ranged from +1 to -1. A value of R
which closer to ‘+1’ shows a strong positive
correlation, whereas a value of R closer to ‘0’ shows
a weaker or no correlation and the same time a value
of R below ‘0’ gives a negative correlation (Sweet
and Grace-Martin, 2011). The positive or negative
signs with the value show the direction of the
relationship among the independent and dependent
variables. For example a positive sign shows that if
one is increased then the other will also increases.
The value of R-square indicates the percentage of
variance in the dependent variable caused by the
independent variable. At the same time value of P
shows the significance of the relationship. If P-
Value is less than 0.05, then we can consider that the
relationship is significant (Sweet and Grace-Martin,
2011).
The proposed framework has been investigated
through the multiple regression analysis and the
relationship of the dependent and independent
variables has been analyzed. Based on the proposed
framework, the relationship of geographical
distance, socio-cultural distance, temporal distance
and communication has been analyzed.
Table 4: Model Summary of the Proposed Framework.
Model R
R
Square
Adjusted R
Square
Std. Error
of the
Estimate
1 0.815a 0.664 0.655 0.773
a. Predictors: (Constant), Geographical Distance,
Socio-cultural distance, Temporal Distance
The above Table 4 shows that the value of R is
0.815; this indicates a strong correlation among the
predictors (geographical distance, socio-cultural
distance, temporal distance) with communication.
The value of R Square shows the variance in the
dependent variable (Communication) which can be
predicted by independent variables (Geographical
distance, Socio-cultural distance, Temporal
distance). As shown in Table 4, the value of R
Square is 0.664, indicating that a 66.4% variation in
communication can be predicted by independent
variables (geographical distance, socio-cultural
distance, temporal distance).
Based on the Table 5 results the relationship of
each variable and significance has been explained
one by one based on their hypothesis statement
which is given below:
H1: Geographical distance has a negative effect
on communication.
Table 5 shows the results of the geographical
distance having a beta value of -0.718. This shows a
negative influence of the geographical distance for
communication and also the value of P
(significance) is 0.000 which is less than 0.05. This
specifies that there is a significant relationship
between the geographical distance and the
dependent variable communication. This also
implies that there is sufficient evidence to conclude
that the geographical distance is significantly related
to communication. Based on the above discussion
hypothesis (H1) is supported in this research.
H2: Socio-cultural distance has a negative effect
on communication.
Hypothesis (H2) shows relationship among socio-
cultural distance and communication. In Table 5
independent variable socio-cultural distance has a
beta value of -0.245. This shows a negative
relationship of socio-cultural distance and
communication. Table 5 also shows another
important value, P (significance) which is 0.000 and
less than 0.05. This result implies that socio-cultural
distance is significantly related to communication.
Based on the above discussion we can conclude that
socio-cultural distance and communication have
negative and significant relationship. This indicates
that hypothesis (H2) is supported in this study.
H3: Temporal distance has a negative effect on
communication.
To study the relationship between the temporal
distance and communication the beta value is -0.120.
This also shows a negative influence of the temporal
distance for the communication. The value of P
(significance) is 0.011 which is less than 0.05. This
shows that there is a negative and significant
relationship among the temporal distance and
communication. Based on the above evidence,
hypothesis (H3) is supported in this research.
4.5 Discussion
This section is about the discussion of the hypothesis
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Table 5: Model Summary of the Proposed Framework.
Model
Un-Standardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
(Constant)
-1.184 0.325 -3.643 0.000
Geographical Distance 0.734 0.047 -0.718 15.466 0.000
Socio-Cultural Distance 0.326 0.064 -0.245 5.094 0.000
Temporal Distance 0.210 0.082 -0.120 0.557 0.011
a. Dependent Variable: Communication
results obtained from the data analysis. We will
discuss each hypothesis under the results discussed
in section 4.4.
H1: Geographical distance has a negative effect
on communication.
Geographical distance is one of the main factors that
affect communication in GSD. The results of this
hypothesis were presented in section 4.4. The beta
value obtained from geographical distance was -
0.718, which shows that the geographical distance
has a negative influence over communication
process. Meanwhile, the value of P (Sig) was
obtained as 0.000 (p<0.05), which shows that the
relationship between geographical distance and
communication is significant.
It shows that geographical distance can
negatively affect the communication process
between team members. It implies that there is
sufficient evidence to conclude that as much
geographical distance increases between the team
members, the level of communication will
decreases.
H2: Socio-cultural distance has a negative effect
on communication.
In GSD people may be coming from different
cultural backgrounds and they have their own
cultural styles (Sahay and Walsham, 1997).
Variations in culture styles can create
misunderstanding among distributed sites which can
cause the damage of communication process
(Imsland et al., 2003). In this case, the results of
hypothesis 2 (H2) have been presented in section
4.4. The beta value obtained for socio-cultural
distance was -0.245, showing a negative influence of
socio-cultural distance over communication. Other
key value was P (Sig). The P (sig) value was
obtained as 0.00 (p<0.05), this shows a significant
relationship between socio-cultural distance and
communication. The above results come to be the
evidence about the presence of a negative and
significant relationship between socio-cultural
distance and communication. This respectively
shows that cultural distance among dispersed team
members can negatively affect the communication
process in GSD. This hypothesis was also proved
and supported as an influence factor in
communication adopted by various studies of
researchers (Sahay and Walsham, 1997) in different
domains.
H3: Temporal distance has a negative effect on
communication.
Temporal distance is actually the reducing of
overlapping hours between distributed sites (Casey
and Richardson, 2008). Less time overlapping is
challenging and problematic matter for GSD
industry. Sometime dispersed team members try to
develop something very quickly than delay in
response become an immense problem due to the
temporal distance (Noll et al., 2010). In this case, the
results of hypothesis 3 (H3) have been presented in
section 4.4, where the beta value obtained for
temporal distance was -0.120 and values of P (sig)
was 0.011 (P<0.05). It is showing a negative and
significant relationship between temporal distance
and communication. Hence the hypothesis 3 (H3) is
supported.
The above results come to be the evidence about
the presence of a negative and significant
relationship between temporal distance and
communication. Similar findings have been proved
and supported by various studies of researchers in
other domains. For example in (Damian et al., 2007;
Helena et al., 2006) the authors stated that as a result
of temporal distance the team members may unable
to find track of the overall developing process and it
can be a serious problem in GSD. The study here
confirms this as an issue. Based on the above
discussion about results, the summary of all the
hypothesis results have been presented in Table 6.
4.6 Final Proposed Framework
An attempt has been made in this study to develop a
framework that consolidates relevant factors that
have been categorized as geographical distance,
socio-cultural distance and temporal distance. The
framework has been applied in GSD industries of
EffectsofGeographical,Socio-culturalandTemporalDistancesonCommunicationinGlobalSoftwareDevelopment
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165
Table 6: Summary of Hypothesis results.
S. No Hypothesis No Independent Variables Dependent Variables Results
1 H1 Geographical Distance Communication Supported
2 H2 Socio-Cultural Distance Communication Supported
3 H3 Temporal Distance Communication Supported
Pakistan to validate the hypothesis of the proposed
framework. Based on aforementioned discussions,
three hypotheses were highlighted separately to
examine the relationship of independent and the
dependent variables. It has been found that all the
three hypotheses have been supported. Therefore,
the final proposed framework is given below in
Figure 2.
Figure 2: Final Proposed Framework.
The above framework has three independent
variables namely geographical distance, socio-
cultural distance and temporal distance. A multiple
regression analysis was conducted to evaluate the
each independent variable relationship with the
dependent variable. The results of Beta for
independent variables geographical distance, socio-
cultural distance and temporal distance was obtained
as -0.718, -0.245 and -0.120 respectively. Similarly
the significance score of the independent variables
geographical distance, socio-cultural distance and
temporal distance was 0.000, 0.000 and 0.011
respectively. This indicated that these three variables
have a negative and significant correlation with
communication.
Based on the above framework discussion, it can
be concluded that the communication process
depends on three vital factors, geographical distance,
socio-cultural distance and temporal distance. As the
effect of these factors increases the communication
process will be negatively affected.
5 THREATS TO VALIDITY
Threats to validity are possible risks that could come
up throughout the planning and execution phases of
the empirical research studies (Wohlin et al., 2012).
The identification and alleviation of these risks are
crucial activities that involve a lot of effort from
researchers (Neto and Conte, 2013). According to
(Biffl and Halling, 2003) every empirical study
address threats to validity. These threats must be
determined and tackled (Biffl and Halling, 2003).
There are several threats towards the design of
this study. During literature review phase most of
the data was collected by a single researcher.
Though we tried to alleviate this threat by observing
any unclear problems and discuss them together still
there exist a higher risk that a single researcher
could be biased and continually extract the wrong
data. For the future we will try to have at least two
reviewers for each research article.
The context used for survey data collection was
limited to single country. In extending of this work
we should consider more GSD organizations in
different other countries. This would give a chance
to collect more data with big sample size, which
could allow well and more thorough statistical
analysis as well as could cover more GSD
organizations in different countries.
In the current study we have defined three key
factors that could negatively affect the
communication process. Therefore there is clear
threat to the validity of the framework in the sense
of additional factors that might affect the
communication process. The results of this study can
be used in order to identify additional factors from
literature and industry.
6 CONCLUSIONS
In this study communication during the requirements
change management process in GSD has been
assessed on the bases of three key factors
geographical, socio-cultural and temporal distances.
The negative effect of these factors on
communication has been examined. In this study a
framework was proposed to examine the relationship
among dependent variables and the independent
variables. The findings from the survey data shows
that geographical distance, socio-cultural distance
and temporal distance have negative and significant
relationship with communication. This indicates that
with a high geographical distance, socio-cultural
distance and temporal distance among distributed
Geographical
Distance
Socio‐Cultural
Distance
Temporal
Distance
Communication
Beta= -0.718
Si
g
= 0.000
Beta= -0.245
Si
g
= 0.000
Beta= -0.120
Sig= 0.011
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team members, the level of communication will be
decreases. To the best of the knowledge of the
authors this study is the one attempted to investigate
the impact of factors on communication process for
the first time through literature survey and state of
the practice (industrial study) during requirements
change management process.
7 FUTURE WORK
The results from this exploratory study can be used
for future research in the GSD area in relation to
communication during requirements change
management. Following topics can be potential
future of this study.
In future the proposed framework of this study
can be tested with a bigger sample size.
For future research it is important to identify the
mitigation practices which could alleviate the
effect of the identified factors.
For future research study it is vital to identify
additional factors that can affect the
communication process.
ACKNOWLEDGEMENTS
We would like to thanks department of Computer
Science, City University of Hong Kong. This
research is supported in part by the City University
of Hong Kong research fund (Project No. 7200354,
7004222).
REFERENCES
Agerfalk, P. J., Fitzgerald, B., Holmstrom Olsson, H.,
Lings, B., Lundell, B., & Ó Conchúir, E. 2005. A
framework for considering opportunities and threats in
distributed software development, In Proceedings of
the International Workshop on Distributed Software
Development (Paris, Aug. 29, 2005).Austrian
Computer Society, 47–61.
Ahire, S. L., Golhar, D. Y., & Waller, M. A. 2007.
Development and validation of TQM implementation
constructs. Journal of Decision Sciences, 27(1), 23-56.
Bass, M., Herbsleb, J. D., & Lescher, C. 2009. A
Coordination Risk Analysis Method for Multi-site
Projects: Experience Report. Paper presented at the
Fourth IEEE International Conference on Global
Software Engineering, ICGSE, 2009.
Biffl, S., & Halling, M. 2003. Investigating the defect
detection effectiveness and cost benefit of nominal
inspection teams. IEEE Transactions on Software
Engineering, , 29(5), 385-397.
Brislin, R. W., Lonner, W. J., & Thorndike, R. M. 1973.
Cross-cultural research methods: J. Wiley.
Casey, V., & Richardson, I. 2008. Virtual teams:
understanding the impact of fear. Software Process:
Improvement and Practice, 13(6), 511-526. doi:
10.1002/spip.404
Cottmeyer, M. 2008. The Good and Bad of Agile Offshore
Development. Paper presented at international
conference on Agile, 2008. AGILE '08.
Da Silva, F. Q. B., Costa, C., Fran, A. C. C., &
Prikladinicki, R. 2010. Challenges and Solutions in
Distributed Software Development Project
Management: A Systematic Literature Review. Paper
presented at the 5th IEEE International Conference on
Global Software Engineering (ICGSE), 2010
Damian, D. 2006. Requirements Engineering in
Distributed Projects. Paper presented at the
Proceedings of the IEEE international conference on
Global Software Engineering.
Damian, D. 2007. Stakeholders in Global Requirements
Engineering: Lessons Learned from Practice. IEEE,
Software, 24(2), 21-27. doi: 10.1109/ms.2007.55
Damian, D., Izquierdo, L., Singer, J., & Kwan, I. 2007
Awareness in the Wild: Why Communication
Breakdowns Occur. Paper presented at the Second
IEEE International Conference on Global Software
Engineering (ICGSE), 2007.
E. Conchuir, O. H., H. Olsson, P. Agerfalk, B. Fitzgerald.
2009. Benefits of global software development:
exploring the unexplored. Software Process:
Improvement and Practice, 14(5), 301-301. doi:
10.1002/spip.444
Gunasekaran, A., & Ngai, E. W. 2008. Adoption of
e-procurement in Hong Kong: an empirical research.
International Journal of Production Economics,
113(1), 159-175.
Helena, H., Eoin, O. C., Par, J. A., & Brian, F. 2006.
Global Software Development Challenges: A Case
Study on Temporal, Geographical and Socio-Cultural
Distance. Paper presented at the International
Conference on Global Software Engineering, ICGSE,
2006.
Herbsleb, J. D. 2007. Global Software Engineering: The
Future of Socio-technical Coordination. Paper
presented at the Future of Software Engineering,
FOSE, 2007.
Herbsleb, J. D., & Mockus, A. 2003. An empirical study
of speed and communication in globally distributed
software development. IEEE Transactions on Software
Engineering, 29(6), 481-494.
Hofner, G., & Mani, V. S. 2007. TAPER: A generic
framework for establishing an offshore development
center. Paper presented at the Second IEEE
International Conference on Global Software
Engineering (ICGSE) 2007.
Holmström, H., Fitzgerald, B., Ågerfalk, P. J., &
Conchúir, E. Ó. 2006. Agile Practices Reduce
Distance in Global Software Development.
EffectsofGeographical,Socio-culturalandTemporalDistancesonCommunicationinGlobalSoftwareDevelopment
duringRequirementsChangeManagement-APilotStudy
167
Information Systems Management, 23(3), 7-18.
Huang, H., & Trauth, E. M. 2007. Cultural influences and
globally distributed information systems development:
experiences from Chinese IT professionals. Paper
presented at the Proceedings of the ACM SIGMIS
CPR conference on Computer personnel research: The
global information technology workforce, St. Louis,
Missouri, USA.
Hussain, W., & Clear, T. 2012. GRCM: A Model for
Global Requirements Change Management, presented
in 2nd International Requirements Engineering
Efficiency Workshop (REEW 2012).
Imsland, V., Sahay, S., & Wartiainen, Y. 2003. Key issues
in Managing a Global Software Outsourcing
relationship between a Norwegian and Russian firm:
Some Practical Implications. 26th Information
Systems Research Seminar in Scandinavia Finland
Joreskog, K. G., Sorbom, D., & Jhoreskog, K. 1989.
LISREL 7 user's reference guide: Scientific Software
Mooresville, IN.
Khan, A. A., Basri, S., & Dominic, P. D. D. 2012. A
proposed framework for requirement Change
Management in Global Software Development. Paper
presented at the International conference on Computer
& Information Science (ICCIS), 2012
Khan, A. A., Basri, S., Dominic, P. D. D., & Fazal-e-
Amin. 2012. A process model for Requirements
Change Management in collocated software
development. Paper presented at the IEEE Symposium
on E-Learning, E-Management and E-Services (IS3e),
2012.
Khan, S. U., Niazi, M., & Ahmad, R. 2011. Factors
influencing clients in the selection of offshore
software outsourcing vendors: An exploratory study
using a systematic literature review. Journal of
Systems and Software, 84(4), 686-699. doi:
10.1016/j.jss.2010.12.010
Korkala, M., & Abrahamsson, P. 2007. Communication in
Distributed Agile Development: A Case Study. Paper
presented at the 33rd EUROMICRO Conference on
Software Engineering and Advanced Applications. .
Liao, Z., & Shi, X. 2009. Consumer perceptions of
internet-based e-retailing: an empirical research in
Hong Kong. Journal of Services Marketing, 23(1), 24-
30.
Moe, N. B., & Šmite, D. 2008. Understanding a lack of
trust in Global Software Teams: a multiple-case study.
Software Process: Improvement and Practice, 13(3),
217-231. doi: 10.1002/spip.378
Neto, A. A., & Conte, T. 2013. A conceptual model to
address threats to validity in controlled experiments.
Paper presented at the Proceedings of the 17th
International Conference on Evaluation and
Assessment in Software Engineering.
Noll, J., Beecham, S., & Richardson, I. 2010. Global
software development and collaboration: barriers and
solutions. ACM Inroads, 1(3), 66-78.
Rafael, P., Jorge Luis Nicolas, A., & Roberto, E. 2006. A
Reference Model for Global Software Development:
Findings from a Case Study. Paper presented at
International Conference on the Global Software
Engineering, ICGSE '06.
Ramzan, S., & Ikram, N. 2006. Requirement Change
Management Process Models: Activities, Artifacts and
Roles. Paper presented at the IEEE Multitopic
Conference, INMIC, 2006.
Rea, L. M., & Parker, R. A. 2012. Designing and
conducting survey research: A comprehensive guide:
Jossey-Bass.
Sahay, S. (2003). Global software alliances: the challenge
of ‘standardization’. Scandinavian Journal of
Information Systems, 15(1), 3-21.
Sahay, S., & Walsham, G. 1997. Social structure and
managerial agency in India. Organization Studies,
18(3), 415-444.
Santos, J. R. A. 1999. Cronbach’s alpha: A tool for
assessing the reliability of scales. Journal of
Extension, 37(2), 1-5.
Shahzad, F. 2011. Communication Challenges in Success
of Offshore Software Development. International
Journal of Reviews in Computing, 8(31st of decemebr
2011).
Shujian, W. 2012. Overview of communication in global
software development process. Paper presented at the
IEEE International Conference on Service Operations
and Logistics, and Informatics (SOLI), 2012.
Šmite, D., Moe, N., & Torkar, R. 2008. Pitfalls in Remote
Team Coordination: Lessons Learned from a Case
Study Product-Focused Software Process
Improvement (Vol. 5089, pp. 345-359): Springer
Berlin Heidelberg.
Smite, D., Wohlin, C., Feldt, R., & Gorschek, T. 2008.
Reporting Empirical Research in Global Software
Engineering: A Classification Scheme. Paper
presented at the IEEE International Conference on
Global Software Engineering, ICGSE, 2008.
Sweet, S. A., & Grace-Martin, K. 2011. Data Analysis
with Spss + Mysearchlab With Etext: A First Course
in Applied Statistics: Pearson College Division.
Weisberg, S. 2014. Applied linear regression: John Wiley
& Sons.
Winkler, J. K., Dibbern, J., & Heinzl, A. 2008. The impact
of cultural differences in offshore outsourcing Case
study results from German–Indian application
development projects. Information Systems Frontiers,
10(2), 243-258.
Wohlin, C., Runeson, P., Höst, M., Ohlsson, M. C.,
Regnell, B., & Wesslén, A. 2012. Experimentation in
software engineering: Springer.
Zhu, J., Liang, Y., & Gu, Y. 2008. The Requirements
Change Analysis for Different Level Users. Paper
presented at the International Symposium on
Intelligent Information Technology Application
Workshops, 2008. IITAW '08.
ENASE2015-10thInternationalConferenceonEvaluationofNovelSoftwareApproachestoSoftwareEngineering
168
