Do Agile Managed Information Systems Projects Fail Due to a Lack of Emotional Intelligence?

Abstract

Agile development methodologies (ADM) have become a widely implemented project management approach in Information Systems (IS). Yet, along with its growing popularity, the amount of concerns raised in regard to human related challenges caused by applying ADM are rapidly increasing. Nevertheless, the extant scholarly literature has neglected to identify the primary origins and reasons of these challenges. The purpose of this study is therefore to examine if these human related challenges are related to a lack of Emotional Intelligence (EI) by means of a quantitative approach. From a sample of 194 agile practitioners, EI was found to be significantly correlated to human related challenges in agile teams in terms of anxiety, motivation, mutual trust and communication competence. Hence, these findings offer important new knowledge for IS-scholars, project managers and human resource practitioners, about the vital role of EI for staffing and training of agile managed IS-projects.

Full text

Do Agile Managed Information Systems Projects Fail Due to a Lack
of Emotional Intelligence?
Tan Trung Luong
1
&Uthayasankar Sivarajah
1
&Vishanth Weerakkody
1
#The Author(s) 2019
Abstract
Agile development methodologies (ADM) have become a widely implemented project management approach in Information
Systems (IS). Yet, along with its growing popularity, the amount of concerns raised in regard to human related challenges caused
by applying ADM are rapidly increasing. Nevertheless,the extantscholarly literaturehas neglected to identify the primary origins
and reasons of these challenges. The purpose of this study is therefore to examine if these human related challenges are related to
a lack of Emotional Intelligence (EI) by means of a quantitativeapproach. From a sample of 194 agile practitioners, EI was found
to be significantly correlated to human related challenges in agile teams in terms of anxiety, motivation, mutual trust and
communication competence. Hence, these findings offer important new knowledge for IS-scholars, project managers and human
resource practitioners, about the vital role of EI for staffing and training of agile managed IS-projects.
Keywords Agile .Information systems .Project management .Emotional intelligence
1 Introduction
Since the introduction of Agile development methodologies
(ADM), organizations have been fascinated by its potential to
engage stakeholders, adapt to changing requirements and
quickly deliver software (Cram 2019). Consequently, ADM
is now the mainstream information systems (IS) project man-
agement method of choice worldwide (Hoda et al. 2018)with
97% of organizations reporting to practice them (VersionOne
2018). ADM offer disciplined, yet lightweight processes
while placing human effort and experience at the core through
its central focus on people and interactions (Hoda et al. 2018).
Therefore, the importance of the people factor for the success
of ADM projects has been constantly highlighted in the schol-
arly literature (Cockburn and Highsmith 2001; Boehm and
Turner 2005; Moe et al. 2012;Fortmann2018;Cram2019)
and it has been repeatedly identified as critical success factor
for ADM managed projects (Lindvall et al. 2002;Chowand
Cao 2008; Pedersen 2013; Kalenda et al. 2018). Recognizing
the importance of the people factor, scholars have made con-
siderable effort to examine human related challenges that oc-
cur in agile teams. They reported challenges related to recruit-
ment, training, motivation and performance evaluation
(Conboy et al. 2011) or effective communication, social inter-
action and motivation (Lalsing et al. 2012). However, explor-
ing the primary origins and reasons of these challenges has
received less effort (Javdani Gandomani and Ziaei Nafchi
2016) and therefore remains vague.
Yet, if human aspects are neglected, there is a risk of the
results that are produced do not uncover key factors for deter-
mining the success or failure of software projects (Lenberg
et al. 2015). A construct that might be related to these reported
challenges and that has been neglected so far is Emotional
Intelligence (EI). Emotions grow out of social interactions
and are thus fundamental how team members interact and
work together (Barczak et al. 2010). Hence, EI has been found
to be positively related to important domains, such as im-
proved communication (Ciarrochi and Mayer 2013), job per-
formance and leadership effectiveness (Côté 2017)orteam
performance (Macht et al. 2019). Yet, empirical research on
EI of IS-professionals is scant (for exceptions, see Kosti et al.
(2014); Ahmad Marzuki et al. (2015); Rezvani and Khosravi
(2019)). As a consequence, the study of psychological aspects
in ADM remains a quite new research field (Thorgren and
Caiman 2019).
*Tan Trung Luong
T.T.Luong@bradford.ac.uk
Uthayasankar Sivarajah
U.Sivarajah@bradford.ac.uk
Vishanth Weerakkody
V.Weerakkody@bradford.ac.uk
1
University of Bradford, Richmond Road, Bradford BD7 1DP, UK
https://doi.org/10.1007/s10796-019-09962-6
Published online: 7 November 2019
Information Systems Frontiers (2021) 23:415–433
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

The purpose of this study is to examine how the EI of IS-
professionals influences the success of ADM-projects. The
research question it endeavors to answer is whether human
related challenges that IS-professionals perceive when work-
ing in agile managed teams are related to a lack of their EI.
Hence, this study contributes to information systems, project
management, organizational, psychology and human re-
sources research. It introduces EI as so far neglected critical
success factor to the ADM literature. The findings of this
study will significantly improve the staffing and training of
agile team members.
2 Literature Review
2.1 Plan-Driven Development Methodologies
Early project management methodologies are driven by a pro-
ject plan, which includes predefined project stages (Cooper
2014). The project stages are executed sequentially, whereas
each stage is only executed once (Robson 2013).
Consequently, these approaches are referred to as plan-
driven project management methodologies (Goodpasture
2010)orwaterfall-models(CooperandSommer2016;
Pedersen 2013). Plan-driven project management methodolo-
gies are characterized by their reliance on written documenta-
tion, extensive front-up planning, up-front customer involve-
ment and a more formal, command-and-control oriented man-
agement structure (Cram 2019).
Many authors have alluded on the pitfalls of plan-driven
methodologies. An often highlighted disadvantage is that they
are inflexible to cope with changes (Cobb 2011; Cooper
2014), i.e. they assume that the full scope of requirements
can be certainly assessed in advanced and that an optimal
and predictable solution exists for every problem (Dybå and
Dingsøyr 2008). Furthermore, they assume that projects exist
isolated from their environment and their emphasize on ro-
bustness of the plan has thus been criticized to not properly
respond to the increasing complexity and dynamic of today’s
projects (Radujkovićet al. 2014). Another drawback is that
too much time is wasted with documentation and client col-
laboration starts too late (Mandal and Pal 2015). Being
confronted with increasing problem complexity and rapid
changing requirements, software developers hence started to
realize that an alternative project management approach is
needed (Kakar 2017).
2.2 Agile Project Management
As response to these plan-driven methodologies, ADM
emerged in the late 1990s (Hoda et al. 2018). The aim of
ADM is to enable project management to sustain in an unpre-
dictable world (Dybå and Dingsøyr 2008), by facilitating
customer involvement, continuous software design and flexi-
ble scope (Serrador and Pinto 2015). In contrast to plan-driven
teams, where team members execute only their assigned tasks
within their specified roles, such as business analyst or pro-
grammers, agile teams organize themselves (Kakar 2017).
ADM apply an iterative process, where in each iteration, the
team plans, analyzes, designs, codes, and tests to achieve de-
fined goals without being driven by a sequential plan
(Thorgren and Caiman 2019).
Empirical studies have indicated that applying ADM is
positively related to project success, in terms of efficiency
and overall stakeholder satisfaction (Serrador and Pinto
2015), that they enable developers to produce higher quality
software (Tan and Teo 2007; Schmidt et al. 2014) and also
enable companies to achieve their targets in decreasing lead
times and improving the quality of their products (Minna
Pikkarainen et al. 2012). Though, ADM place great trust on
individuals (Goodpasture 2010). Not only that they demand
agile teams to be able to work in a self-managed way, but also
to possess the required mix of technical and business, as well
as behavioral knowledge (Goh et al. 2013). Consequently, for
many agile team members it is not easy to adapt to agile
practices, because they might feel uncomfortable with the in-
creased social interaction or being exposed with a lack of
sufficient business knowledge (Conboy et al. 2011). Also,
self-organizing teams struggle, when they are meant to take
over and share project management tasks as estimation, plan-
ning and requirement elicitation (Hoda and Murugesan 2016).
As a dilemma, ADM thus require a premium on people and
their interactions (Vinekar et al. 2006), if not the best available
(Radujkovićet al. 2014). Therefore, as provokingly stated by
Lalsing et al. (2012), agile looks great on paper, but will fail to
succeed in reality, if human psychology is not understood and
taken into account.
2.3 Conceptualization of Emotional Intelligence
Being dissatisfied with the narrow conceptualization of hu-
man intelligence and its focus on verbal, performance or aca-
demic abilities (Ciarrochi et al. 2000), a notable group of
scholars (Bar-On 1997; Mayer and Salovey 1997; Petrides
and Furnham 2001) focused on the relation between emotions
and intelligence, i.e. the conceptualization of EI as a new
distinct form of intelligence. Though, scholars are not aligned
whether non-cognitive competencies, such as motivation, per-
sonality or temper should be part of EI or not (Cho et al. 2015),
which resulted in considerable confusion and misunderstand-
ing (J. D. Mayer et al. 2008). Although, emotions have be-
come a central topic of research in the past 30 years in several
domains of psychological science, disparate approaches to
define and measure EI have still produced rather inconsistent
findings (Schlegel and Mortillaro 2019). Yet, most scholars
agree that the EI approaches can be classified into two broad
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categories: ability-based or mixed-based models (Rosete and
Ciarrochi 2005; Cho et al. 2015; Iliceto and Fino 2017;
Mattingly and Kraiger 2019).
Ability-based models have their origin in the Four-Branch
model of EI conceptualized by Mayer and Salovey (1997),
including the branches: perceiving emotions, facilitating
thought by using emotions, understanding emotions and man-
aging emotions in oneself and others. Each branch represents
a group of skills that proceeds developmentally from basic
tasks to more challenging ones Mayer et al. 2016). Mayer
et al. (1999) argued that EI can only meet the stringent criteria
of true intelligence, if it is defined as a set of abilities of mental
performance rather than just preferred ways of behaving. In
their view, intelligence has to be distinguished from personal-
ity, because intelligence involves organismic abilities to be-
have, whereas personality traits involve dispositions toward
behavior (Mayer and Salovey 1993).
In contrast to ability-based models, mixed-based models of
EI include in addition to emotional abilities, a constellation of
non-cognitive competencies, such as motivation, personality,
temperament or character and social skills (Schutte et al. 2013;
Cho et al. 2015). Having so many additional independent
qualities, mixed EI models have been criticized to be weak
from a construct validity point (Mayer et al. 2008) due to their
unknown content and theoretical value (Joseph and Newman
2010). This research will thus follow the concept of ability EI
rather than mixed EI.
2.4 How Should EI be Assessed?
Despite of advances in the conceptualization of EI, there is
still limited understanding of the psychometric properties of
existing EI measures (Cho et al. 2015). The controversy starts
with attempting to classify the different assessment methods
into different categories. Petrides and Furnham (2001)pro-
posed a simplistic classification to either testing ability or trait
EI, based on either to be measured via self-report or perfor-
mance based tests. However, researchers should pay attention
to distinct between construct and method (Arthur and Villado
2008). Hence, both the underlying model of an EI assessment
tool and its assessment method should be used as distinction
(Joseph and Newman 2010). Current measures of EI can
therefore be organized in three streams (Lopes 2016;
Schlegel and Mortillaro 2019), which are depicted in Fig. 1.
As illustrated by Lopes (2016), the first stream is based on
ability models and applies performance-based assessment.
The second stream is also based on ability models, but utilizes
self-report assessment. The third stream is based on mixed
models and relies on self-report assessment. As mentioned
above, this research will follow the concept of ability EI and
thus self-report mixed EI assessment tools have been excluded
from further discussion.
2.4.1 Ability EI Assessed Performance-Based
Some scholars argue that that EI is best measured as ability,
because people are poor at estimating their own levels of in-
telligence and therefore they estimate their abilities based on
other bases, such as self-confidence or self-esteem (Mayer
et al. 2016; Schlegel and Mortillaro 2019). The Mayer-
Salovey-Caruso Emotional Intelligence Test (MSCEIT) is still
the only performance-based ability assessment tool (Macht
et al. 2019). Yet, there is also no consensus among experts
in regard to the evaluation of the responses of this test
(Carvalho et al. 2016). The challenge is to determine objective
correct responses (Roberts et al. 2001). In particular, cultural
context might affect people’s emotions and it is thus not cer-
tain if a measurement developed within a specific cultural
setting can also applied in another cultural setting (Law et al.
2008; Lee and Kwak 2012). Another drawback of the
MSCEIT is the considerable amount of time to complete the
141 items and the high costs of its application (Carvalho et al.
2016). Thus, measuring EI as an ability remains a big chal-
lenge (Lopes 2016).
2.4.2 Ability EI Assessed Self-Reported
One of the most frequently administered ability EI self-report
assessment tools is the Wong and Law Emotional Intelligence
Scale (WLEIS). The initial motivation to develop the WLEIS,
was the need for a simple, practical, and psychometric sound
measure of ability EI that can be used for organizational re-
search purposes, i.e. that can be applied on the workplace
(Wong and Law 2002). The WLEIS has many advantages,
such as the fact that it is relatively short, including only 16
items, designed to be applied on working population, free to
administer and described to be relatively independent of per-
sonality traits (Ng et al. 2007;Brannicketal.2009). Current
research indeed supports the cross-cultural generalizability of
the WLEIS (Libbrecht et al. 2014;Carvalhoetal.2016;Iliceto
and Fino 2017) and there is evidence that it is a predictor for
job performance (Wong and Law 2002; Law et al. 2008;
Trivellas et al. 2013;Chenetal.2015).
Fig. 1 Concepts, models and assessment method for EI (source: author)
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2.5 Artificial Emotional Intelligence as Neglected
Component of Artificial Intelligence
Although, Artificial Intelligence (AI) technologies have evi-
dently demonstrated how they enhance our lives, e.g.
smartphones, online shopping services and how business con-
nect with and understand customers, many challenges remain
to be met, for AI to take off with all on board (Helal 2018).
According to Rossi (2018), the current research in AI focus on
two main areas. One is based on rules, logic and symbols. The
other area of research is based on examples, data analysis and
correlation and can be applied in cases where there is an in-
complete definition of the problem to be solved. However,
when it comes to AI, emotions are not usually the first thing
that comes to mind (Schuller and Schuller 2018). Yet, under-
standing how people solve problems in the area of emotions is
essential for computer systems and robots to emulate human
reasoning (Mayer et al. 2016). Consequently, all tasks requir-
ing EI are still beyond the reach of AI systems (Brynjolfsson
and Mitchell 2017). Therefore, developing Artificial
Emotional Intelligence, in particular the ability to recognize
emotions and then respond appropriately, is essential to the
true success of digital assistants we interact with every day,
such as Apple’sSiriorGoogle’s Alexa (Krakovsky 2018).
3 Conceptual Model and Hypotheses
Development
3.1 Anxiety Caused by Agile Practices
Individuals can experience negative psychological states, such
as anxiety, because they ineffectively interpret emotional stim-
uli, set inappropriate goals, implement ineffective coping
strategies or fail to employ appropriate emotion regulation
skills (Thomas et al. 2017). For this research, anxiety is de-
fined as “a negative psycho-emotional state that results when
fear of events, which are not always identifiable, manifests as
an exaggerated response where nervousness and worry pre-
dominate”(Castro-Sánchez et al. 2019).
This also applies to IS-professionals, as they encounter
numerous obstacles in their effort to successfully complete
their assigned tasks and these challenges increase levels of
stress, which subsequently affect their ability to self-regulate
their feelings and understanding (Rezvani and Khosravi
2019). For example, some agile team members experience
fear that is caused by the transparency of their skill deficien-
cies, because agile practices, such as daily stand-up meetings,
onsite customers or the use of storyboards require direct and
constant communication and collaboration (Conboy et al.
2011). Similar cases have been reported by Lalsing et al.
(2012), where team members did not raise concerns regarding
their technical deficits in order to avoid revealing that they
were technically behind other team members. Furthermore,
many developers feel a strong temptation to always say
“yes”to avoid appearing less competent than other team mem-
bers, even if they know that they cannot deliver a certain task
in a given time (Kovitz 2003). Furthermore, some agile team
members, particularly junior team members, might feel scared
to make estimates, velocity or product backlog, because they
are afraid to be perceived as incompetent for potentially mak-
ing wrong estimates (Dorairaj et al. 2012). It has also has been
pointed out that agile team members might even try to avoid
arguing in order to conform to other team members, although
this behavior is preventing effective decision-making (Moe
et al. 2012). In addition, agile team members might also feel
worried about adapting to the new agile methodology, i.e. that
they might feel unsecure whether they can adapt adequately to
this new methodology and having concerns, about how other
team members might judge them (Javdani Gandomani and
Ziaei Nafchi 2016).
The ability to regulate one’s own emotions can decrease
undesired emotional impact on job performance, i.e. people
can rise above negative perceptions quickly and thus their
performance will be impacted less (Law et al. 2008). For ex-
ample, when spiked by aggressive customer behavior, being
able to regulate emotions is important to the long term health
and retention of IS-professionals (Shih et al. 2014).
Individuals with this ability also present a wider repertoire of
strategies for maintaining positive emotions and for reducing
or modifying negative emotions (Martínez-Monteagudo et al.
2019). Furthermore, this ability has also been found to buffer
the impact of cognitive text anxiety in academic achievements
(Thomas et al. 2017). Consequently, following hypothesis is
proposed:
H1a. The ability to regulate one’s own emotions is neg-
atively associated with anxiety perceived by agile team
members.
3.2 Motivation to Apply Agile
Motivation is recognized as a key success factor for software
projects (Sharp et al. 2009) and hence low motivation can
cause failure of software engineering endeavors (Pankratz
and Basten 2017). For this research motivation is defined as
“initiation, direction, intensity and persistence of behavior”
(Sharp et al. 2009). On the one hand, recent studies indicate
that motivation of agile teams is even significant higher than
of plan-driven teams (Kakar 2017). They argued that this is
due to self-organization, which is positively related to moti-
vation because it stimulates greater team member involvement
and participation, resulting in higher commitment and moti-
vation. On the other hand, motivation has also been increas-
ingly cited as a particularly pernicious people problem in
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software engineering (Sharp et al. 2009). For example,
Conboy et al. (2011) reported that some agile team members
perceived the adoption of agile methods as overly onerous,
complex and time-consuming. Although, possessing the com-
petence, they were not convinced that agile will work and
hence lacked motivation to apply agile practices. This was
particularly prominent in companies that adopted agile
methods top-down. A lack of enough motivation can also be
a hidden reasons why some agile team members are indiffer-
ent to adopt agile methods in their organizations (Javdani
Gandomani and Ziaei Nafchi 2016). Another aspect has been
highlighted by Lalsing et al. (2012), who reported that agile
team members might suffer from motivation issues, when
tasks are assigned to them that they do not perceive as chal-
lenging enough.
Law et al. (2008) associated motivation with the ability to
use emotions to facilitate performance. They argued that peo-
ple with strong learned goal-seeking behaviors are able to
make use of their emotions in order to direct their behaviors
to achieve their goals. In a similar vein, Mayer et al. (2016)
stated that EI includes the ability to facilitate thinking by
drawing on emotions as motivational and substantive inputs.
In regard to team EI, Barczak et al. (2010) stated that teams
with higher EI are better to inspire support and confidence in
fellow team members. As a result, following hypothesis is
proposed:
H2a. The ability to use emotions is negatively associ-
ated with motivation challenges of agile team
members.
Teams with higher ability to monitor and regulate their
emotions are more likely to motivate themselves (Barczak
et al. 2010). Similarly, research revealed that individuals with
higher perceived ability to regulate their emotions are more
likely to report being motivated by achievement needs
(Christie et al. 2007). In line with this, below hypothesis is
proposed:
H2b. The ability to regulate emotions is negatively asso-
ciated with motivation challenges of agile team members.
3.3 Communication Competence in Agile Teams
In ADM projects, close and frequent communication among
team members substitutes predetermined plans, such as used
in traditional management approaches (Thorgren and Caiman
2019) and therefore hurdles in communication can in turn
have a negative impact on the efficiency of agile practices
(Pikkarainen et al. 2008) and several dependant functionali-
ties, such as the communication of requirements or queries
(Lalsing et al. 2012). The importance of communication com-
petence throughout the entire ADM project has thus been
highlighted by many scholars (Lalsing et al. 2012;Hummel
et al. 2013; Ghobadi and Mathiassen 2016). Likewise,
Pedersen (2013) shed light on the importance of communica-
tion with the client as it continues throughout the development
process. For example, customers are given demonstrations of
solution after each iteration and their feedback is used as the
basics for the next course in action. Great emphasis is also
placed on communication involving diverse stakeholders
through practices such as joint-application design sessions
and customer focus groups (Ghobadi and Mathiassen 2016).
This research has chosen a definition of communication com-
petence proposed by McCroskey (1988)as“adequate ability
to pass along or give information; the ability to make known
by talking or writing”.
In ADM projects, knowledge is considered to be social
constructed and collectively held and verbal communication
is considered to be more effective in sharing concepts, ideas or
desires, because it allows rapid mutual feedback and also stim-
ulate further thinking, by transforming and reshaping thoughts
and drawing new implications from them (Melnik and Maurer
2004). Hence, agile practices mainly rely on face to face con-
versations between team members to communicate rather than
just source code (Kovitz 2003) and they are therefore shifting
communication from the traditional paradigm, including doc-
umentation, plans and models towards more informal commu-
nication (Hummel et al. 2013). In the same vein, Begel and
Nagappan (2007) pointed out that within an agile context so-
cial cliques may become the dominant means of communica-
tion and that those with poor interpersonal skills might be
excluded from these cliques and thus from important commu-
nication as well.
Yet, in order to successfully transfer tacit knowledge, the
agile team members need to possess a multitude of character-
istics, such as empathy and the ability to articulate and com-
municate enough (Takpuie and Tanner 2016). However, IS-
professionals have been reported to be an introverted person-
ality type (Beecham et al. 2008;Hendonetal.2017), who
enjoys working alone and may get overwhelmed with too
much social interactions (Sharp et al. 2009;Shihetal.
2014). They have also been characterized to have no desire
to interact with customers (Shih et al. 2014)andwhotypically
have difficulties in communicating because their actions are
based on what they think rather than on what somebody else
feels (Capretz 2003). Yet, whenever team members work to-
gether, emotions grow out of social interactions and thus have
a pervasive influence in establishing a collaborative environ-
ment, where team members are encouraged to embrace
change and to openly share and discuss their individual view-
points, share knowledge and learn from each other (Barczak
et al. 2010). Furthermore, emotions convey information and
therefore function as communication signals, such as happi-
ness is a signal of wanting to join with others or sadness is a
signal of loss and wanting of comfort (Mayer et al. 2008).
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Therefore, scholars have shed light on the relation between EI
and communication competence, such as effective communi-
cation requires the management and recognition of one’sown
and others’emotional expression (Troth et al. 2012a)orthe
ability to understand emotions contributes to developing com-
munication skills (Petrovici and Dobrescu 2014). In a similar
vein, George (2000) argued that in order to effectively com-
municate with other people about one own needs and con-
cerns it is necessary to accurate appraise and express emotions
of one’s self and others’. Consequently, the following hypoth-
eses are proposed:
H3a. The ability to appraise and recognize emotions in
one’s self is negatively associated with communication
challenges.
H3b. The ability to appraise and recognize emotions in
others is negatively associated with communication
challenges.
H3c. The ability to regulate emotions is negatively asso-
ciated with communication challenges.
3.4 Mutual Trust as Key Factor for Team Performance
Mutual trust is one of the most influential key factors in regard
to agile team performance (Lalsing et al. 2012) and a predictor
for project performance and project effectiveness (Rezvani
et al. 2016). For this research, trust will be understood as “a
psychological state comprising of the intention to accept vul-
nerability based upon positive expectations of the intentions
of behaviours of another”(Rousseau et al. 1998). Yet, trust
can be challenging in agile teams. For example team members
might be reluctant to assign certain tasks to other team mem-
bers, because they have concerns if they can accomplish them
in an effective manner and on time and thus assign the tasks to
themselves and as a consequence cause delays in other areas
(Lalsing et al. 2012). Likewise, Henttonen and Blomqvist
(2005) argued that trust is an important component in team
development and effectiveness, because team members are
less willing to contribute and cooperate if there is a lack of
trust. In particular, at the start of a project, a lack of familiarity
between the team members can be an impediment for collab-
oration and communication (Lalsing et al. 2012). Another
aspect that impedes the building of trust are cultural issues
(Javdani Gandomani and Ziaei Nafchi 2016). As illustrated
by Dorairaj et al. (2012), cultural differences include the ac-
cent and rapidness of verbal communication, body language
and also actual meaning for the spoken word. For example,
words might have different meanings in different cultures.
Replying with “yes”by an Indian team member might mean
“Yes, I heard you.”However, an American team member
might perceived it as “Yes, it is done”. This lack of cultural
understanding impedes significantly the building of trust and
bonding among the team members.
In fact, as stated by Barczak et al. (2010) team trust is mainly
build on both, emotional bonds and perceived competencies of
individual team members. They argued that when team mem-
bers manage their own emotions and those of their peers, they
aremorelikelytobetrustedandreliedonfortheircompetence
and ability. Besides, when team members are aware of their
own emotions they can easier emphasize with their peers and
provide support and consequently creating more team trust.
Accordingly, below hypothesis can be formulated:
H4a. The ability to regulate emotions is negatively asso-
ciated with mutual trust challenges.
H4b. The ability to appraise and recognize emotions in
one’s self is negatively associated with mutual trust
challenges.
The ability to appraise and recognize other people’s
emotions assist in being accepted by others, earning
their trust and gaining their collaboration (Law et al.
2008). As a result, below hypothesis can be
formulated:
H4c. The ability to appraise and recognize other people’s
emotions is negatively associated with mutual trust
challenges.
An overview of the nine proposed hypotheses is presented
in the conceptual model in Fig. 2. The purpose of this research
is to examine causal inferences between EI and human related
challenges perceived by agile team members.
4 Research Methodology
4.1 Indicating Causal Inference
One of the most repeated mantras in social science is that
“correlation does not imply causation”(Box-Steffensmeier
2007). In order to imply that changes of one variable causes
changes in another variable, research design needs to en-
sure that their relationship is not spurious, i.e. that there are
no unaccounted causes making the original variables just to
appear to be correlated (Abbott and McKinney 2013). The
failsafe to ensure nonspuriousness is to use randomized
experiments, because if the individuals were randomly
assigned to the treatments, the baseline characteristics, also
referred to as covariates, on average are approximately
equal (Antonakis et al. 2010). However, randomisation is
often unethical or just not feasible (Russo et al. 2011). In
social science, most studies are therefore designed based on
non-experimental design and observational data, as the
studied objects can often not be randomly exposed to the
event (Tsapeli and Musolesi 2015).
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4.2 Propensity Score Matching
A framework for quasi-experimentation has first been formal-
ized by Rubin (1974). Rubin’s Causal Model (RCM) is a
model with a compact and precise conceptualization of causal
inference, which includes the three key elements: units, treat-
ment and potential outcomes (Shadish 2010). In order to re-
duce bias, the treatment units have to be matched with the
most similar control units (Tsapeli and Musolesi 2015), e.g.
by applying propensity score matching (PSA). PSA is a math-
ematical approach that utilizes the participant’s probability to
be assigned to a group to balance the participants between the
groups (Forrest 2012). This probability is calculated based on
a propensity score, which is the probability of being treated,
by summarizing the covariates into one single scalar (Stuart
2010).
PSA can thus only provide consistent estimates, if the
researcher has sufficient knowledge about covariates that
predict whether an individual would have received the treat-
ment or not (Antonakis et al. 2010). However, not all
covariates, related to treatment and outcome needs to be
included, as a sufficient number of covariates is sufficient
to delink selection into treatment from the outcome (Herzog
2014). Covariates omitted are controlled for the extent that
they correlate with the covariates included in the propensity
score and therefore from a theoretic perspective, the inclu-
sion of only those covariates that effect the treatment assign-
ment is sufficient and thus covariates related to the outcome
can be neglected (Austin 2011). Yet, the decision to include
certain variables as covariates or not should be generous,
because there is no huge impact when including variables
that actually do not influence the treatment variable.
However, neglecting potentially important covariates could
be very costly in regard to increased bias (Stuart 2010). In
regard to this study, prior research has demonstrated the EI
is influenced by gender (Carvalho et al. 2016; Cabello et al.
2016), cultural background (Van Rooy et al. 2005;Ngetal.
2007; Margavio et al. 2012) and age (Cabello et al. 2016).
Consequently, these three characteristics will be considered
as confounding covariates.
Fig. 2 Proposed conceptual model “Impact of Emotional Intelligence in Agile teams”(source: author)
421Inf Syst Front (2021) 23:415–433
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Furthermore, PSM aims to balance covariates between the
treatment and control group, when the treatment is binary
(Imai and David 2004). Though, the treatment variable for this
study is EI, which is a continuous variable. EI has thus been
dichotomized, in a way that participants, with a score in the
upper third of the population in the examined EI dimension
have been assigned to the treatment group and respectively,
participants with a score in the lower third have been assigned
to the control group.
4.3 Data Collection, Ethical Considerations, Sample
and Measures
Data collection started July 13th 2018 and was conducted for
eleven weeks. Any IS-professional, who had experienced
ADM were welcome to participate in this research.
Participation was anonymous, voluntary and without any
compensation. The participants were also informed about the
purpose of this research, that they are free to withdraw at any
time and that everything they report is confidential.
Approximately 4.000 personal invitations were sent
through online business network platforms. In total 324 par-
ticipants completed the survey, within which 210 participants
mainly worked in ADM projects, rather than in plan-driven or
hybrid managed projects. In order to reduce bias, nine
outliners were excluded from the dataset. Outliners, are par-
ticipants with a score very different from the rest of the data
(Field 2013). Furthermore, seven participants had received EI
training before. As recent research indicated that these kind of
trainings indeed can increase EI for adults(Schutte et al. 2013;
Lopes 2016; Mattingly and Kraiger 2019), these participants
were also excluded. As a result, the final sample contained
194 participants. Most participants were male (86%) and be-
tween 25 and 40 years old (77%). The cultural distribution is
illustrated in Fig. 3.
The participants came from 53 different cultural back-
grounds. Though, the majority either came from German
(22%) or Indian (23%) cultural background. Despite of the
high amount of German participants, the sample is a fair rep-
resentation of the global software industry, which is dominat-
ed by men (Weilemann and Brune 2015) and also is the Indian
IT service industry possessing a high share of the world mar-
ket (Woszczynski et al. 2016).
4.4 Human Related Agile Challenges Inventory
(HRACI)
So far, no instrument has been designed to measure the degree
of perceived challenges in ADM projects and therefore the
Human Related Agile Challenges Inventory (HRACI) had to
be developed. The HRACI builds on previous research and
contains the four dimensions: anxiety (ANX), motivation
(MOT), communication (COM) and mutual trust (TRU).
Three indicators for each dimension have been derived based
on identified human related challenges reported in the litera-
ture for ANX, MOT and TRU. The indicators for COM are
based on the Self-Perceived Communication Competence
Scale (McCroskey 1988). All indicators are assessed by
means of a 5-point Likert-scale.
4.5 Wong and Law Emotional Intelligence Scale
(WLEIS)
The WLEIS contains the four dimensions: appraisal and ex-
pression of emotion in oneself (SEA), appraisal and recogni-
tion of emotion in others (OEA), use of emotion to facilitate
performance (UOE) and regulation of emotion in oneself
(ROE) (Law et al. 2004). These four dimensions are based
on the Salovey and Mayer Ability Model of Emotional
Intelligence (Wong and Law 2002). Recent research con-
firmed similarities between the four dimensions used in the
WLEIS and the four factors of Salovey and Mayer’s Ability
Model of Emotional Intelligence (Carvalho et al. 2016).
5Dataanalysis
5.1 Hypothesis Testing
All analyses were conducted using IBM SPSS 24 and IBM
AMOS 25. There was no missing data. Table 1reports reli-
ability statistics for all dimensions of HRACI and WLEIS.
Cronbach-alphas were all above 0.7, indicating that the survey
items were good indicators of the constructs they were sup-
posed to measure.
Confirmatory factor analysis using maximum likelihood meth-
od was conducted to examine structure validity for both scales.
AccordingtoSun(2005), a χ2/dfratiolessthan2or3anda
RMSEA less than 0.08 indicate an acceptable model. For CFI and
TLI a value greater than 0.9 indicates an acceptable fit and a value
greater than 0.95 indicates a good fit. The results, which are pre-
sented in Table 2, demonstrate good fit for both scales.
The CFA model for HRACI and its standardized parameter
estimates are shown in Fig. 4.
The standardized factor loadings for the HRACI are pre-
sented in Table 3. For the second-order factor model all factor
loadings met the recommended cut-off criteria of 0.32
(Tabachnick and Fidell 2007) and most loaded well with
>0.5. However, for the first-order factor model, except of
COM, the factor loadings were low, with MOT and TRU even
below the cut-off criteria.
Figure 5presents the CFA model for the WLEIS and its
standardized parameter estimates. The standardized factor
loadings for the WLEIS are presented in Table 4. For the
second-order factor model, except of s4, all factor loadings
met the recommended cut-off criteria of 0.32 (Tabachnick
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and Fidell 2007) and most loaded well with >0.4. However,
for the first-order factor model, except of SEA, the factor
loadings were low, with OEA even below the cut-off criteria.
Correlations, as illustrated above are a prerequisite to indi-
cate causal inference. As MOT, COM and TRU did not meet
the assumption of normality, Spearman-rho was applied to
calculate correlations which are presented in Table 5.
Hypothesis 1, proposing a negative association between
ROE and ANX was not supported (p= 0.106). Hypothesis
2a and 2b, suggesting a negative association between MOT
and UOE, as well as ROE were both fully supported with
statistical significance (p≤0.01). Hypothesis 3a and 3c, sug-
gesting a negative association between COM and SEA as well
as ROE were also both fully supported with statistical signif-
icance (p ≤0.001). Hypothesis 3b proposing a negative asso-
ciation between COM and OEA was also supported with sta-
tistical significance (p≤0.05). Hypothesis 4a and 4c, suggest-
ing a negative association between TRU and ROE, as well as
OEA were both fully supported with statistical significance
(p ≤0.01). Finally hypothesis 4b, proposing a negative asso-
ciation between TRU and SEAwas not supported (p=0.150).
The results also revealed three new findings that were not
hypothesized. SEA and UOE were both significant negatively
associated with ANX (p ≤0.05). Finally, COM and UOE were
also statistically significant negatively associated (p≤0.001).
The revised conceptual model is presented in Fig. 6.
Fig. 3 Cultural distribution of 194 agile practitioners
423Inf Syst Front (2021) 23:415–433
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The analysis of the data has confirmed seven and rejected
two of the proposed hypothesis. Also three new findings were
identified. The confirmed hypothesis and the new findings
will be discussed in section 6.
5.2 Impact of EI on Perceived Challenges
In order to quantify the impact of EI on the perceived chal-
lenges the Average Treatment Effects (ATE) were calculated
for the ten combinations which were significant correlated.
The ATEs were calculated by applying subclassification, with
five subclasses defined by quantiles of the propensity score.
This has the advantage that the initial bias due to covariates
can be reduced to at least 90% (Stuart 2010). The ATEs are
illustrated in Table 6.
The abilities to use emotion to facilitate performance and to
regulate emotions in oneself had the highest impact on the
measured challenges.
6 Discussion and Findings
The purpose of this research was to examine if a lack of EI has
a negative impact on perceived human related challenges in
agile teams within the dimensions of anxiety, motivation,
communication competence and mutual trust. Data analyses
revealed ten significant negative associations. Each dimension
is now discussed.
6.1 EI as Protective Factor for Anxiety
The results showed that anxiety in agile teams is negatively
related to the abilities of self-emotional appraisal and use of
emotions. These findings are consistent with results of prior
research. For example, male medical Iranian students perceive
less test anxiety, if they have high EI (Ahmadpanah et al.
2016) or young Spanish football players who have low scores
in perceiving and regulating their emotions report higher
levels of anxiety (Castro-Sánchez et al. 2019). Recent research
has indicated that EI can diminish the probability of anxiety
(Abdollahi and Abu Talib 2015) and therefore can serve as a
protective factor in the path from rumination to anxiety (Liu
and Ren 2018).
These findings therefore support prior recommendations. If
agile team members feel insecure, they might be reluctant to
be transparent about their weaknesses and feel afraid to admit
the truth about what is really happening in their teams
(Dorairaj et al. 2012). Hence, such as that agile team members
need an environment where they feel safe to expose their
weaknesses (Conboy et al. 2011) and they need a sense of
psychological safety, that is they must feel safe to speak up
when noting a gap in others’work or difficulties in their own
(Thorgren and Caiman 2019). Hence, the decision on the ex-
tent of agile use should consider concerns raised by agile team
members and where significant anxiety is noted, management
may wish to make participation optional if possible (Cram
2019).
6.2 EI as Predictor of Motivation
The results of this study have found that the abilities to use
emotions and to regulate emotions are both negatively related
to agile team members perceiving challenges in regard to mo-
tivation. This is in line with prior research, such as Law et al.
(2008), who stated that EI is a reasonable predictor of moti-
vation, because individuals with high EI are able to regulate
and user their emotions to improve performance and therefore
Table 1 Reliability statistics
Scale Dimension Mean S.D. Cronbach-
alpha
HRACI Anxiety 2.881 1.236 0.776
HRACI Motivation 2.708 1.07 0.836
HRACI Communication 2.18 1.042 0.875
HRACI Mutual Trust 2.581 1.059 0.720
WLEIS Appraisal and expression of emotion in oneself 3.919 0.864 0.818
WLEIS Appraisal and recognition of emotion hi other 3.773 0.894 0.848
WLEIS Use of emotion to facilitate performance 3.995 0.871 0.749
WLEIS Regulation of emotion in oneself 3.657 0.973 0.857
n=194
S.D. standard deviation
Table 2 Goodness-of-fit-statistics
Scale Models χ2
(df)
χ2/df RMSEA CFI TLI
HRACI Fist-order 59.982
(48)
1.250 0.036 0.986 0.981
HRACI Second-order 63.944
(50)
1.279 0.038 0.984 0.979
WLEIS First-order 166.021
(98)
1.694 0.060 0.949 0.938
I’LEIS Second-order 169.397
(100)
1.694 0.060 0.948 0.938
424 Inf Syst Front (2021) 23:415–433
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they are able to focus their efforts and maintain their
motivation levels. In a similar vein, Christie et al. (2007)
found that individuals with higher ability to regulate emotions
are more likely to report being motivated by achievement
needs. Recently, there has also been growing interest in exam-
ining the relation of EI and motivation in the context of
athletes. For example, Rubaltelli et al. (2018) conducted a
study to investigate the impact of EI on half marathon finish
times. Their results suggested that individuals who are effec-
tive at controlling emotions can reduce the impact of fatigue,
which then leads to better performance. They argued that this
is in particular important when participating in foot races, as it
takes great mental strength to keep going despite feeling close
to exhaustion. In another recent study related to athletes,
Sukys et al. (2019) examined adult basketball players and
reported that the ability to manage emotions is negatively
related to athletes’motivation to perform.
In regard to agile teams, Javdani Gandomani and Ziaei
Nafchi (2016) thus suggested that agile team members, who
experience motivation challenges, need more time to change
themselves and to find their ways to adopt agile practices.
6.3 All EI Dimensions are Related to Communication
Competence
The results have revealed that all four dimensions of EI, self-
emotional appraisal, others’emotional appraisal, use of emo-
tions and regulation of emotions are significantly related to
communication challenges occurring in agile teams. The re-
sults of this research are in line with what has been previously
reported. For example, a significant relationship between EI
and social communication competence has been found when
examining American IS-professionals (Hendon et al. 2017),
Malaysian students with high EI have been reported to better
command in communication skills (Ahmad Marzuki et al.
2015) or the ability to manage others’emotions is significant-
ly correlated with communication performance (Troth et al.
2012b).
The security and ease of communication is fundamental in
ADM projects, in order to keep individual team members in
sync with the iterative cycle as well as with other team mem-
bers (Thorgren and Caiman 2019). Hence, organizations
should not simply hire IS-professionals based upon their
Fig. 4 HRACI Standardized
parameter estimates
Table 3 HRACI Standardized factor loadings
Dimensions al a2 a3 ml m2 m3 cl c2 c3 tl t2 t3 ANX MOT COM TRU
ANX 0.554 0.506 0.509
MOT 0.629 0.716 0.559
COM 0.717 0.659 0.717
TRU 0.481 0.624 0.342
challenges 0.367 0.135 0.639 0.319
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technical strength, but pay attention to their EI and communi-
cation skills (Hendon et al. 2017).
6.4 EI Fosters Mutual Trust
The results provide preliminary evidence that mutual trust
challenges in agile teams are negative related to the ability to
appraise others’emotionsand the ability to regulate one’sown
emotions. The results confirm prior finding, such as that EI
promotes team trust and trust in turn fosters a collaborative
culture which then enhances the creativity of the team
(Barczak et al. 2010), that the EI is positively related to trust
(Rezvani et al. 2016) or that EI mitigates stress and therefore
fosters trust among software developers (Rezvani and
Khosravi 2019). Hence, EI plays a key role in social situa-
tions, instilling feelings of trust and cooperation, in particular
in highly stressful work conditions, such as complex projects
(Rezvani et al. 2016).
7 Contribution
7.1 Contribution to Theory
This study has made four notable contributions to theory.
First, this study has provided preliminary evidence that EI
plays an important role in agile teams and thus extends the
research on critical success factors in ADM-projects. So far,
the current literature has only highlighted the importance of
quality, scope, time and costs (Chow and Cao 2008)ororga-
nizational, team and customer factors (Ahimbisibwe et al.
2015). Second, this research also contributes to research
Fig. 5 WLEIS Standardized
parameter estimates
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efforts on the vital role of EI in the workplace. Consistent with
past studies, it confirms that EI measured as self-report ability
by WLEIS is a significant predictor for job performance be-
yond the effect of general mental ability (Wong and Law
2002;Trivellasetal.2013;Chenetal.2015; Law et al.
2008). Third, the benefits of applying PSM in psychological
and organizational research have been demonstrated.
Although, PSM has gained popularity in fields such as eco-
nomics, epidemiology, medicine and political science (Stuart
2010), due to a lack of understanding of the underlying prin-
ciples of PSM techniques, is has yet not been widely applied
in psychological research (Harder et al. 2010). Fourth, an in-
creasing body of literature has reported human related chal-
lenges perceived by agile team members (Conboy et al. 2011;
Lalsing et al. 2012; Javdani Gandomani and Ziaei Nafchi
2016). Yet, there has been a lack of a practical measure for
these challenges. This paper has developed the HRACI, which
has demonstrated good internal validity for all its dimensions.
7.2 Contribution to Practice
Two key managerial implications follow from these discus-
sions. First, the findings clearly advocate the need to consider
and assess the EI of IS-professionals when staffing ADM-
projects. This study therefore provides support for researchers
who have argued that prior research has focusing on technical
skills of software developers on project outcome, yet
underestimated social and emotional skills (Rezvani and
Khosravi 2019) or who advocated that employers should se-
lect employees not only based on their technical skills, but
also if they can express their expertise with the use of positive
EI and communication effectiveness (Hendon et al. 2017).
Second, training of agile team members should go beyond
improving only their technical skills but also include special
EI awareness and development training. Preliminary evidence
exists, that EI indeed can be trained (e.g. Nelis et al. (2011),
Lopes (2016) or Mattingly and Kraiger (2019)). Compared to
more long-term or costly talent management approaches, EI
training programs can provide a more immediate benefit to
organizations, such as improved performance and affective
outcomes (Mattingly and Kraiger 2019).
8 Limitations and Future Research
This research has some limitations that needs to be taken into
account. First, both HRACI, as well as WLEIS are self-report
measures and therefore are prone to self-enhancement and
socially desirable responses (Lopes 2016). Hence, scholars
have raised concerns, if EI assessed by self- report measures,
actually measures an actual ability rather than a trait (Mayer
et al. 2008; Brannick et al. 2009; Joseph and Newman 2010).
Contrariwise, self-report EI measures are more efficient to
Table 4 WLEIS Standardized factor loadings
Dimensions s1 s2 s3 s4 ol o2 o3 o4 ul u2 u3 u4 rl r2 r3 r4 SEA OEA UOE ROE
SEA 0.480 0.724 0.752 0.283
OEA 0.488 0.783 0.420 0.710
UOE 0.549 0.322 0.525 0.385
ROE 0.457 0.639 0.540 0.793
EI 0.605 0.275 0.327 0.338
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assess EI in cross-cultural settings, because they tap into typ-
ical attributes of the individual’s thoughts, feelings, and be-
haviors in certain situations (Li et al. 2012). Subjective assess-
ments may even provide a more comprehensive view of
(perceived) emotional abilities, because test-takers are more
likely to draw upon their full range of emotional experience
across different context in life (Lopes 2016).
Second, the continuous treatment variable EI has been di-
chotomized. Although, methods such as the Generalized
Propensity Score (Hirano and Guido 2004) exists, diagnostics
are complicated for these methods, as it becomes more
complex to assess the balance of the covariates (Stuart
2010). Consequently, the application of PSM to continuous
treatment is rare (Fong et al. 2018) and researchers often di-
chotomize the continuous treatment variable in order to apply
PSM (e.g. (Nielsen et al. 2011;DeandRatha2012)).
Third, the authors acknowledge that the sample only in-
cludes IS-professionals and thus limits the generalizability of
the research findings. Yet, this creates research opportunities
for future researchers to examine if the preliminary findings of
this study can be extended into other domains. This is in par-
ticular important, as although originally designed for software
Table 5 Correlation matrix
Dimension Anxiety Motivation Communication Mutual Trust
Appraisal and expression of emotion in oneself −0.155
*
−0.074 −0.268
***
−0.104
Appraisal and recognition of emotion in others −0.023 −0.034 −0.154
*
−0.195
**
Use of emotion to facilitate performance −0.177
*
−0.195
**
−0.248
***
−0.095
Regulation of emotion in oneself −0.116 −0.202
**
−0.274
***
−0.226
**
***
p≤0.001;
**
p≤0.01;
*
p≤0.05; n= 194 (two-tailed test)
Fig. 6 Revised conceptual model “Impact of Emotional Intelligence in Agile teams”(source: author)
428 Inf Syst Front (2021) 23:415–433
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development, due to its success ADM have now also spread to
non-IS projects (Serrador and Pinto 2015;Hodaetal.2018),
such as product development (Ramesh et al. 2019) and also
moved into mainstream thinking as management practice
(Birkinshaw 2019). Furthermore, human-related challenges
related to anxiety, motivation, mutual trust or communication
competence are not limited to collaboration in agile teams, yet
occur in everybody’s daily life whenever people socially
interact.
9 Conclusions
With the increasing popularity of ADM in modern soft-
ware development, agile practitioners realized that its
adoption within an organization is challenging . Yet, prior
work has only focused on reporting various human related
challenges, without providing insights about their origins.
The findings of this study provide preliminary evidence
that these challenges are negatively related to specific di-
mensions of EI.
Finally, the importance of EI in ADM might even
become more important with the emergence of
Aritifical Intelligence (AI). Recent research suggests that
AI might assist human programmers in coding, e.g. AI
could act as pair programming partner or humans could
focus on writing test cases and AI would create the
corresponding code. However, AI is less suitable for
unstructured tasks, such as interacting with others or
the potentially emotionally fraught tasks of communicat-
ing. Thus, with the increasing use of AI, the human role
in ADM might shift from coding into primarily focusing
on unstructured tasks, such as organizing and collabora-
tion, which then might result in more human related
challenges.
Open Access This article is distributed under the terms of the Creative
Commons Attribution 4.0 International License (http://
creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided you give appro-
priate credit to the original author(s) and the source, provide a link to the
Creative Commons license, and indicate if changes were made.
References
Abbott, M. L., & McKinney, J. (2013). Understanding and applying
research design (1st;1. Aufl.;1; ed., Vol. book, whole). Somerset:
John Wiley & Sons, Incorporated.
Abdollahi, A., & Abu Talib, M. (2015). Emotional intelligence moderates
perfectionism and test anxiety among Iranian students. School
Psychology International, 36(5), 498–512. https://doi.org/10.1177/
0143034315603445.
Ahimbisibwe, A., Cavana, R. Y., & Daellenbach, U. (2015). A contin-
gency fit model of critical success factors for software development
projects. Journal of Enterprise Information Management, 28(1), 7.
Ahmad Marzuki, N., Mustaffa, C. S., & Mat Saad, Z. (2015). Emotional
intelligence: Its relations to communication and information tech-
nology skills. Asian Social Science, 11(15). https://doi.org/10.5539/
ass.v11n15p267.
Ahmadpanah M., Keshavarz M, Haghighi M, Jahangard L, Bajoghli H,
Sadeghi Bahmani D, et al. (2016). Higher emotional intelligence is
related to lower test anxiety among students. Neuropsychiatric
Disease and Treatment, 2016(Issue 1), 133-136.
Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2010). On making
causal claims: A review and recommendations. The Leadership
Quarterly, 21(6), 1086–1120. https://doi.org/10.1016/j.leaqua.
2010.10.010.
Arthur, W., & Villado, A. J. (2008). The importance of distinguishing be-
tween constructs and methods when comparing predictors in personnel
selection research and practice. Journal of Applied Psychology, 93(2),
435–442. https://doi.org/10.1037/0021-9010.93.2.435.
Austin, P. C. (2011). An introduction to propensity score methods for
reducing the effects of confounding in observational studies.
Multivariate Behavioral Research, 46(3), 399–424. https://doi.org/
10.1080/00273171.2011.568786.
Barczak, G., Lassk, F., & Mulki, J. (2010). Antecedents of team creativ-
ity: An examination of team emotional intelligence, team trust and
collaborative culture. Creativity and Innovation Management,
19(4), 332. https://doi.org/10.1111/j.1467-8691.2010.00574.x.
Bar-On, R. (1997). BarOn emotioal quotient inventory: Technical
manual. Toronto.
Beecham, S., Baddoo, N., Hall, T., Robinson, H., & Sharp, H. (2008).
Motivation in software engineering: A systematic literature review.
Information and Software Technology, 50(9), 860–878. https://doi.
org/10.1016/j.infsof.2007.09.004.
Begel, A., & Nagappan, N. (2007). Usage and perceptions of agile soft-
ware development in an industrial context: An exploratory study. In,
2007 (pp. 255–264): IEEE. https://doi.org/10.1109/ESEM.2007.12.
Birkinshaw, J. (2019). What to expect from agile. MIT Sloan
Management Review, 60(3), 1–37.
Boehm, B., & Turner, R. (2005). Management challenges to
implementing agile processes in traditional development organiza-
tions. IEEE Software, 22(5), 30–39. https://doi.org/10.1109/MS.
2005.129.
Box-Steffensmeier, J. (2007). The Neyman-Rubin model of causal infer-
ence and estimation via matching methods.
Table 6 Average treatment effect
Dimension Anxiety Motivation Communication Mutual Trust
Appraisal and expression of emotion in oneself −0.29 −0.43
Appraisal and recognition of emotion in others −0.17 −0.31
Use of emotion to facilitate performance −0.62 −0.45 −0.34
Regulation of emotion in oneself −0.29 −0.32 −0.25
429Inf Syst Front (2021) 23:415–433
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Brannick, M. T., Wahi, M. M., Arce, M., Johnson, H.-A., Nazian, S., &
Goldin, S. B. (2009). Comparison of trait and ability measures of
emotional intelligence in medical students. Medical Education,
43(11), 1062–1068. https://doi.org/10.1111/j.1365-2923.2009.
03430.x.
Brynjolfsson, E., & Mitchell, T. (2017). What can machine learning do?
Workforce implications. Science (New York, N.Y.), 358(6370),
1530–1534. https://doi.org/10.1126/science.aap8062.
Cabello, R., Sorrel, M. A., Fernández-Pinto, I., Extremera, N., &
Fernández-Berrocal,P. (2016). Age and gender differences in ability
emotional intelligence in adults: A cross-sectional study.
Developmental Psychology, 52(9), 1486–1492. https://doi.org/10.
1037/dev0000191.
Capretz, L. F. (2003). Personality types in software engineering.
International Journal of Human - Computer Studies, 58(2), 207–
214. https://doi.org/10.1016/S1071-5819(02)00137-4.
Carvalho, V. S., Guerrero, E., Chambel, M. J., & González-Rico, P.
(2016). Psychometric properties of WLEIS as a measure of emo-
tional intelligence in the Portuguese and Spanish medical students.
Evaluation and Program Planning, 58,152–159. https://doi.org/10.
1016/j.evalprogplan.2016.06.006.
Castro-Sánchez, M., Zurita-Ortega, F., Ubago-Jiménez, J. L., González-
Valero, G., & Chacón-Cuberos, R. (2019). Relationships between
anxiety, emotional intelligence, and motivational climate among ad-
olescent football players. Sports (Basel, Switzerland), 7(2), 34.
https://doi.org/10.3390/sports7020034.
Chen, A. S.-y., Bian, M.-d., & Hou, Y.-H. (2015). Impact of transforma-
tional leadership on subordinate's EI and work performance.
Personnel Review, 44(4), 438.
Cho, S., Drasgow, F., & Cao, M. (2015). An investigation of emotional
intelligence measures using item response theory. Psychological
Assessment, 27(4), 1241.
Chow, T., & Cao, D.-B. (2008). A survey study of critical success factors
in agile software projects. Journal of Systems and Software, 81(6),
961–971. https://doi.org/10.1016/j.jss.2007.08.020.
Christie, A., Jordan, P., Troth, A. C., & Lawrence, S. (2007). Testing the
links between emotional intelligence and motivation. Journal of
Management and Organization, 13(3), 212–226. https://doi.org/
10.1017/S1833367200003709.
Ciarrochi, J., & Mayer, J. D. (2013). Applying emotional intelligence : A
practitioner's guide. London: Taylor and Francis.
Ciarrochi, J. V., Chan, A. Y. C., & Caputi, P. (2000). A critical evaluation
of the emotional intelligence construct. Personality and Individual
Differences, 28(3), 539–561. https://doi.org/10.1016/S0191-
8869(99)00119-1.
Cobb,C.G.(2011).Making sense of agile project management :
Balancing control and agility. Hoboken: Wiley.
Cockburn, A., & Highsmith, J. (2001). Agile software development, the
people factor. Computer, 34(11), 131–133. https://doi.org/10.1109/
2.963450.
Conboy, K., Coyle, S., Wang, X., & Pikkarainen, M. (2011). People over
process: Key challenges in agile development. IEEE Software,
28(4), 48–57. https://doi.org/10.1109/MS.2010.132.
Cooper, R. G. (2014). What's next? After stage-gate: Progressive compa-
nies are developing a new generation of idea-to-launch processes.
Research-Technology Management, 57(1), 20. https://doi.org/10.
5437/08956308X5606963.
Cooper, R. G., & Sommer, A. F. (2016). The agile–stage-gate hybrid
model: A promising new approach and a new research opportunity.
Journal of Product Innovation Management, 33(5), 513–526.
https://doi.org/10.1111/jpim.12314.
Côté, S. (2017). Enhancing managerial effectiveness via four core facets
of emotional intelligence. Organizational Dynamics, 46(3), 140–
147. https://doi.org/10.1016/j.orgdyn.2017.05.007.
Cram, W. A. (2019). Agile development in practice: Lessons from the
trenches. Information Systems Management, 36(1), 2–14. https://
doi.org/10.1080/10580530.2018.1553645.
De, P. K., & Ratha, D. (2012). Impact of remittances on household in-
come, asset and human capital: Evidence from Sri Lanka. Migration
and Development, 1(1), 163–179. https://doi.org/10.1080/
21632324.2012.719348.
Dorairaj, S., Noble, J., & Malik, P. (2012). Understanding lack of trust in
distributed agile teams: A grounded theory study. In Stevenage,
2012 2012 (1 ed., Vol. 2012, pp. 81-90): The institution of
Engineering & Technology. https://doi.org/10.1049/ic.2012.0011.
Dybå, T., & Dingsøyr, T. (2008). Empirical studies of agile software
development. A systematic review. Information and Software
Technology, 50(9–10), 833–859. https://doi.org/10.1016/j.infsof.
2008.01.006.
Field, A. (2013). Discovering statistics using IBM SPSS Statistics (4th
edition ed.): Sage.
Fong, C. H., Chad, & Kosuke, I. (2018). Covariate BALANCING pro-
pensity score for a continuous treatment: Application to the efficacy
of political advertisements. The Annals of Applied Statistics, 12(1),
156–177.
Forrest, C. L. (2012). An illustrative example of propensity score
matching with education research. Career and Technical
Education Research.
Fortmann, L. (2018). Agile or fragile? - the depleting effects of agile
methodologies for software developers. In ECIS.
George, J. M. (2000). Emotions and leadership: The role of emotional
intelligence. Human Relations, 53(8), 1027–1055. https://doi.org/
10.1177/0018726700538001.
Ghobadi, S., & Mathiassen, L. (2016). Perceived barriers to effective
knowledge sharing in agile software teams. Information Systems
Journal, 26(2), 95–125. https://doi.org/10.1111/isj.12053.
Goh, J. C.-L., Pan, S. L., & Zuo, M. (2013). Developing the agile IS
development practices in large-scale IT projects: The trust-
mediated organizational controls and IT project team capabilities
perspectives. Journal of the Association for Information Systems,
14(12), 722.
Goodpasture, J. C. (2010). Project management the agile way: making it
work in the enterprise (Vol. book, whole). Ft. Lauderdale, Fla: J.
Ross Pub.
Gregory, P., Barroca, L., Sharp, H., Deshpande, A., & Taylor, K. (2016).
The challenges that challenge: Engaging with agile practitioners'
concerns. Information and Software Technology, 77,92–104.
https://doi.org/10.1016/j.infsof.2016.04.006.
Harder, V. S., Stuart, E. A., & Anthony, J. C. (2010). Propensity score
techniques and the assessment of measured covariate balance to test
causal associations in psychological research. Psychological
Methods, 15(3), 234–249. https://doi.org/10.1037/a0019623.
Helal, S. (2018). The expanding frontier of artificial intelligence.
Computer, 51(9), 14–17. https://doi.org/10.1109/MC.2018.
3620976.
Hendon, M., Powell, L., & Wimmer, H. (2017). Emotional intelligence
and communication levels in information technology professionals.
Computers in Human Behavior, 71, 165–171. https://doi.org/10.
1016/j.chb.2017.01.048.
Henttonen, K., & Blomqvist, K. (2005). Managing distance in a global
virtual team: The evolution of trust through technology-mediated
relational communication. Strategic Change, 14(2), 107–119.
https://doi.org/10.1002/jsc.714.
Herzog, S. (2014). The propensity score analytical framework: An over-
view and institutional research example. New Directions for
Institutional Research, 2014(161), 21–40. https://doi.org/10.1002/
ir.20065.
Hirano, K., Imbens, G. (2004). The propensity score with continuous
treatments.
430 Inf Syst Front (2021) 23:415–433
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Hoda, R., & Murugesan, L. K. (2016). Multi-level agile project manage-
ment challenges: A self-organizing team perspective. Journal of
Systems and Software, 117,245–257. https://doi.org/10.1016/j.jss.
2016.02.049.
Hoda, R., Salleh, N., & Grundy, J. (2018). The rise and evolution of agile
software development. IEEE Software, 35(5), 58–63. https://doi.org/
10.1109/MS.2018.290111318.
Hummel, M., Rosenkranz, C., & Holten, R. (2013). The role of commu-
nication in agile systems development: An analysis of the state of the
art. Business & Information Systems Engineering, 5(5), 343–355.
https://doi.org/10.1007/s12599-013-0282-4.
Iliceto, P., & Fino, E. (2017). The Italian version of the Wong-Law emo-
tional intelligence scale (WLEIS-I): A second-order factor analysis.
Personality and Individual Differences, 116,274–280. https://doi.
org/10.1016/j.paid.2017.05.006.
Imai, K., & David, A. V. D. (2004). Causal inference with general treat-
ment regimes: Generalizing the propensity score. Journal of the
American Statistical Association, 99(467), 854–866. https://doi.
org/10.1198/016214504000001187.
Javdani Gandomani, T., & Ziaei Nafchi, M. (2016). Agile transition and
adoption human-related challenges and issues: A grounded theory
approach. Computers in Human Behavior, 62,257–266. https://doi.
org/10.1016/j.chb.2016.04.009.
Joseph, D. L., & Newman, D. A. (2010). Emotional intelligence: An
integrative meta-analysis and cascading model. Journal of Applied
Psychology, 95(1), 54–78. https://doi.org/10.1037/a0017286.
Kakar, A. K. (2017). Assessing self-organization in agile software devel-
opment teams. The Journal of Computer Information Systems,
57(3), 208–210. https://doi.org/10.1080/07362994.2016.1184002.
Kalenda, M., Hyna, P., & Rossi, B. (2018). Scaling agile in large organi-
zations: Practices, challenges, and success factors. Journal of
Software: Evolution and Process, 30(10), e1954–n/a. https://doi.
org/10.1002/smr.1954.
Kosti M V, Feldt R, Angelis L, Blekinge Tekniska H. (2014). Personality,
emotional intelligence and work preferences in software engineer-
ing: An empirical study. Information and Software Technology,
56(8), 973–990. https://doi.org/10.1016/j.infsof.2014.03.004.
Kovitz, B. (2003). Hidden skills that support phased and agile require-
ments engineering. Requirements Engineering, 8(2), 135–141.
https://doi.org/10.1007/s00766-002-0162-9.
Krakovsky, M. (2018). Artificial (Emotional). Intelligence., 61(4), 18.
Lalsing, V., Kishnah, S., & Pudaruth, S. (2012). People factors in agile
software development and project management. International
Journal of Software Engineering & Applications, 3(1), 117–137.
Law, K. S., Wong, C.-S., & Song, L. J. (2004). The construct and criterion
validity of emotional intelligence and its potential utility for man-
agement studies. Journal of Applied Psychology, 89(3), 483–496.
https://doi.org/10.1037/0021-9010.89.3.483.
Law, K. S., Wong, C.-S., Huang, G.-H., & Li, X. (2008). The effects of
emotional intelligence on job performance and life satisfaction for
the research and development scientists in China. Asia Pacific
Journal of Management, 25(1), 51–69. https://doi.org/10.1007/
s10490-007-9062-3.
Lee, H., & Kwak, Y. (2012). Development and initial validation of a trait
emotional intelligence scale for Korean adults. Asia Pacific
Education Review, 13(2), 209–217. https://doi.org/10.1007/
s12564-011-9175-8.
Lenberg, P., Feldt, R., Wallgren, L. G., Institutionen för, p., Fakulteten för,
d, & Blekinge Tekniska, H. (2015). Behavioral software engineer-
ing: A definition and systematic literature review. The Journal of
Systems & Software, 107,15–37. https://doi.org/10.1016/j.jss.2015.
04.084.
Li, T., Saklofske, D. H., Bowden, S. C., Yan, G., & Fung, T. S. (2012).
The measurement invariance of the Wong and Law emotional intel-
ligence scale (WLEIS) across three Chinese University student
groups from Canada and China. Journal of Psychoeducational
Assessment, 30(4), 439–452. https://doi.org/10.1177/
0734282912449449.
Libbrecht, N., Beuckelaer, A. d., Lievens, F., & Rockstuhl, T. (2014).
Measurement invariance of the Wong and Law emotional intelli-
gence scale scores: Does the measurement structure hold across far
eastern and European countries? Applied Psychology, 63(2), 223–
237. https://doi.org/10.1111/j.1464-0597.2012.00513.x.
Lindvall, M., Basili, V., Boehm, B., Costa, P., Dangle, K., Shull, F.,
Tesoriero, R.,, & Williams, L., Zelkowitz (2002). Empirical findings
in agile methods. Proceedings of Extreme Programming and Agile
Methods –XP/Agile Universe.
Liu, M., & Ren, S. (2018). Moderating effect of emotional intelligence on
the relationship between rumination and anxiety. Current
Psychology, 37(1), 272–279. https://doi.org/10.1007/s12144-016-
9510-7.
Lopes, P. N. (2016). Emotional intelligence in organizations: Bridging
research and practice. Emotion Review, 8(4), 316–321. https://doi.
org/10.1177/1754073916650496.
Macht,G.A.,Nembhard,D.A.,&Leicht,R.M.(2019).
Operationalizing emotional intelligence for team performance.
International Journal of Industrial Ergonomics, 71,57–63. https://
doi.org/10.1016/j.ergon.2019.02.007.
Mandal, A., & Pal, S. C. (2015). Achieving agility through BRIDGE
process model: An approach to integrate the agile and disciplined
software development. [journal article]. Innovations in Systems and
Software Engineering, 11(1), 1–7. https://doi.org/10.1007/s11334-
014-0239-x.
Margavio, T. M., Margavio, G. W., Hignite, M. A., & Moses, D. R.
(2012). A comparative analysis of the emotional intelligence levels
of American and Chinese business students. College Student
Journal, 46(4), 776.
Martínez-Monteagudo, M. C., Inglés, C. J., Granados, L., Aparisi, D., &
García-Fernández, J. M. (2019). Trait emotional intelligence pro-
files, burnout, anxiety, depression, and stress in secondary education
teachers. Personality and Individual Differences, 142, 53–61.
https://doi.org/10.1016/j.paid.2019.01.036.
Mattingly, V., & Kraiger, K. (2019). Can emotional intelligence be
trained? A meta-analytical investigation. Human Resource
Management Review, 29(2), 140–155. https://doi.org/10.1016/j.
hrmr.2018.03.002.
Mayer, J. D., & Salovey, P. (1993). The intelligence of emotional intelli-
gence. Intelligence, 17(4), 433–442. https://doi.org/10.1016/0160-
2896(93)90010-3.
Mayer, J., & Salovey, P. (1997). What is emotional intelligence?
Emotional development and emotional intelligence: Educational
implications,3–34.
Mayer, J., Caruso, D. R., & Salovey, P. (1999). Emotional intelligence
meets traditional standards for an intelligence. Intelligence, 27(4),
267–298. https://doi.org/10.1016/S0160-2896(99)00016-1.
Mayer, J. D., Salovey, P., & Caruso, D. R. (2008). Emotional intelligence:
New ability or eclectic traits? American Psychologist, 63(6), 503–
517. https://doi.org/10.1037/0003-066X.63.6.503.
Mayer, J. D., Caruso, D. R., & Salovey, P. (2016). The ability model of
emotional intelligence: Principles and updates. Emotion Review,
8(4), 290–300. https://doi.org/10.1177/1754073916639667.
McCroskey, M. C. (1988). Self-report as an approach to measuring com-
munication competence. Communication Research Reports, 5,108–
113.
Melnik, G., & Maurer, F. (2004). Direct verbal communication as a cat-
alyst of agile knowledge sharing. In,2004(pp. 21–31): IEEE.
https://doi.org/10.1109/ADEVC.2004.12.
Mithas, S., Kude, T., & Whitaker, J. (2018). Artificial intelligence and IT
professionals. IT Professional, 20(5), 6–13. https://doi.org/10.1109/
MITP.2018.053891331.
Moe, N. B., Aurum, A., & Dybå, T. (2012). Challenges of shared deci-
sion-making: A multiple case study of agile software development.
431Inf Syst Front (2021) 23:415–433
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Information and Software Technology, 54(8), 853–865. https://doi.
org/10.1016/j.infsof.2011.11.006.
Nelis, D., Kotsou, I., Quoidbach, J., Hansenne, M., Weytens, F., Dupuis,
P., et al. (2011). Increasing emotional competence improves psycho-
logical and physical well-being, social relationships, and employ-
ability. Emotion, 11(2), 354–366. https://doi.org/10.1037/a0021554.
Ng, K.-M., Wang, C., Zalaquett, C. P., & Bodenhorn, N. (2007). A con-
firmatory factor analysis of the Wong and Law emotional intelli-
gence scale in a sample of international college students.
International Journal for the Advancement of Counselling, 29(3),
173–185. https://doi.org/10.1007/s10447-007-9037-6.
Nielsen, R. A., Findley, M. G., Davis, Z. S., Candland, T., & Nielson, D.
L. (2011). Foreign aid shocks as a cause of violent armed conflict.
American Journal of Political Science, 55(2), 219–232. https://doi.
org/10.1111/j.1540-5907.2010.00492.x.
Pankratz, O., & Basten, D. (2017). Opening the black Box: Managers’
Perceptions of IS Project Success Mechanisms. Information &
Management,doi:https://doi.org/10.1016/j.im.2017.09.005.
Pedersen, M. (2013). A quantitative examination of critical success fac-
tors comparing agile and waterfall project management
methodologies. ProQuest Dissertations Publishing,
Petrides, K. V., & Furnham, A. (2001). Trait emotional intelligence:
Psychometric investigation with reference to established trait taxon-
omies. European Journal of Personality, 15(6), 425–448. https://
doi.org/10.1002/per.416.
Petrovici, A., & Dobrescu, T. (2014). The roleof emotional intelligence in
building interpersonal communication skills. Procedia - Social and
Behavioral Sciences, 116,1405–1410. https://doi.org/10.1016/j.
sbspro.2014.01.406.
Pikkarainen, M., Haikara, J., Salo, O., Abrahamsson, P., & Still, J. (2008).
The impact of agile practices on communication in software devel-
opment. Empirical Software Engineering, 13(3), 303–337. https://
doi.org/10.1007/s10664-008-9065-9.
Pikkarainen, M., Salo, O., Kuusela, R., & Abrahamsson, P. (2012).
Strengths and barriers behind the successful agile deployment—
Insights from the three software intensive companies in Finland.
Empirical Software Engineering, 17(6), 675–702. https://doi.org/
10.1007/s10664-011-9185-5.
Radujković, M., Vukomanović, M., Wagner, R., & Špundak, M. (2014).
Selected papers from the 27th IPMA (International Project
Management Association), World Congress, Dubrovnik, Croatia,
2013mixed agile/traditional Project Management methodology –
Reality or illusion? Procedia - Social and Behavioral Sciences,
119 ,939
–948. https://doi.org/10.1016/j.sbspro.2014.03.105.
Ramesh, B., Cao, L., Kim, J., Mohan, K., & James, T. L. (2019). Consider
culture when implementing agile practices. MIT Sloan Management
Review, 60(3), 1–37.
Rezvani, A., & Khosravi, P. (2019). Emotional intelligence: The key to
mitigating stress and fostering trust among software developers
working on information system projects. International Journal of
Information Management, 48,139–150. https://doi.org/10.1016/j.
ijinfomgt.2019.02.007.
Rezvani, A., Chang, A., Wiewiora, A., Ashkanasy, N. M., Jordan, P. J., &
Zolin, R. (2016). Manager emotional intelligence and project suc-
cess: The mediating role of job satisfaction and trust. International
Journal of Project Management, 34(7), 1112–1122. https://doi.org/
10.1016/j.ijproman.2016.05.012.
Roberts, R. D., Zeidner, M., & Matthews, G. (2001). Does emotional
intelligence meet traditional standards for an intelligence? Some
new data and conclusions. Emotion, 1(3), 196–231. https://doi.org/
10.1037/1528-3542.1.3.196.
Robson, S. (2013). Agile SAP: introducing flexibility, transparency and
speed to SAP implementations (Vol. book, whole): IT Governance
Pub.
Rosete, D., & Ciarrochi, J. (2005). Emotional intelligence and its rela-
tionship to workplace performance outcomes of leadership
effectiveness. Leadership & Organization Development Journal,
26(5), 388–399. https://doi.org/10.1108/01437730510607871.
Rossi, F. (2018). Building trust in ARTIFICIAL intelligence. Journal of
International Affairs, 72(1), 127.
Rousseau, D. M., Sitkin, S. B., Burt, R. S., & Camerer, C. (1998). Not so
different after all: A cross-discipline view of trust. Academy of
Management Review, 23(3), 393–404. https://doi.org/10.5465/amr.
1998.926617.
Rubaltelli, E., Agnoli,S., & Leo, I. (2018). Emotional intelligence impact
on half marathon finish times. Personality and Individual
Differences, 128,107–112. https://doi.org/10.1016/j.paid.2018.02.
034.
Rubin, D. B. (1974). Estimating causal effects of treatments in random-
ized and nonrandomized studies. Journal of Educational
Psychology, 66,668–701.
Russo, F., Wunsch, G., & Mouchart, M. (2011). Inferring causality
through counterfactuals in observational studies - some epistemo-
logical issues. Bulletin de Méthodologie Sociologique,111(1), 43–
64, doi:https://doi.org/10.1177/0759106311408891.
Schlegel, K., & Mortillaro, M. (2019). The Geneva emotional compe-
tence test (GECo): An ability measure of workplace emotional in-
telligence. The Journal of Applied Psychology, 104(4), 559–580.
https://doi.org/10.1037/apl0000365.
Schmidt, C. T., Venkatesha, S. G., & Heymann, J. (2014). Empirical
insights into the perceived benefits of agile software engineering
practices: A case study from SAP. Paper presented at the companion
proceedings of the 36th international conference on software engi-
neering, Hyderabad, India,
Schuller, D., & Schuller, B. W. (2018). The age of Artificial emotional
intelligence. Computer, 51(9), 38–46. https://doi.org/10.1109/MC.
2018.3620963.
Schutte, N. S., Malouff, J. M., & Thorsteinsson, E. B. (2013). Increasing
emotional intelligence through training: Current status and future
directions. International Journal of Emotional Education, 5(1),
56–72.
Serrador, P., & Pinto, J. K. (2015). Does agile work? —A quantitative
analysis of agile project success. International Journal of Project
Management, 33(5), 1040–1051. https://doi.org/10.1016/j.
ijproman.2015.01.006.
Shadish, W. R. (2010). Campbell and Rubin:A primer and comparison of
their approaches to causal inference in field settings. Psychological
Methods, 15(1), 3–17. https://doi.org/10.1037/a0015916.
Sharp, H., Baddoo, N., Beecham, S., Hall, T., & Robinson, H. (2009).
Models of motivation in software engineering. Information and
Software Technology, 51(1), 219–233. https://doi.org/10.1016/j.
infsof.2008.05.009.
Shih, S.-P., Lie, T., Klein, G., & Jiang, J. J. (2014). Information technol-
ogy customer aggression: The importance of an organizational cli-
mate of support. Information & Management, 51(6), 670–678.
https://doi.org/10.1016/j.im.2014.06.001.
Stuart, E. A. (2010). Matching methods for causal inference: A review
and a look forward. Statistical Science, 25(1), 1–21. https://doi.org/
10.1214/09-STS313.
Sukys, S., Tilindienė, I., Cesnaitiene, V. J., & Kreivyte, R. (2019). Does
emotional intelligence predict Athletes' motivation to participate in
sports? Perceptual and Motor Skills, 31512518825201.
Sun, J. (2005). Assessing goodness of fit in confirmatory factor analysis.
Measurement and Evaluation in Counseling and Development,
37(4), 240–256. https://doi.org/10.1080/07481756.2005.11909764.
Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics
(5th ed., Vol. Book, Whole). Boston: Pearson Education.
Takpuie, D., & Tanner, M. (2016). Investigating the characteristics need-
ed by scrum team members to successfully transfer tacit knowledge
during agile software projects. Electronic Journal of Information
Systems Evaluation U6, 19(1), 36.
432 Inf Syst Front (2021) 23:415–433
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Tan, C.-H., & Teo, H.-H. (2007). Training future software developers to
acquire agile development skills. (Vol. 50, pp. 97–98).NewYork:
ACM.
Thomas, C. L., Cassady, J. C., & Heller, M. L. (2017). The influence of
emotional intelligence, cognitive test anxiety, and coping strategies
on undergraduate academic performance. Learning and Individual
Differences, 55,40–48. https://doi.org/10.1016/j.lindif.2017.03.001.
Thorgren, S., & Caiman, E. (2019). The role of psychological safety in
implementing agile methods across cultures. Research-Technology
Management, 62(2), 31–39. https://doi.org/10.1080/08956308.
2019.1563436.
Trivellas, P., Gerogiannis, V., & Svarna, S. (2013). Exploring workplace
implications of emotional intelligence (WLEIS) in hospitals: Job
satisfaction and turnover intentions. Procedia - Social and
Behavioral Sciences, 73,701–709. https://doi.org/10.1016/j.
sbspro.2013.02.108.
Troth, A. C., Jordan, P. J., & Lawrence, S. A. (2012a). Emotional intel-
ligence, communication competence, and student perceptions of
team social cohesion. Journal of Psychoeducational Assessment,
30(4), 414–424. https://doi.org/10.1177/0734282912449447.
Troth, A. C., Jordan, P. J., Lawrence, S. A., & Herman H. M, T. S. E.
(2012b). A multilevel model of emotional skills, communication
performance, and task performance in teams. Journal of
Organizational Behavior,33(5), 700–722, doi:https://doi.org/10.
1002/job.785.
Tsapeli, F., & Musolesi, M. (2015). Investigating causality in human
behavior from smartphone sensor data: A quasi-experimental ap-
proach. 4(1), 1-15. https://doi.org/10.1140/epjds/s13688-015-0061-
1.
Van Rooy, D. L., Alonso, A., & Viswesvaran, C. (2005). Group differ-
ences in emotional intelligence scores: Theoretical and practical im-
plications. Personality and Individual Differences, 38(3), 689–700.
https://doi.org/10.1016/j.paid.2004.05.023.
VersionOne. (2018). 12th annual state of agile report. Alpharetta:
CollabNet.
Vinekar, V., Slinkman, C. W., & Nerur, S. (2006). Can agile and tradi-
tional systems development approaches coexist? An ambidextrous
view. Information Systems Management, 23(3), 31–42. https://doi.
org/10.1201/1078.10580530/46108.23.3.20060601/93705.4.
Weilemann, E., & Brune, P. (2015). Less distress with a scrum mistress?:
On the impact of females in agile software development teams. In,
2015 (Vol. 28-, pp. 3–7): ACM. https://doi.org/10.1145/2811681.
2811682.
Wong, C., & Law, K. S. (2002). The effects of leader and follower emo-
tional intelligence on performance and attitude an exploratory study.
The Leadership Quarterly, 13(3), 243–274. https://doi.org/10.1016/
S1048-9843(02)00099-1.
Woszczynski, A. B., Dembla, P., & Zafar, H. (2016). Gender-based dif-
ferences inculture in the Indian IT workplace. International Journal
of Information Management, 36(4), 507–519. https://doi.org/10.
1016/j.ijinfomgt.2016.02.002.
Publisher’sNote Springer Nature remains neutral with regard to juris-
dictional claims in published maps and institutional affiliations.
Tan Trung Luong is a fourth-year DBA student at the School of
Management, University of Bradford, UK, and works as a Solution
Architect for an international IT-company. He received his diploma in
Business Administration from the University of Mannheim, Germany.
His research interests include agile project management and Emotional
Intelligence.
Uthayasankar Sivarajah is a Reader in Technology Management and
Circular Economy and the Head of Business Analytics, Circular
Economy and Supply Chain (BACES) Research Centre in the School
of Management at University of Bradford, UK. His broad area of research
interest and expertise is interdisciplinary focusing on the use of emerging
digital technology for the betterment of society, be it in a business or
government context. He actively publishes in leading high impact factor
journals such as Journal of Business Research, Computers in Human
Behaviour and Government Information Quarterly. Hisresearch has also
been featured in reputable media/trade publications such as Computer
Weekly, Public Sector Focus, London School of Economics academic
blog etc. To date, he has been involved as Principal and Co-investigator
in over £3 million worth of Research and Innovation and consultancy
projects funded by national, international funding bodies and commercial
organisations. Some of the notable funders have been the European
Commission (FP7, H2020, Marie Curie), Qatar National Research Fund
(QNRF), Innovate UK/DEFRA and British Council focusing on projects
addressing business and societal challenges surrounding themes such as
Blockchain use in Financial Services, Smart Waste and Cities, Energy
efficient data centres, Social innovation and Participatory Budgeting. He
has a PhD in Management Information Systems from Brunel University
London, a MSc with Distinction in Management from Cass Business
School (City University, London) and a first-class BSc (Hons) in
Business and Management specialising in Computer Sciences from
Brunel University London. He is a Fellow of the UK Higher Education
Academy (FHEA), a member of the British Academy of Management
(BAM) and Association for Information Systems (AIS).
Vishanth Weerakkody is currently the Deputy Dean for the Faculty of
Management, Law and Social Sciences and Professor of Digital
Governance at University of Bradford. Prior to this, I was a Professor
of Digital Governance at the Business School in Brunel University
London. My current research is multidisciplinary and centred around
public sector policy implementation, process transformation through dig-
ital government, social innovation and the implementation, diffusion and
adoption of disruptive technologies within a smart city–services context.
Focusing on the evolving role of technology, I closely follow and critique
digital enabled service transformation efforts in government. My passion
for solving societalproblems through research and innovation together
with a vast network of collaborators from both academia and industry
has allowed me to attract over £25 million of R& D funding over the last
few years fromthe EU, ESRC, Qatar Foundation, British Council and UK
Local Government. Given the changing landscape ofUK within Europe, I
am particularly passionate about research and innovation to tackle the
societal challenges prioritised in our National Industry Strategy, including
the use of emerging technologies to transform health and social care and
using community led innovation to deal with increasing levels of scarcity
and poverty.
433Inf Syst Front (2021) 23:415–433
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